From e01a43c3cffa73a9a4a68ddd479224ec50b7cde1 Mon Sep 17 00:00:00 2001 From: denjev03 Date: Tue, 3 Mar 2026 18:56:46 +0100 Subject: [PATCH 01/28] feat: script for BLE reading --- pyproject.toml | 4 +- requirements.txt | 446 +++++++++++++++++++++----------------- uv.lock | 296 +++++++++++++++++++++++++ wearable/read_BLE_data.py | 44 ++++ 4 files changed, 594 insertions(+), 196 deletions(-) create mode 100644 wearable/read_BLE_data.py diff --git a/pyproject.toml b/pyproject.toml index b6ff264..61557dc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,9 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.12" license-files = ["LICENSE"] -dependencies = [] +dependencies = [ + "bleak>=2.1.1", +] [dependency-groups] diff --git a/requirements.txt b/requirements.txt index 943a453..ee1028f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,139 +1,112 @@ # This file was autogenerated by uv via the following command: # uv export --frozen 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Press Ctrl+C to stop.") + + try: + while True: + await asyncio.sleep(1) + except KeyboardInterrupt: + print("\nStopping recording...") + + await client.stop_notify(CHAR_UUID) + + file.close() + print("File saved.") + + +asyncio.run(main()) From e8398a0d9975cdc7884c65907acd0782dae19356 Mon Sep 17 00:00:00 2001 From: denjev03 Date: Tue, 3 Mar 2026 19:03:11 +0100 Subject: [PATCH 02/28] feat: script for sending IMU data from Arduino --- wearable/send_IMU_data.cpp | 94 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 wearable/send_IMU_data.cpp diff --git a/wearable/send_IMU_data.cpp b/wearable/send_IMU_data.cpp new file mode 100644 index 0000000..83f1664 --- /dev/null +++ b/wearable/send_IMU_data.cpp @@ -0,0 +1,94 @@ +#include +#include + +BLEService imuService("12345678-1234-1234-1234-1234567890ab"); +BLECharacteristic imuCharacteristic( + "12345678-1234-1234-1234-1234567890ac", + BLENotify, + 24 +); + + +const float accelerationThreshold = 2.5; // G +const int sampleRate = 50; // Hz +const int captureSeconds = 2; +const int numSamples = sampleRate * captureSeconds; + + +int samplesRead = numSamples; + +void setup() { + Serial.begin(115200); + + if (!BLE.begin()) { + while (1); + } + + if (!IMU.begin()) { + while (1); + } + + BLE.setLocalName("NameOfYourDevice"); + BLE.setAdvertisedService(imuService); + + imuService.addCharacteristic(imuCharacteristic); + BLE.addService(imuService); + + BLE.advertise(); + Serial.println("Advertising..."); +} + +void loop() { + + BLEDevice central = BLE.central(); + + if (central) { + Serial.println("Connected"); + + while (central.connected()) { + + float ax, ay, az, gx, gy, gz; + + // ===== 1) Vent på significant motion ===== + while (samplesRead == numSamples && central.connected()) { + + if (IMU.accelerationAvailable()) { + + IMU.readAcceleration(ax, ay, az); + + float aSum = fabs(ax) + fabs(ay) + fabs(az); + + if (aSum >= accelerationThreshold) { + samplesRead = 0; // start capture + Serial.println("Motion detected"); + break; + } + } + } + + + while (samplesRead < numSamples && central.connected()) { + + if (IMU.accelerationAvailable() && IMU.gyroscopeAvailable()) { + + IMU.readAcceleration(ax, ay, az); + IMU.readGyroscope(gx, gy, gz); + + float data[6] = {ax, ay, az, gx, gy, gz}; + imuCharacteristic.writeValue((byte*)data, sizeof(data)); + + samplesRead++; + + delay(1000 / sampleRate); + } + } + + + if (samplesRead == numSamples) { + Serial.println("Capture complete"); + } + } + + Serial.println("Disconnected"); + } +} \ No newline at end of file From 14a27a07fe9ae0baf45e3431abd94d943490aad3 Mon Sep 17 00:00:00 2001 From: denjev03 Date: Sat, 7 Mar 2026 12:08:59 +0100 Subject: [PATCH 03/28] feat: added globale variables --- pyproject.toml | 1 + requirements.txt | 3 +++ uv.lock | 15 ++++++++++++++- .../{read_BLE_data.py => IMU_data_collection.py} | 10 ++++++++-- wearable/device_scanner.py | 11 +++++++++++ wearable/{send_IMU_data.cpp => send_IMU_data.ino} | 1 - 6 files changed, 37 insertions(+), 4 deletions(-) rename wearable/{read_BLE_data.py => IMU_data_collection.py} (84%) create mode 100644 wearable/device_scanner.py rename wearable/{send_IMU_data.cpp => send_IMU_data.ino} (99%) diff --git a/pyproject.toml b/pyproject.toml index 61557dc..37454d0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,6 +7,7 @@ requires-python = ">=3.12" license-files = ["LICENSE"] dependencies = [ "bleak>=2.1.1", + "python-dotenv>=1.2.2", ] diff --git a/requirements.txt b/requirements.txt index ee1028f..c741b43 100644 --- a/requirements.txt +++ b/requirements.txt @@ -150,6 +150,9 @@ pytest==8.4.2 \ pytest-cov==6.3.0 \ --hash=sha256:35c580e7800f87ce892e687461166e1ac2bcb8fb9e13aea79032518d6e503ff2 \ --hash=sha256:440db28156d2468cafc0415b4f8e50856a0d11faefa38f30906048fe490f1749 +python-dotenv==1.2.2 \ + --hash=sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a \ + --hash=sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3 pyyaml==6.0.2 \ --hash=sha256:0833f8694549e586547b576dcfaba4a6b55b9e96098b36cdc7ebefe667dfed48 \ --hash=sha256:0ffe8360bab4910ef1b9e87fb812d8bc0a308b0d0eef8c8f44e0254ab3b07133 \ diff --git a/uv.lock b/uv.lock index 6691bc8..93e9e59 100644 --- a/uv.lock +++ b/uv.lock @@ -619,6 +619,7 @@ version = "0.0.1" source = { virtual = "." } dependencies = [ { name = "bleak" }, + { name = "python-dotenv" }, ] [package.dev-dependencies] @@ -638,7 +639,10 @@ docs = [ ] [package.metadata] -requires-dist = [{ name = "bleak", specifier = ">=2.1.1" }] +requires-dist = [ + { name = "bleak", specifier = ">=2.1.1" }, + { name = "python-dotenv", specifier = ">=1.2.2" }, +] [package.metadata.requires-dev] dev = [ @@ -840,6 +844,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, ] +[[package]] +name = "python-dotenv" +version = "1.2.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/82/ed/0301aeeac3e5353ef3d94b6ec08bbcabd04a72018415dcb29e588514bba8/python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3", size = 50135, upload-time = "2026-03-01T16:00:26.196Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0b/d7/1959b9648791274998a9c3526f6d0ec8fd2233e4d4acce81bbae76b44b2a/python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a", size = 22101, upload-time = "2026-03-01T16:00:25.09Z" }, +] + [[package]] name = "pyyaml" version = "6.0.2" diff --git a/wearable/read_BLE_data.py b/wearable/IMU_data_collection.py similarity index 84% rename from wearable/read_BLE_data.py rename to wearable/IMU_data_collection.py index 543bf64..aa5401d 100644 --- a/wearable/read_BLE_data.py +++ b/wearable/IMU_data_collection.py @@ -2,10 +2,16 @@ import struct import csv import time +import os +from dotenv import load_dotenv from bleak import BleakClient -ADDRESS = "A6:12:65:AC:5D:91" -CHAR_UUID = "12345678-1234-1234-1234-1234567890ac" +load_dotenv() +address = os.getenv("MAC_ADRESS") +char_uuid = os.getenv("UUID") + +ADDRESS = address +CHAR_UUID = char_uuid LABEL = "shoulder_press" file = open("shoulder_press.csv", "a", newline="") diff --git a/wearable/device_scanner.py b/wearable/device_scanner.py new file mode 100644 index 0000000..f4d1de2 --- /dev/null +++ b/wearable/device_scanner.py @@ -0,0 +1,11 @@ +import asyncio +from bleak import BleakScanner + + +async def main(): + devices = await BleakScanner.discover() + for d in devices: + print(d) + + +asyncio.run(main()) diff --git a/wearable/send_IMU_data.cpp b/wearable/send_IMU_data.ino similarity index 99% rename from wearable/send_IMU_data.cpp rename to wearable/send_IMU_data.ino index 83f1664..ca35644 100644 --- a/wearable/send_IMU_data.cpp +++ b/wearable/send_IMU_data.ino @@ -14,7 +14,6 @@ const int sampleRate = 50; // Hz const int captureSeconds = 2; const int numSamples = sampleRate * captureSeconds; - int samplesRead = numSamples; void setup() { From 5f36a4f706a85a85e5c3be766f266687c5de5ce4 Mon Sep 17 00:00:00 2001 From: denjev03 Date: Sat, 7 Mar 2026 12:28:49 +0100 Subject: [PATCH 04/28] feat: cleand up file structure --- .gitignore | 5 ++- wearable/{ => firmware}/send_IMU_data.ino | 14 ++++---- wearable/{ => python}/IMU_data_collection.py | 17 ++++++++-- wearable/python/bleak_test.py | 27 +++++++++++++++ wearable/{ => python}/device_scanner.py | 0 wearable/python/exercise_clasification.py | 35 ++++++++++++++++++++ 6 files changed, 88 insertions(+), 10 deletions(-) rename wearable/{ => firmware}/send_IMU_data.ino (83%) rename wearable/{ => python}/IMU_data_collection.py (69%) create mode 100644 wearable/python/bleak_test.py rename wearable/{ => python}/device_scanner.py (100%) create mode 100644 wearable/python/exercise_clasification.py diff --git a/.gitignore b/.gitignore index 8727d1f..25af7c5 100644 --- a/.gitignore +++ b/.gitignore @@ -251,4 +251,7 @@ lerna-debug.log # System Files .DS_Store -Thumbs.db \ No newline at end of file +Thumbs.db + +#CSV files +*.csv \ No newline at end of file diff --git a/wearable/send_IMU_data.ino b/wearable/firmware/send_IMU_data.ino similarity index 83% rename from wearable/send_IMU_data.ino rename to wearable/firmware/send_IMU_data.ino index ca35644..4c2252b 100644 --- a/wearable/send_IMU_data.ino +++ b/wearable/firmware/send_IMU_data.ino @@ -1,23 +1,25 @@ #include #include -BLEService imuService("12345678-1234-1234-1234-1234567890ab"); +// Sett inn dine egne UUID-er her (kan genereres på https://www.uuidgenerator.net/) +BLEService imuService("3ba9f7b5-8e2d-4f12-8338-08e1296dd5fe"); BLECharacteristic imuCharacteristic( - "12345678-1234-1234-1234-1234567890ac", + "3ba9f7b5-8e2d-4f12-8338-08e1296dd5ff", BLENotify, 24 ); -const float accelerationThreshold = 2.5; // G -const int sampleRate = 50; // Hz +const float accelerationThreshold = 0; +const int sampleRate = 50; const int captureSeconds = 2; const int numSamples = sampleRate * captureSeconds; + int samplesRead = numSamples; void setup() { - Serial.begin(115200); + Serial.begin(9600); if (!BLE.begin()) { while (1); @@ -27,7 +29,7 @@ void setup() { while (1); } - BLE.setLocalName("NameOfYourDevice"); + BLE.setLocalName("CogitoIMU"); BLE.setAdvertisedService(imuService); imuService.addCharacteristic(imuCharacteristic); diff --git a/wearable/IMU_data_collection.py b/wearable/python/IMU_data_collection.py similarity index 69% rename from wearable/IMU_data_collection.py rename to wearable/python/IMU_data_collection.py index aa5401d..31ab43d 100644 --- a/wearable/IMU_data_collection.py +++ b/wearable/python/IMU_data_collection.py @@ -12,9 +12,20 @@ ADDRESS = address CHAR_UUID = char_uuid -LABEL = "shoulder_press" +# Shoulder press = 1, Bicep curl = 2, +LABEL = 2 -file = open("shoulder_press.csv", "a", newline="") +recording_number = 1 +person = "dennis" +exercise = "bicep_curl" + +folder = "data" +os.makedirs(folder, exist_ok=True) # lager mappen hvis den ikke finnes + +filename = f"{exercise}_{person}_{recording_number}.csv" +filepath = os.path.join(folder, filename) + +file = open(filepath, "a", newline="") writer = csv.writer(file) @@ -22,7 +33,7 @@ def notification_handler(sender, data): values = struct.unpack("ffffff", data) timestamp = time.time() - row = [timestamp] + list(values) + [LABEL] + row = [timestamp] + list(values) + [LABEL] + [person] writer.writerow(row) print(row) diff --git a/wearable/python/bleak_test.py b/wearable/python/bleak_test.py new file mode 100644 index 0000000..1dd1df0 --- /dev/null +++ b/wearable/python/bleak_test.py @@ -0,0 +1,27 @@ +import asyncio +from bleak import BleakScanner, BleakClient + +DEVICE_NAME = "CogitoIMU" + + +async def main(): + print("Scanning...") + devices = await BleakScanner.discover() + + device = None + for d in devices: + print(d) + if d.name == DEVICE_NAME: + device = d + + if device is None: + print("Device not found") + return + + print("Connecting to", device) + + async with BleakClient(device) as client: + print("Connected:", client.is_connected) + + +asyncio.run(main()) diff --git a/wearable/device_scanner.py b/wearable/python/device_scanner.py similarity index 100% rename from wearable/device_scanner.py rename to wearable/python/device_scanner.py diff --git a/wearable/python/exercise_clasification.py b/wearable/python/exercise_clasification.py new file mode 100644 index 0000000..02e42a6 --- /dev/null +++ b/wearable/python/exercise_clasification.py @@ -0,0 +1,35 @@ +import asyncio +import os +from dotenv import load_dotenv +from bleak import BleakClient + +load_dotenv() +address = os.getenv("MAC_ADRESS") +char_uuid = os.getenv("UUID2") + +ADDRESS = address +CHAR_UUID = char_uuid + + +def notification_handler(sender, data): + print(f"Notification from {sender}: {data}") + + +async def main(): + async with BleakClient(ADDRESS) as client: + print("Connected:", client.is_connected) + + await client.start_notify(CHAR_UUID, notification_handler) + + print("Recording... Press Ctrl+C to stop.") + + try: + while True: + await asyncio.sleep(1) + except KeyboardInterrupt: + print("\nStopping recording...") + + await client.stop_notify(CHAR_UUID) + + +asyncio.run(main()) From fdaae6766be676f0f1b723b932a29a013e341e6a Mon Sep 17 00:00:00 2001 From: HenrikOen Date: Sat, 7 Mar 2026 16:49:19 +0100 Subject: [PATCH 05/28] Modified IMU_data_collection.py and added data --- wearable/python/IMU_data_collection.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/wearable/python/IMU_data_collection.py b/wearable/python/IMU_data_collection.py index 31ab43d..2339573 100644 --- a/wearable/python/IMU_data_collection.py +++ b/wearable/python/IMU_data_collection.py @@ -12,14 +12,14 @@ ADDRESS = address CHAR_UUID = char_uuid -# Shoulder press = 1, Bicep curl = 2, -LABEL = 2 +# Tricep extension= 0 Shoulder press = 1, Bicep curl = 2, Squat = 3, Rows = 4 +LABEL = 3 recording_number = 1 -person = "dennis" -exercise = "bicep_curl" +person = "henrik" +exercise = "squat" -folder = "data" +folder = "data\\" + exercise os.makedirs(folder, exist_ok=True) # lager mappen hvis den ikke finnes filename = f"{exercise}_{person}_{recording_number}.csv" From ad9afe4e0f95d895c184feecee9f639bbf02d90b Mon Sep 17 00:00:00 2001 From: HenrikOen Date: Sat, 7 Mar 2026 16:58:13 +0100 Subject: [PATCH 06/28] added measurement data --- .../data/bicep_curl/bicep_curl_dennis_1.csv | 804 ++++++++++++ .../data/bicep_curl/bicep_curl_gustav_1.csv | 775 ++++++++++++ wearable/python/data/rows/rows_dennis_1.csv | 902 +++++++++++++ wearable/python/data/rows/rows_gustav_1.csv | 662 ++++++++++ .../shoulder_press_dennis_1.csv | 996 +++++++++++++++ .../shoulder_press_gustav_1.csv | 871 +++++++++++++ .../shoulder_press_henrik_1.csv | 1122 +++++++++++++++++ wearable/python/data/squat/squat_dennis_1.csv | 1056 ++++++++++++++++ wearable/python/data/squat/squat_gustav_1.csv | 914 ++++++++++++++ .../tricep_extension_dennis_1.csv | 1094 ++++++++++++++++ .../tricep_extension_gustav_1.csv | 888 +++++++++++++ .../tricep_extension_henrik_1.csv | 910 +++++++++++++ 12 files changed, 10994 insertions(+) create mode 100644 wearable/python/data/bicep_curl/bicep_curl_dennis_1.csv create mode 100644 wearable/python/data/bicep_curl/bicep_curl_gustav_1.csv create mode 100644 wearable/python/data/rows/rows_dennis_1.csv create mode 100644 wearable/python/data/rows/rows_gustav_1.csv create mode 100644 wearable/python/data/shoulder_press/shoulder_press_dennis_1.csv create mode 100644 wearable/python/data/shoulder_press/shoulder_press_gustav_1.csv create mode 100644 wearable/python/data/shoulder_press/shoulder_press_henrik_1.csv create mode 100644 wearable/python/data/squat/squat_dennis_1.csv create mode 100644 wearable/python/data/squat/squat_gustav_1.csv create mode 100644 wearable/python/data/tricep_extension/tricep_extension_dennis_1.csv create mode 100644 wearable/python/data/tricep_extension/tricep_extension_gustav_1.csv create mode 100644 wearable/python/data/tricep_extension/tricep_extension_henrik_1.csv diff --git a/wearable/python/data/bicep_curl/bicep_curl_dennis_1.csv b/wearable/python/data/bicep_curl/bicep_curl_dennis_1.csv new file mode 100644 index 0000000..fbb0410 --- /dev/null +++ b/wearable/python/data/bicep_curl/bicep_curl_dennis_1.csv @@ -0,0 +1,804 @@ +1772889620.9390173,-0.8369140625,-0.4825439453125,-0.2740478515625,-0.9765624403953552,0.4272460639476776,-0.1831054538488388,2,dennis +1772889621.0147347,-0.8350830078125,-0.4793701171875,-0.271484375,-0.54931640625,0.0,0.30517578125,2,dennis +1772889621.0439222,-0.83447265625,-0.485107421875,-0.2752685546875,0.0610351525247097,-0.1831054538488388,0.1831054538488388,2,dennis +1772889621.0609438,-0.8355712890625,-0.4808349609375,-0.275634765625,1.0986328125,-0.30517578125,0.4882812201976776,2,dennis +1772889621.1045513,-0.83203125,-0.4853515625,-0.27001953125,-0.0610351525247097,-0.0610351525247097,0.30517578125,2,dennis 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+1772896119.6205056,0.9744873046875,0.21630859375,-0.152587890625,8.4228515625,0.30517578125,-3.479003667831421,0,henrik +1772896119.6215248,0.9542236328125,0.2242431640625,-0.1514892578125,-2.44140625,-2.197265625,-3.051757574081421,0,henrik +1772896120.4548037,0.9576416015625,0.220458984375,-0.1822509765625,-6.835937023162842,-1.15966796875,0.6103515625,0,henrik +1772896120.4548037,0.97607421875,0.2017822265625,-0.15380859375,-0.54931640625,-1.7089842557907104,4.45556640625,0,henrik +1772896120.4596155,0.9666748046875,0.233642578125,-0.1541748046875,1.15966796875,-2.868652105331421,1.4648436307907104,0,henrik +1772896120.4596155,0.97021484375,0.216552734375,-0.1639404296875,-4.45556640625,-3.723144292831421,0.4882812201976776,0,henrik From 77ae2996609f4de6a2b7c9bf2b6a65d05f329f59 Mon Sep 17 00:00:00 2001 From: GustavNat Date: Sat, 7 Mar 2026 17:59:25 +0100 Subject: [PATCH 07/28] plotting of IMU-data --- wearable/python/plot_IMU_data.py | 133 +++++++++++++++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 wearable/python/plot_IMU_data.py diff --git a/wearable/python/plot_IMU_data.py b/wearable/python/plot_IMU_data.py new file mode 100644 index 0000000..18f1807 --- /dev/null +++ b/wearable/python/plot_IMU_data.py @@ -0,0 +1,133 @@ +import os +import glob +import pandas as pd +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec + +DATA_DIR = os.path.join(os.path.dirname(__file__), "data") +COLUMNS = ["timestamp", "accel_x", "accel_y", "accel_z", "gyro_x", "gyro_y", "gyro_z", "label", "person"] + +EXERCISE_NAMES = { + 0: "Unknown", + 1: "Squat", + 2: "Bicep Curl", + 3: "Shoulder Press", + 4: "Rows", + 5: "Tricep Extension", +} + + +def load_all_data(data_dir: str) -> pd.DataFrame: + csv_files = glob.glob(os.path.join(data_dir, "**", "*.csv"), recursive=True) + if not csv_files: + raise FileNotFoundError(f"No CSV files found in {data_dir}") + + frames = [] + for path in csv_files: + df = pd.read_csv(path, header=None, names=COLUMNS) + frames.append(df) + + data = pd.concat(frames, ignore_index=True) + data["timestamp"] = pd.to_numeric(data["timestamp"]) + data["label"] = pd.to_numeric(data["label"], errors="coerce").fillna(0).astype(int) + return data + + +def plot_session(df: pd.DataFrame, title: str): + """Plot accel and gyro for a single recording session.""" + t = df["timestamp"] - df["timestamp"].iloc[0] + + fig, axes = plt.subplots(2, 1, figsize=(12, 7), sharex=True) + fig.suptitle(title, fontsize=13) + + axes[0].plot(t, df["accel_x"], label="X") + axes[0].plot(t, df["accel_y"], label="Y") + axes[0].plot(t, df["accel_z"], label="Z") + axes[0].set_ylabel("Acceleration (g)") + axes[0].legend() + axes[0].grid(True, alpha=0.3) + + axes[1].plot(t, df["gyro_x"], label="X") + axes[1].plot(t, df["gyro_y"], label="Y") + axes[1].plot(t, df["gyro_z"], label="Z") + axes[1].set_ylabel("Rotation (deg/s)") + axes[1].set_xlabel("Time (s)") + axes[1].legend() + axes[1].grid(True, alpha=0.3) + + plt.tight_layout() + + +def plot_all_exercises(data: pd.DataFrame): + """One subplot per exercise, overlaying all persons, showing accel magnitude.""" + exercise_ids = sorted(data["label"].unique()) + n = len(exercise_ids) + fig, axes = plt.subplots(n, 1, figsize=(13, 3.5 * n), sharex=False) + if n == 1: + axes = [axes] + + fig.suptitle("Acceleration Magnitude by Exercise", fontsize=14) + + for ax, ex_id in zip(axes, exercise_ids): + ex_name = EXERCISE_NAMES.get(ex_id, f"Label {ex_id}") + subset = data[data["label"] == ex_id] + + for person, grp in subset.groupby("person"): + grp = grp.sort_values("timestamp") + t = grp["timestamp"] - grp["timestamp"].iloc[0] + mag = (grp["accel_x"] ** 2 + grp["accel_y"] ** 2 + grp["accel_z"] ** 2) ** 0.5 + ax.plot(t.values, mag.values, label=person, alpha=0.8) + + ax.set_title(ex_name) + ax.set_ylabel("|accel| (g)") + ax.set_xlabel("Time (s)") + ax.legend() + ax.grid(True, alpha=0.3) + + plt.tight_layout() + + +def plot_per_file(data_dir: str): + """Plot each CSV file individually.""" + csv_files = sorted(glob.glob(os.path.join(data_dir, "**", "*.csv"), recursive=True)) + for path in csv_files: + df = pd.read_csv(path, header=None, names=COLUMNS) + df["timestamp"] = pd.to_numeric(df["timestamp"]) + title = os.path.relpath(path, data_dir) + plot_session(df, title) + + +def plot_accel_magnitude(csv_path: str): + """Plot acceleration magnitude (sqrt(ax^2 + ay^2 + az^2)) for a single CSV file.""" + df = pd.read_csv(csv_path, header=None, names=COLUMNS) + df["timestamp"] = pd.to_numeric(df["timestamp"]) + t = df["timestamp"] - df["timestamp"].iloc[0] + mag = (df["accel_x"] ** 2 + df["accel_y"] ** 2 + df["accel_z"] ** 2) ** 0.5 + + _, ax = plt.subplots(figsize=(12, 4)) + ax.plot(t, mag, color="tab:blue") + ax.set_title(f"Acceleration Magnitude — {os.path.basename(csv_path)}") + ax.set_xlabel("Time (s)") + ax.set_ylabel("|accel| (g)") + ax.grid(True, alpha=0.3) + plt.tight_layout() + + +if __name__ == "__main__": + data = load_all_data(DATA_DIR) + + print(f"Loaded {len(data)} samples from {data['person'].nunique()} persons " + f"across {data['label'].nunique()} exercise(s).") + print("Exercises:", {k: EXERCISE_NAMES.get(k, k) for k in sorted(data['label'].unique())}) + + # Overview: accel magnitude per exercise + plot_all_exercises(data) + + # Individual file plots + plot_per_file(DATA_DIR) + + # Acceleration magnitude for each CSV file + for csv_path in sorted(glob.glob(os.path.join(DATA_DIR, "**", "*.csv"), recursive=True)): + plot_accel_magnitude(csv_path) + + plt.show() From 0f03f65c55af2097ae81b3c7f61937028f45a2ae Mon Sep 17 00:00:00 2001 From: Odin-ntnu Date: Thu, 12 Mar 2026 18:36:52 +0100 Subject: [PATCH 08/28] Update IMU_data_collection.py --- wearable/python/IMU_data_collection.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/wearable/python/IMU_data_collection.py b/wearable/python/IMU_data_collection.py index 2339573..26323be 100644 --- a/wearable/python/IMU_data_collection.py +++ b/wearable/python/IMU_data_collection.py @@ -13,11 +13,11 @@ ADDRESS = address CHAR_UUID = char_uuid # Tricep extension= 0 Shoulder press = 1, Bicep curl = 2, Squat = 3, Rows = 4 -LABEL = 3 +LABEL = 2 recording_number = 1 -person = "henrik" -exercise = "squat" +person = "odin" +exercise = "bicep_curl" folder = "data\\" + exercise os.makedirs(folder, exist_ok=True) # lager mappen hvis den ikke finnes From ccdf4d1f775c6a04be960aa4b4b60ac6bed80bfb Mon Sep 17 00:00:00 2001 From: denjev03 Date: Thu, 12 Mar 2026 18:39:16 +0100 Subject: [PATCH 09/28] feat: added labels to csv --- pyproject.toml | 3 + requirements.txt | 681 ++++++++++++- uv.lock | 942 +++++++++++++++++- wearable/python/IMU_data_collection.py | 4 +- wearable/python/ML.py | 15 + .../data/bicep_curl/bicep_curl_dennis_1.csv | 1 + .../data/bicep_curl/bicep_curl_gustav_1.csv | 1 + wearable/python/data/rows/rows_dennis_1.csv | 1 + wearable/python/data/rows/rows_gustav_1.csv | 1 + .../shoulder_press_dennis_1.csv | 1 + .../shoulder_press_gustav_1.csv | 1 + .../shoulder_press_henrik_1.csv | 1 + wearable/python/data/squat/squat_dennis_1.csv | 1 + wearable/python/data/squat/squat_gustav_1.csv | 1 + .../tricep_extension_dennis_1.csv | 1 + .../tricep_extension_gustav_1.csv | 1 + .../tricep_extension_henrik_1.csv | 1 + wearable/python/data_visualization.py | 8 + 18 files changed, 1646 insertions(+), 19 deletions(-) create mode 100644 wearable/python/ML.py create mode 100644 wearable/python/data_visualization.py diff --git a/pyproject.toml b/pyproject.toml index 37454d0..ea57728 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,10 @@ requires-python = ">=3.12" license-files = ["LICENSE"] dependencies = [ "bleak>=2.1.1", + "matplotlib>=3.10.8", + "pandas>=3.0.1", "python-dotenv>=1.2.2", + "tensorflow>=2.21.0", ] diff --git a/requirements.txt b/requirements.txt index c741b43..61f3c08 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,14 +1,116 @@ # This 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coverage==7.10.3 \ --hash=sha256:03db599f213341e2960430984e04cf35fb179724e052a3ee627a068653cf4a7c \ --hash=sha256:07009152f497a0464ffdf2634586787aea0e69ddd023eafb23fc38267db94b84 \ @@ -67,6 +169,9 @@ coverage==7.10.3 \ --hash=sha256:f35580f19f297455f44afcd773c9c7a058e52eb6eb170aa31222e635f2e38b87 \ --hash=sha256:f5983c132a62d93d71c9ef896a0b9bf6e6828d8d2ea32611f58684fba60bba35 \ --hash=sha256:fe72cbdd12d9e0f4aca873fa6d755e103888a7f9085e4a62d282d9d5b9f7928c +cycler==0.12.1 \ + --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ + --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c dbus-fast==4.0.0 ; sys_platform == 'linux' \ --hash=sha256:0a91ec3707b743c2e211fa9ecd08ee483c3af19a2028ad90d2911a7e17d20737 \ --hash=sha256:0b2aaf80991734e2bbff60b0f57b70322668acccb8bb15a0380ca80b8f8c5d72 \ @@ -92,18 +197,486 @@ distlib==0.4.0 \ filelock==3.18.0 \ --hash=sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2 \ 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--hash=sha256:f95c00d5d6700b2b890479664a06e754974848afaae5e21beb4d83c106923fd0 \ + --hash=sha256:fc44ef1f3de4f45b50ccf9136999d71abb99dca7706bc75d222ed350b9fd2289 platformdirs==4.3.8 \ --hash=sha256:3d512d96e16bcb959a814c9f348431070822a6496326a4be0911c40b5a74c2bc \ --hash=sha256:ff7059bb7eb1179e2685604f4aaf157cfd9535242bd23742eadc3c13542139b4 @@ -113,6 +686,15 @@ pluggy==1.6.0 \ pre-commit==4.5.1 \ --hash=sha256:3b3afd891e97337708c1674210f8eba659b52a38ea5f822ff142d10786221f77 \ --hash=sha256:eb545fcff725875197837263e977ea257a402056661f09dae08e4b149b030a61 +protobuf==7.34.0 \ + --hash=sha256:3871a3df67c710aaf7bb8d214cc997342e63ceebd940c8c7fc65c9b3d697591a \ + --hash=sha256:4a72a8ec94e7a9f7ef7fe818ed26d073305f347f8b3b5ba31e22f81fd85fca02 \ + --hash=sha256:8e329966799f2c271d5e05e236459fe1cbfdb8755aaa3b0914fa60947ddea408 \ + --hash=sha256:964cf977e07f479c0697964e83deda72bcbc75c3badab506fb061b352d991b01 \ + --hash=sha256:9d7a5005fb96f3c1e64f397f91500b0eb371b28da81296ae73a6b08a5b76cdd6 \ + 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tf.keras.datasets.mnist + +(x_train, y_train), (x_test, y_test) = mnist.load_data() +x_train, x_test = x_train / 255.0, x_test / 255.0 + +model = tf.keras.models.Sequential( + [ + tf.keras.layers.Flatten(input_shape=(28, 28)), + tf.keras.layers.Dense(128, activation="relu"), + tf.keras.layers.Dropout(0.2), + tf.keras.layers.Dense(10), + ] +) diff --git a/wearable/python/data/bicep_curl/bicep_curl_dennis_1.csv b/wearable/python/data/bicep_curl/bicep_curl_dennis_1.csv index fbb0410..f0aacb5 100644 --- a/wearable/python/data/bicep_curl/bicep_curl_dennis_1.csv +++ b/wearable/python/data/bicep_curl/bicep_curl_dennis_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772889620.9390173,-0.8369140625,-0.4825439453125,-0.2740478515625,-0.9765624403953552,0.4272460639476776,-0.1831054538488388,2,dennis 1772889621.0147347,-0.8350830078125,-0.4793701171875,-0.271484375,-0.54931640625,0.0,0.30517578125,2,dennis 1772889621.0439222,-0.83447265625,-0.485107421875,-0.2752685546875,0.0610351525247097,-0.1831054538488388,0.1831054538488388,2,dennis diff --git a/wearable/python/data/bicep_curl/bicep_curl_gustav_1.csv b/wearable/python/data/bicep_curl/bicep_curl_gustav_1.csv index fba9284..59312c6 100644 --- a/wearable/python/data/bicep_curl/bicep_curl_gustav_1.csv +++ b/wearable/python/data/bicep_curl/bicep_curl_gustav_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772894610.2445748,-0.962646484375,-0.24267578125,-0.021484375,1.5258787870407104,0.0,-0.4272460639476776,2,gustav 1772894610.2734165,-0.96435546875,-0.237548828125,-0.029541015625,-2.807616949081421,0.1831054538488388,-1.5869139432907104,2,gustav 1772894610.3029106,-0.9698486328125,-0.218017578125,-0.05126953125,0.4882812201976776,-1.4648436307907104,-1.8310545682907104,2,gustav diff --git a/wearable/python/data/rows/rows_dennis_1.csv b/wearable/python/data/rows/rows_dennis_1.csv index 33864c1..b13e2f8 100644 --- a/wearable/python/data/rows/rows_dennis_1.csv +++ b/wearable/python/data/rows/rows_dennis_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772890010.398463,-0.955322265625,-0.2119140625,-0.15087890625,-0.9765624403953552,-0.1831054538488388,0.4882812201976776,4,dennis 1772890010.4473574,-0.945556640625,-0.2197265625,-0.154541015625,-1.8310545682907104,-0.3662109076976776,0.1220703050494194,4,dennis 1772890010.5214446,-0.935546875,-0.215087890625,-0.14697265625,-1.7700194120407104,-0.54931640625,0.1220703050494194,4,dennis diff --git a/wearable/python/data/rows/rows_gustav_1.csv b/wearable/python/data/rows/rows_gustav_1.csv index 0ab3d75..63322f8 100644 --- a/wearable/python/data/rows/rows_gustav_1.csv +++ b/wearable/python/data/rows/rows_gustav_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772895820.7607853,-0.962890625,0.1151123046875,0.08349609375,-5.615233898162842,24.658201217651367,24.414060592651367,4,gustav 1772895820.7898414,-0.9888916015625,0.0703125,0.0692138671875,-12.451170921325684,16.78466796875,25.878904342651367,4,gustav 1772895820.8441007,-0.975341796875,0.10205078125,0.0697021484375,-4.45556640625,4.45556640625,24.230955123901367,4,gustav diff --git a/wearable/python/data/shoulder_press/shoulder_press_dennis_1.csv b/wearable/python/data/shoulder_press/shoulder_press_dennis_1.csv index e06a192..3571c2c 100644 --- a/wearable/python/data/shoulder_press/shoulder_press_dennis_1.csv +++ b/wearable/python/data/shoulder_press/shoulder_press_dennis_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772889729.9308772,0.9107666015625,0.1322021484375,0.383544921875,-3.784179449081421,-5.554198741912842,1.0986328125,1,dennis 1772889729.9728525,0.932373046875,0.125732421875,0.3712158203125,-2.44140625,-3.479003667831421,1.220703125,1,dennis 1772889730.005074,0.931884765625,0.132568359375,0.3895263671875,-0.9765624403953552,-0.54931640625,0.4882812201976776,1,dennis diff --git a/wearable/python/data/shoulder_press/shoulder_press_gustav_1.csv b/wearable/python/data/shoulder_press/shoulder_press_gustav_1.csv index 14af1a4..655fd60 100644 --- a/wearable/python/data/shoulder_press/shoulder_press_gustav_1.csv +++ b/wearable/python/data/shoulder_press/shoulder_press_gustav_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772894804.1599865,0.9012451171875,0.4154052734375,-0.085693359375,-0.9765624403953552,3.723144292831421,-1.8310545682907104,1,gustav 1772894804.247412,0.9019775390625,0.40576171875,-0.0814208984375,-1.3427733182907104,3.234863042831421,-1.7700194120407104,1,gustav 1772894804.2561038,0.909423828125,0.408935546875,-0.0927734375,1.2817381620407104,3.112792730331421,-0.6713866591453552,1,gustav diff --git a/wearable/python/data/shoulder_press/shoulder_press_henrik_1.csv b/wearable/python/data/shoulder_press/shoulder_press_henrik_1.csv index 002a70f..1a61488 100644 --- a/wearable/python/data/shoulder_press/shoulder_press_henrik_1.csv +++ b/wearable/python/data/shoulder_press/shoulder_press_henrik_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772897651.2735004,0.9329833984375,0.1707763671875,0.390625,5.310058116912842,-0.30517578125,-7.934569835662842,1,henrik 1772897651.2988472,0.9658203125,0.1336669921875,0.42333984375,4.21142578125,-3.784179449081421,-2.01416015625,1,henrik 1772897651.3448708,0.954833984375,0.14453125,0.4254150390625,-1.9531248807907104,-6.225585460662842,-4.2724609375,1,henrik diff --git a/wearable/python/data/squat/squat_dennis_1.csv b/wearable/python/data/squat/squat_dennis_1.csv index 9a882d8..890bcd9 100644 --- a/wearable/python/data/squat/squat_dennis_1.csv +++ b/wearable/python/data/squat/squat_dennis_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772889934.0413623,0.685791015625,-0.2735595703125,0.6090087890625,5.859374523162842,-3.173827886581421,2.563476324081421,3,dennis 1772889934.1015446,0.7000732421875,-0.29833984375,0.6319580078125,5.0048828125,-3.051757574081421,4.82177734375,3,dennis 1772889934.116594,0.7083740234375,-0.312744140625,0.6595458984375,3.479003667831421,-1.8920897245407104,2.929687261581421,3,dennis diff --git a/wearable/python/data/squat/squat_gustav_1.csv b/wearable/python/data/squat/squat_gustav_1.csv index aa5be61..e6c4ed1 100644 --- a/wearable/python/data/squat/squat_gustav_1.csv +++ b/wearable/python/data/squat/squat_gustav_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772894927.8483737,0.629638671875,-0.668701171875,0.414306640625,-1.0986328125,1.9531248807907104,0.0,3,gustav 1772894927.9101162,0.630859375,-0.671142578125,0.4169921875,1.15966796875,2.563476324081421,-0.6713866591453552,3,gustav 1772894927.9101162,0.63427734375,-0.67431640625,0.4169921875,0.6713866591453552,2.50244140625,-1.4038084745407104,3,gustav diff --git a/wearable/python/data/tricep_extension/tricep_extension_dennis_1.csv b/wearable/python/data/tricep_extension/tricep_extension_dennis_1.csv index 28be0ea..6b2176c 100644 --- a/wearable/python/data/tricep_extension/tricep_extension_dennis_1.csv +++ b/wearable/python/data/tricep_extension/tricep_extension_dennis_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772889826.4435542,-0.124755859375,0.923095703125,-0.2938232421875,-1.7700194120407104,-0.1831054538488388,0.0610351525247097,0,dennis 1772889826.504195,-0.1279296875,0.926513671875,-0.3118896484375,1.4038084745407104,0.7324218153953552,0.9155272841453552,0,dennis 1772889826.5124376,-0.1192626953125,0.9361572265625,-0.302978515625,-0.8544921278953552,0.6103515625,0.6713866591453552,0,dennis diff --git a/wearable/python/data/tricep_extension/tricep_extension_gustav_1.csv b/wearable/python/data/tricep_extension/tricep_extension_gustav_1.csv index 3fc8a6b..61521bc 100644 --- a/wearable/python/data/tricep_extension/tricep_extension_gustav_1.csv +++ b/wearable/python/data/tricep_extension/tricep_extension_gustav_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772894705.6774936,-0.0867919921875,0.827392578125,-0.4102783203125,2.0751953125,0.1220703050494194,-12.451170921325684,0,gustav 1772894705.7188146,-0.0902099609375,0.8472900390625,-0.3779296875,-5.737304210662842,5.310058116912842,-11.047362327575684,0,gustav 1772894705.7518148,-0.0855712890625,0.8587646484375,-0.399169921875,-4.45556640625,7.934569835662842,-5.920409679412842,0,gustav diff --git a/wearable/python/data/tricep_extension/tricep_extension_henrik_1.csv b/wearable/python/data/tricep_extension/tricep_extension_henrik_1.csv index 61e2115..40965a2 100644 --- a/wearable/python/data/tricep_extension/tricep_extension_henrik_1.csv +++ b/wearable/python/data/tricep_extension/tricep_extension_henrik_1.csv @@ -1,3 +1,4 @@ +time,ax,ay,az,gx,gy,gz,label,person 1772896072.1567667,0.9515380859375,0.2064208984375,-0.1519775390625,1.3427733182907104,-4.94384765625,-13.488768577575684,0,henrik 1772896072.2181222,0.975341796875,0.1995849609375,-0.144287109375,3.784179449081421,-4.0283203125,-10.314940452575684,0,henrik 1772896072.2614384,0.96875,0.1697998046875,-0.153076171875,-1.6479490995407104,-4.82177734375,-9.8876953125,0,henrik diff --git a/wearable/python/data_visualization.py b/wearable/python/data_visualization.py new file mode 100644 index 0000000..7cc29a1 --- /dev/null +++ b/wearable/python/data_visualization.py @@ -0,0 +1,8 @@ +import pandas as pd +from pathlib import Path + +data_path = Path("./data/shoulder_press/shoulder_press_dennis_1.csv") +df = pd.read_csv(data_path) +print(df.head()) + +df.to From a8567e8b78c00508945815c76d1b95dc917d4535 Mon Sep 17 00:00:00 2001 From: denjev03 Date: Tue, 17 Mar 2026 18:42:30 +0100 Subject: [PATCH 10/28] feat: added labels to csv file for new recordings --- wearable/python/IMU_data_collection.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/wearable/python/IMU_data_collection.py b/wearable/python/IMU_data_collection.py index b550d6f..33c24e8 100644 --- a/wearable/python/IMU_data_collection.py +++ b/wearable/python/IMU_data_collection.py @@ -7,16 +7,17 @@ from bleak import BleakClient load_dotenv() -address = os.getenv("MAC_ADRESS") +address = os.getenv("MAC_ADDRESS") char_uuid = os.getenv("UUID") ADDRESS = address CHAR_UUID = char_uuid + # Tricep extension= 0 Shoulder press = 1, Bicep curl = 2, Squat = 3, Rows = 4 LABEL = 2 recording_number = 1 -person = "test" +person = "dj" exercise = "squat" folder = "data\\" + exercise @@ -41,6 +42,9 @@ def notification_handler(sender, data): async def main(): async with BleakClient(ADDRESS) as client: print("Connected:", client.is_connected) + csv.writer(file).writerow( + ["timestamp", "ax", "ay", "az", "gx", "gy", "gz", "label", "person"] + ) await client.start_notify(CHAR_UUID, notification_handler) From 43e367d0e14ac8a3a1c6b7b1e5fd2aafc65891f1 Mon Sep 17 00:00:00 2001 From: GustavNat Date: Tue, 17 Mar 2026 20:00:06 +0100 Subject: [PATCH 11/28] Script for counting repetitions in python. It is to test the dsp and fuck-around methods to be implemented on our arduino nano. As-salamu alaykum:) --- wearable/python/rep_counter.py | 265 +++++++++++++++++++++++++++++++++ 1 file changed, 265 insertions(+) create mode 100644 wearable/python/rep_counter.py diff --git a/wearable/python/rep_counter.py b/wearable/python/rep_counter.py new file mode 100644 index 0000000..3493e85 --- /dev/null +++ b/wearable/python/rep_counter.py @@ -0,0 +1,265 @@ +""" +rep_counter.py — counts exercise repetitions from raw IMU CSV data. + +Supported files (placed in data/): + - bicep_curl_dennis_1.csv + - rows_dennis_1.csv +""" + +import os +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from scipy.signal import butter, filtfilt, find_peaks + +# --------------------------------------------------------------------------- +# Configuration — tweak these without touching the algorithm +# --------------------------------------------------------------------------- + +DATA_DIR = os.path.join(os.path.dirname(__file__), "data") + +FILES = [ + os.path.join("bicep_curl", "bicep_curl_gustav_1.csv"), + os.path.join("rows", "rows_dennis_1.csv"), +] + +# Expected column names (order matters when the CSV has no header) +COLUMN_NAMES = ["timestamp", "accel_x", "accel_y", "accel_z", + "gyro_x", "gyro_y", "gyro_z", "label", "person"] + +# Candidate acceleration column name fragments (case-insensitive substring match) +ACCEL_CANDIDATES = ["accel_x", "accel_y", "accel_z", "ax", "ay", "az"] + +# Low-pass filter +FILTER_CUTOFF_HZ = 3.0 # cut-off frequency in Hz +FILTER_ORDER = 4 # Butterworth filter order + +# Peak detection +MIN_PEAK_DISTANCE_SAMPLES = 20 # minimum samples between counted peaks +MIN_PEAK_PROMINENCE = 0.05 # minimum prominence to even be a candidate + +# Adaptive threshold +ADAPTIVE_THRESHOLD_FACTOR = 0.6 # T = factor * mean(last two valid peaks) +SEED_THRESHOLD = 0.3 # used until two valid peaks have been found + +# --------------------------------------------------------------------------- +# I/O helpers +# --------------------------------------------------------------------------- + +def load_csv(filepath: str) -> pd.DataFrame: + """Load a CSV, assigning column names when no header is present.""" + # Peek at the first line to decide whether a header exists + with open(filepath, "r") as fh: + first = fh.readline() + + first_col = first.split(",")[0].strip().lower() + # Accept any non-numeric first cell as a header row + try: + float(first_col) + has_header = False + except ValueError: + has_header = True + + if has_header: + df = pd.read_csv(filepath) + # Normalise timestamp column: rename 'time' -> 'timestamp' if needed + if "timestamp" not in df.columns: + time_cols = [c for c in df.columns if c.lower() in ("time", "timestamp", "t")] + if time_cols: + df = df.rename(columns={time_cols[0]: "timestamp"}) + else: + df = pd.read_csv(filepath, header=None, names=COLUMN_NAMES) + + # Ensure timestamp is numeric + if "timestamp" in df.columns: + df["timestamp"] = pd.to_numeric(df["timestamp"], errors="coerce") + + return df + + +def find_accel_columns(df: pd.DataFrame) -> list[str]: + """Return the acceleration columns present in df, matched by substring.""" + matched = [] + for col in df.columns: + for cand in ACCEL_CANDIDATES: + if cand.lower() in col.lower() and col not in matched: + matched.append(col) + break + if len(matched) < 3: + raise ValueError( + f"Expected at least 3 acceleration columns, found: {matched}\n" + f"Available columns: {list(df.columns)}" + ) + return matched[:3] # keep exactly three + + +def estimate_sample_rate(timestamps: pd.Series) -> float: + """Estimate sample rate in Hz from a timestamp column (seconds).""" + diffs = np.diff(timestamps.values) + median_dt = np.median(diffs[diffs > 0]) + return float(1.0 / median_dt) + +# --------------------------------------------------------------------------- +# Signal processing +# --------------------------------------------------------------------------- + +def select_best_axis(df: pd.DataFrame, accel_cols: list[str]) -> tuple[str, np.ndarray]: + """Return the column name and values of the axis with highest variance.""" + variances = {col: df[col].var() for col in accel_cols} + best = max(variances, key=variances.__getitem__) + return best, df[best].values + + +def lowpass_filter(signal: np.ndarray, cutoff_hz: float, + sample_rate: float, order: int = 4) -> np.ndarray: + """Apply a zero-phase Butterworth low-pass filter.""" + nyq = 0.5 * sample_rate + norm_cutoff = min(cutoff_hz / nyq, 0.99) # clamp below Nyquist + b, a = butter(order, norm_cutoff, btype="low") + return filtfilt(b, a, signal) + + +def detect_candidate_peaks(filtered: np.ndarray, + min_distance: int, + min_prominence: float) -> np.ndarray: + """Find all candidate peaks in the filtered signal.""" + peaks, _ = find_peaks( + filtered, + distance=min_distance, + prominence=min_prominence, + ) + return peaks + +# --------------------------------------------------------------------------- +# Adaptive threshold repetition counter +# --------------------------------------------------------------------------- + +def count_reps(filtered: np.ndarray, + candidate_peaks: np.ndarray, + threshold_factor: float = ADAPTIVE_THRESHOLD_FACTOR, + seed_threshold: float = SEED_THRESHOLD, + min_distance: int = MIN_PEAK_DISTANCE_SAMPLES) -> tuple[list[int], list[float]]: + """ + Walk through candidate peaks and count a rep whenever a peak exceeds + the adaptive threshold T = factor * mean(last two valid peak amplitudes). + + Returns + ------- + counted_indices : list of sample indices that were counted as reps + thresholds_at_count : adaptive threshold value at each counted peak + """ + last_two: list[float] = [] # amplitudes of the two most-recent counted peaks + counted: list[int] = [] + thresholds: list[float] = [] + last_counted_idx = -min_distance # allow the very first peak to be counted + + for idx in candidate_peaks: + amp = float(filtered[idx]) + + # Compute current threshold + if len(last_two) < 2: + threshold = seed_threshold + else: + threshold = threshold_factor * np.mean(last_two) + + # Enforce minimum distance between counted peaks + if (idx - last_counted_idx) < min_distance: + continue + + if amp >= threshold: + counted.append(idx) + thresholds.append(threshold) + last_two = (last_two + [amp])[-2:] # keep only the last two + last_counted_idx = idx + + return counted, thresholds + +# --------------------------------------------------------------------------- +# Plotting +# --------------------------------------------------------------------------- + +def plot_results(raw: np.ndarray, + filtered: np.ndarray, + counted_peaks: list[int], + axis_name: str, + filename: str) -> None: + """Plot raw signal, filtered signal, and counted-rep markers.""" + rep_count = len(counted_peaks) + samples = np.arange(len(raw)) + + fig, ax = plt.subplots(figsize=(12, 5)) + + ax.plot(samples, raw, color="lightsteelblue", linewidth=0.8, + alpha=0.7, label=f"Raw {axis_name}") + ax.plot(samples, filtered, color="steelblue", linewidth=1.5, + label="Filtered") + + if counted_peaks: + ax.plot(counted_peaks, filtered[counted_peaks], + "rv", markersize=10, zorder=5, label=f"Counted rep") + + ax.set_xlabel("Sample index") + ax.set_ylabel("Acceleration (g)") + ax.set_title(f"{filename} — Reps counted: {rep_count} (axis: {axis_name})") + ax.legend(loc="upper right") + ax.grid(True, alpha=0.3) + + plt.tight_layout() + plt.show() + +# --------------------------------------------------------------------------- +# Main pipeline +# --------------------------------------------------------------------------- + +def process_file(filepath: str) -> None: + filename = os.path.basename(filepath) + print(f"\n{'='*60}") + print(f"Processing: {filename}") + + # 1. Load data + df = load_csv(filepath) + + # 2. Identify acceleration columns + accel_cols = find_accel_columns(df) + print(f" Acceleration columns: {accel_cols}") + + # 3. Estimate sample rate + if "timestamp" in df.columns: + sample_rate = estimate_sample_rate(df["timestamp"]) + else: + sample_rate = 25.0 # fallback default + print(f" Warning: no timestamp column — assuming {sample_rate} Hz") + print(f" Estimated sample rate: {sample_rate:.1f} Hz") + + # 4. Select axis with highest variance + best_axis, raw_signal = select_best_axis(df, accel_cols) + print(f" Best axis (highest variance): {best_axis}") + + # 5. Low-pass filter + filtered = lowpass_filter(raw_signal, FILTER_CUTOFF_HZ, sample_rate, FILTER_ORDER) + + # 6. Candidate peaks + candidates = detect_candidate_peaks( + filtered, MIN_PEAK_DISTANCE_SAMPLES, MIN_PEAK_PROMINENCE + ) + print(f" Candidate peaks found: {len(candidates)}") + + # 7 & 8. Adaptive threshold → count reps + counted, _ = count_reps(filtered, candidates) + print(f" Repetitions counted: {len(counted)}") + + # 9. Plot + plot_results(raw_signal, filtered, counted, best_axis, filename) + + +def main() -> None: + for fname in FILES: + fpath = os.path.join(DATA_DIR, fname) + if not os.path.isfile(fpath): + print(f"Skipping '{fname}' — file not found in {DATA_DIR}") + continue + process_file(fpath) + + +if __name__ == "__main__": + main() From 55ed2dcbb33062f6bb97c5addda21388e9140b6d Mon Sep 17 00:00:00 2001 From: GustavNat Date: Thu, 19 Mar 2026 18:39:56 +0100 Subject: [PATCH 12/28] updated version of rep_counter --- wearable/python/rep_counter.py | 289 ++++++++++++++------------------- 1 file changed, 122 insertions(+), 167 deletions(-) diff --git a/wearable/python/rep_counter.py b/wearable/python/rep_counter.py index 3493e85..d6ff101 100644 --- a/wearable/python/rep_counter.py +++ b/wearable/python/rep_counter.py @@ -19,8 +19,7 @@ DATA_DIR = os.path.join(os.path.dirname(__file__), "data") FILES = [ - os.path.join("bicep_curl", "bicep_curl_gustav_1.csv"), - os.path.join("rows", "rows_dennis_1.csv"), + os.path.join("shoulder_press", "shoulder_press_gustav_1.csv"), ] # Expected column names (order matters when the CSV has no header) @@ -30,234 +29,190 @@ # Candidate acceleration column name fragments (case-insensitive substring match) ACCEL_CANDIDATES = ["accel_x", "accel_y", "accel_z", "ax", "ay", "az"] + +#sample +SAMPLE_RATE = 50 #ms + # Low-pass filter FILTER_CUTOFF_HZ = 3.0 # cut-off frequency in Hz FILTER_ORDER = 4 # Butterworth filter order # Peak detection -MIN_PEAK_DISTANCE_SAMPLES = 20 # minimum samples between counted peaks -MIN_PEAK_PROMINENCE = 0.05 # minimum prominence to even be a candidate +MIN_PEAK_DISTANCE_SAMPLES = 20 # minimum samples between counted peaks # Adaptive threshold -ADAPTIVE_THRESHOLD_FACTOR = 0.6 # T = factor * mean(last two valid peaks) +ADAPTIVE_THRESHOLD_FACTOR = 0.8 # T = factor * mean(last two valid peaks) SEED_THRESHOLD = 0.3 # used until two valid peaks have been found + # --------------------------------------------------------------------------- -# I/O helpers +# CSV loading # --------------------------------------------------------------------------- -def load_csv(filepath: str) -> pd.DataFrame: - """Load a CSV, assigning column names when no header is present.""" - # Peek at the first line to decide whether a header exists - with open(filepath, "r") as fh: - first = fh.readline() - - first_col = first.split(",")[0].strip().lower() - # Accept any non-numeric first cell as a header row - try: - float(first_col) - has_header = False - except ValueError: - has_header = True - - if has_header: - df = pd.read_csv(filepath) - # Normalise timestamp column: rename 'time' -> 'timestamp' if needed - if "timestamp" not in df.columns: - time_cols = [c for c in df.columns if c.lower() in ("time", "timestamp", "t")] - if time_cols: - df = df.rename(columns={time_cols[0]: "timestamp"}) - else: - df = pd.read_csv(filepath, header=None, names=COLUMN_NAMES) - - # Ensure timestamp is numeric - if "timestamp" in df.columns: - df["timestamp"] = pd.to_numeric(df["timestamp"], errors="coerce") - +def load_csv(filepath): + """Load CSV; assign COLUMN_NAMES if the file has no header or mismatched columns.""" + df = pd.read_csv(filepath) + # If columns look like integers the file had no header — reassign + if df.columns[0].isdigit() or list(df.columns) == list(range(len(df.columns))): + df = pd.read_csv(filepath, header=None, names=COLUMN_NAMES[:len(df.columns)]) return df -def find_accel_columns(df: pd.DataFrame) -> list[str]: - """Return the acceleration columns present in df, matched by substring.""" +# --------------------------------------------------------------------------- +# Column detection +# --------------------------------------------------------------------------- + +def find_accel_columns(df): + """Return the three acceleration column names by matching ACCEL_CANDIDATES.""" matched = [] for col in df.columns: - for cand in ACCEL_CANDIDATES: - if cand.lower() in col.lower() and col not in matched: - matched.append(col) - break + col_lower = col.lower() + if any(cand in col_lower for cand in ["accel_x", "ax"]): + matched.append((col, "x")) + elif any(cand in col_lower for cand in ["accel_y", "ay"]): + matched.append((col, "y")) + elif any(cand in col_lower for cand in ["accel_z", "az"]): + matched.append((col, "z")) if len(matched) < 3: - raise ValueError( - f"Expected at least 3 acceleration columns, found: {matched}\n" - f"Available columns: {list(df.columns)}" - ) - return matched[:3] # keep exactly three + raise ValueError(f"Could not find 3 acceleration columns. Found: {matched}") + # Return in x, y, z order + matched.sort(key=lambda t: t[1]) + return [col for col, _ in matched] -def estimate_sample_rate(timestamps: pd.Series) -> float: - """Estimate sample rate in Hz from a timestamp column (seconds).""" - diffs = np.diff(timestamps.values) - median_dt = np.median(diffs[diffs > 0]) - return float(1.0 / median_dt) - # --------------------------------------------------------------------------- # Signal processing # --------------------------------------------------------------------------- -def select_best_axis(df: pd.DataFrame, accel_cols: list[str]) -> tuple[str, np.ndarray]: - """Return the column name and values of the axis with highest variance.""" +def select_best_axis(df, accel_cols): + """Return (column_name, signal_array) for the axis with the highest variance.""" variances = {col: df[col].var() for col in accel_cols} - best = max(variances, key=variances.__getitem__) - return best, df[best].values + best_col = max(variances, key=variances.get) + print(f" Variances: { {c: f'{v:.4f}' for c, v in variances.items()} }") + print(f" Selected axis: {best_col}") + return best_col, df[best_col].to_numpy(dtype=float) -def lowpass_filter(signal: np.ndarray, cutoff_hz: float, - sample_rate: float, order: int = 4) -> np.ndarray: +def lowpass_filter(signal, cutoff_hz, sample_rate, order=FILTER_ORDER): """Apply a zero-phase Butterworth low-pass filter.""" nyq = 0.5 * sample_rate - norm_cutoff = min(cutoff_hz / nyq, 0.99) # clamp below Nyquist - b, a = butter(order, norm_cutoff, btype="low") + normal_cutoff = cutoff_hz / nyq + b, a = butter(order, normal_cutoff, btype="low", analog=False) return filtfilt(b, a, signal) -def detect_candidate_peaks(filtered: np.ndarray, - min_distance: int, - min_prominence: float) -> np.ndarray: - """Find all candidate peaks in the filtered signal.""" - peaks, _ = find_peaks( - filtered, - distance=min_distance, - prominence=min_prominence, - ) - return peaks - # --------------------------------------------------------------------------- -# Adaptive threshold repetition counter +# Repetition counting with adaptive threshold # --------------------------------------------------------------------------- -def count_reps(filtered: np.ndarray, - candidate_peaks: np.ndarray, - threshold_factor: float = ADAPTIVE_THRESHOLD_FACTOR, - seed_threshold: float = SEED_THRESHOLD, - min_distance: int = MIN_PEAK_DISTANCE_SAMPLES) -> tuple[list[int], list[float]]: +def count_reps(filtered_signal): """ - Walk through candidate peaks and count a rep whenever a peak exceeds - the adaptive threshold T = factor * mean(last two valid peak amplitudes). + Detect peaks and count reps using an adaptive threshold. Returns ------- - counted_indices : list of sample indices that were counted as reps - thresholds_at_count : adaptive threshold value at each counted peak + rep_indices : list[int] — sample indices of counted reps + rep_count : int """ - last_two: list[float] = [] # amplitudes of the two most-recent counted peaks - counted: list[int] = [] - thresholds: list[float] = [] - last_counted_idx = -min_distance # allow the very first peak to be counted + # Find all candidate peaks (no height filter yet, only distance) + candidate_idx, _ = find_peaks(filtered_signal, + distance=MIN_PEAK_DISTANCE_SAMPLES) + + rep_indices = [] + last_two_amplitudes = [] # rolling buffer of the last two counted peak values + threshold = SEED_THRESHOLD # bootstrap threshold - for idx in candidate_peaks: - amp = float(filtered[idx]) + for idx in candidate_idx: + amplitude = filtered_signal[idx] - # Compute current threshold - if len(last_two) < 2: - threshold = seed_threshold - else: - threshold = threshold_factor * np.mean(last_two) + if amplitude > threshold: + rep_indices.append(idx) - # Enforce minimum distance between counted peaks - if (idx - last_counted_idx) < min_distance: - continue + # Update rolling buffer + last_two_amplitudes.append(amplitude) + if len(last_two_amplitudes) > 2: + last_two_amplitudes.pop(0) - if amp >= threshold: - counted.append(idx) - thresholds.append(threshold) - last_two = (last_two + [amp])[-2:] # keep only the last two - last_counted_idx = idx + # Recompute threshold once we have two valid peaks + if len(last_two_amplitudes) == 2: + threshold = ADAPTIVE_THRESHOLD_FACTOR * np.mean(last_two_amplitudes) + + return rep_indices, len(rep_indices) - return counted, thresholds # --------------------------------------------------------------------------- # Plotting # --------------------------------------------------------------------------- -def plot_results(raw: np.ndarray, - filtered: np.ndarray, - counted_peaks: list[int], - axis_name: str, - filename: str) -> None: - """Plot raw signal, filtered signal, and counted-rep markers.""" - rep_count = len(counted_peaks) - samples = np.arange(len(raw)) - - fig, ax = plt.subplots(figsize=(12, 5)) - - ax.plot(samples, raw, color="lightsteelblue", linewidth=0.8, - alpha=0.7, label=f"Raw {axis_name}") - ax.plot(samples, filtered, color="steelblue", linewidth=1.5, - label="Filtered") - - if counted_peaks: - ax.plot(counted_peaks, filtered[counted_peaks], - "rv", markersize=10, zorder=5, label=f"Counted rep") - - ax.set_xlabel("Sample index") - ax.set_ylabel("Acceleration (g)") - ax.set_title(f"{filename} — Reps counted: {rep_count} (axis: {axis_name})") - ax.legend(loc="upper right") - ax.grid(True, alpha=0.3) +def plot_results(df, accel_cols, best_col, raw_signal, filtered_signal, + rep_indices, rep_count, title): + """Two-panel figure: all raw axes | selected axis with counted reps.""" + samples = np.arange(len(raw_signal)) + + fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 7), sharex=True) + fig.suptitle(title, fontsize=13) + + # --- Plot 1: all raw acceleration axes --- + for col in accel_cols: + ax1.plot(samples, df[col].to_numpy(dtype=float), label=col) + ax1.set_ylabel("Acceleration") + ax1.set_title("Raw acceleration — all axes") + ax1.legend(loc="upper right") + ax1.grid(True, alpha=0.3) + + # --- Plot 2: selected axis + counted reps --- + ax2.plot(samples, raw_signal, color="steelblue", label=f"{best_col} (raw)") + ax2.plot(samples, filtered_signal, color="orange", linewidth=1.5, + label="filtered") + if rep_indices: + ax2.scatter(rep_indices, filtered_signal[rep_indices], + color="red", zorder=5, label="counted rep") + ax2.set_xlabel("Sample") + ax2.set_ylabel("Acceleration") + ax2.set_title(f"Selected axis: {best_col} | Reps counted: {rep_count}") + ax2.legend(loc="upper right") + ax2.grid(True, alpha=0.3) plt.tight_layout() plt.show() + # --------------------------------------------------------------------------- -# Main pipeline +# Per-file pipeline # --------------------------------------------------------------------------- -def process_file(filepath: str) -> None: +def process_file(filepath): filename = os.path.basename(filepath) - print(f"\n{'='*60}") - print(f"Processing: {filename}") + full_path = os.path.join(DATA_DIR, filepath) - # 1. Load data - df = load_csv(filepath) + if not os.path.exists(full_path): + print(f"Skipping '{filename}' — file not found in {DATA_DIR}") + return - # 2. Identify acceleration columns + print(f"\nProcessing: {filename}") + + df = load_csv(full_path) accel_cols = find_accel_columns(df) print(f" Acceleration columns: {accel_cols}") - # 3. Estimate sample rate - if "timestamp" in df.columns: - sample_rate = estimate_sample_rate(df["timestamp"]) - else: - sample_rate = 25.0 # fallback default - print(f" Warning: no timestamp column — assuming {sample_rate} Hz") - print(f" Estimated sample rate: {sample_rate:.1f} Hz") - - # 4. Select axis with highest variance - best_axis, raw_signal = select_best_axis(df, accel_cols) - print(f" Best axis (highest variance): {best_axis}") - - # 5. Low-pass filter - filtered = lowpass_filter(raw_signal, FILTER_CUTOFF_HZ, sample_rate, FILTER_ORDER) - - # 6. Candidate peaks - candidates = detect_candidate_peaks( - filtered, MIN_PEAK_DISTANCE_SAMPLES, MIN_PEAK_PROMINENCE - ) - print(f" Candidate peaks found: {len(candidates)}") - - # 7 & 8. Adaptive threshold → count reps - counted, _ = count_reps(filtered, candidates) - print(f" Repetitions counted: {len(counted)}") - - # 9. Plot - plot_results(raw_signal, filtered, counted, best_axis, filename) - - -def main() -> None: - for fname in FILES: - fpath = os.path.join(DATA_DIR, fname) - if not os.path.isfile(fpath): - print(f"Skipping '{fname}' — file not found in {DATA_DIR}") - continue + best_col, raw_signal = select_best_axis(df, accel_cols) + + filtered_signal = lowpass_filter(raw_signal, FILTER_CUTOFF_HZ, SAMPLE_RATE) + + rep_indices, rep_count = count_reps(filtered_signal) + print(f" Repetitions counted: {rep_count}") + + title = f"{filename} — {rep_count} reps" + plot_results(df, accel_cols, best_col, raw_signal, filtered_signal, + rep_indices, rep_count, title) + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + +def main(): + for fpath in FILES: process_file(fpath) From 1a51b8eb89843d7d9430d6f7210f27e237da93e2 Mon Sep 17 00:00:00 2001 From: Odin-ntnu Date: Tue, 24 Mar 2026 19:45:07 +0100 Subject: [PATCH 13/28] Added index file for dashboard --- .vscode/settings.json | 3 + wearable/Front/Index.html | 511 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 514 insertions(+) create mode 100644 .vscode/settings.json create mode 100644 wearable/Front/Index.html diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..6f3a291 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "liveServer.settings.port": 5501 +} \ No newline at end of file diff --git a/wearable/Front/Index.html b/wearable/Front/Index.html new file mode 100644 index 0000000..c77a969 --- /dev/null +++ b/wearable/Front/Index.html @@ -0,0 +1,511 @@ + + + + + +GymBro AI — Rep Tracker + + + + +
+ +
+
+ GymBro AI Logo +
+

GYMBROAI

+

Rep Tracker

+
+
+
+ +
+
+ +
+ +
+
+
+
+ + + +
+
+ +
+ + + + From 1c34b47759ad75f28b627b0afae6e9993d8f0118 Mon Sep 17 00:00:00 2001 From: GustavNat Date: Tue, 7 Apr 2026 18:57:26 +0200 Subject: [PATCH 14/28] working version of rep_counter implemented in python and rep_counter for arduino --- wearable/firmware/rep_counter.ino | 2 + wearable/python/rep_counter_variance.py | 292 ++++++++++++++++++++++++ 2 files changed, 294 insertions(+) create mode 100644 wearable/firmware/rep_counter.ino create mode 100644 wearable/python/rep_counter_variance.py diff --git a/wearable/firmware/rep_counter.ino b/wearable/firmware/rep_counter.ino new file mode 100644 index 0000000..e730564 --- /dev/null +++ b/wearable/firmware/rep_counter.ino @@ -0,0 +1,2 @@ +#include +#include \ No newline at end of file diff --git a/wearable/python/rep_counter_variance.py b/wearable/python/rep_counter_variance.py new file mode 100644 index 0000000..8c13a08 --- /dev/null +++ b/wearable/python/rep_counter_variance.py @@ -0,0 +1,292 @@ +""" +rep_counter_variance.py — counts exercise reps using a dual-threshold approach. + +Threshold 1 (rolling stats): + mean + 1 standard deviation of the last 100 signal samples at the time of + each candidate peak. If fewer than 100 samples have been seen, all samples + so far are used. + +Threshold 2 (rep history): + mean amplitude of the two most recent valid reps. + A seed value (SEED_THRESHOLD_FACTOR × max candidate peak) is used until + two valid reps have been accumulated. + +A peak is counted as a rep only when it exceeds BOTH thresholds. + +The accel axis with the highest variance is selected automatically. +An interactive menu lets you pick any CSV file found under data/. +""" + +import os +import glob +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from scipy.signal import butter, filtfilt, find_peaks + +# --------------------------------------------------------------------------- +# Configuration +# --------------------------------------------------------------------------- + +DATA_DIR = os.path.join(os.path.dirname(__file__), "data") + +COLUMN_NAMES = ["time", "ax", "ay", "az", "gx", "gy", "gz", "label", "person"] + +ACCEL_COLS = ["ax", "ay", "az"] + +SAMPLE_RATE = 50 # Hz +FILTER_CUTOFF_HZ = 20.0 +FILTER_ORDER = 2 + +# Minimum distance between candidate peaks (samples) +MIN_PEAK_DISTANCE_SAMPLES = 20 + +# Rolling-stats window (Threshold 1) +ROLLING_WINDOW = 100 # samples + +# Rep-history threshold factor (Threshold 2) +# T2 = REP_THRESHOLD_FACTOR × mean(last two valid peak amplitudes) +REP_THRESHOLD_FACTOR = 0.8 + +# Seed for T2 before two valid reps have been found +# seed = SEED_THRESHOLD_FACTOR × max(candidate peak amplitudes) +SEED_THRESHOLD_FACTOR = 0.3 + + +# --------------------------------------------------------------------------- +# File discovery & interactive selection +# --------------------------------------------------------------------------- + +def discover_files(): + """Recursively find all CSV files under DATA_DIR, sorted by path.""" + paths = glob.glob(os.path.join(DATA_DIR, "**", "*.csv"), recursive=True) + return sorted(set(paths)) + + +def select_file(): + """Print a numbered menu and return the path chosen by the user.""" + files = discover_files() + if not files: + raise FileNotFoundError(f"No CSV files found under {DATA_DIR}") + + print("\nAvailable data files:") + print("-" * 55) + for i, path in enumerate(files): + rel = os.path.relpath(path, DATA_DIR) + size = os.path.getsize(path) + size_str = f"{size // 1024} KB" if size >= 1024 else f"{size} B" + print(f" [{i + 1:2d}] {rel:<40} {size_str}") + print("-" * 55) + + while True: + try: + choice = int(input(f"Select file [1–{len(files)}]: ")) + if 1 <= choice <= len(files): + return files[choice - 1] + print(f" Please enter a number between 1 and {len(files)}.") + except ValueError: + print(" Invalid input — please enter a number.") + + +# --------------------------------------------------------------------------- +# CSV loading +# --------------------------------------------------------------------------- + +def load_csv(filepath): + """Load CSV; fall back to COLUMN_NAMES if the file has no header row.""" + df = pd.read_csv(filepath) + if str(df.columns[0]).lstrip("-").replace(".", "", 1).isdigit(): + df = pd.read_csv(filepath, header=None, + names=COLUMN_NAMES[: len(df.columns)]) + missing = [c for c in ACCEL_COLS if c not in df.columns] + if missing: + raise ValueError( + f"Missing expected columns {missing}. " + f"File has: {list(df.columns)}" + ) + return df + + +# --------------------------------------------------------------------------- +# Signal processing +# --------------------------------------------------------------------------- + +def best_accel_axis(df): + """Return (column_name, raw_numpy_array) for the highest-variance accel axis.""" + variances = {c: df[c].var() for c in ACCEL_COLS} + var_str = ", ".join(f"{c}: {v:.3f}" for c, v in variances.items()) + print(f" Accel variances — {var_str}") + best = max(variances, key=variances.get) + print(f" Selected axis — {best}") + return best, df[best].to_numpy(dtype=float) + + +def lowpass(signal, cutoff_hz=FILTER_CUTOFF_HZ, fs=SAMPLE_RATE, order=FILTER_ORDER): + """Zero-phase Butterworth low-pass filter.""" + nyq = 0.5 * fs + b, a = butter(order, cutoff_hz / nyq, btype="low", analog=False) + return filtfilt(b, a, signal) + + +# --------------------------------------------------------------------------- +# Dual-threshold repetition counting +# --------------------------------------------------------------------------- + +def count_reps_dual_threshold(filtered_signal): + """ + Detect peaks and count reps using two simultaneous thresholds. + + Threshold 1 — rolling stats: + T1(i) = mean + std of filtered_signal[max(0, i-ROLLING_WINDOW+1) : i+1] + Adapts to the recent signal level and noise floor. + + Threshold 2 — rep history: + T2 = REP_THRESHOLD_FACTOR × mean(amplitudes of last two valid reps). + Seed value used until two valid reps have been found. + + A peak counts as a rep only when amplitude > T1 AND amplitude > T2. + + Returns + ------- + rep_indices : list[int] sample indices of counted reps + t1_at_peaks : list[float] T1 value at each candidate peak (for plotting) + t2_at_peaks : list[float] T2 value at each candidate peak (for plotting) + candidate_idx : ndarray indices of all distance-filtered candidate peaks + """ + candidate_idx, _ = find_peaks(filtered_signal, + distance=MIN_PEAK_DISTANCE_SAMPLES) + if len(candidate_idx) == 0: + return [], [], [], candidate_idx + + # Seed T2 relative to the tallest candidate peak so it scales with magnitude + seed_t2 = SEED_THRESHOLD_FACTOR * filtered_signal[candidate_idx].max() + + rep_indices = [] + last_two_amps = [] # rolling buffer for T2 + t2 = seed_t2 + t1_at_peaks = [] + t2_at_peaks = [] + + for idx in candidate_idx: + # --- Threshold 1: rolling mean + 1 std --- + window_start = max(0, idx - ROLLING_WINDOW + 1) + window = filtered_signal[window_start: idx + 1] + t1 = window.mean() + window.std() + + t1_at_peaks.append(t1) + t2_at_peaks.append(t2) + + amp = filtered_signal[idx] + if amp > t1 and amp > t2: + rep_indices.append(idx) + + # Update T2 buffer + last_two_amps.append(amp) + if len(last_two_amps) > 2: + last_two_amps.pop(0) + if len(last_two_amps) == 2: + t2 = REP_THRESHOLD_FACTOR * np.mean(last_two_amps) + + return rep_indices, t1_at_peaks, t2_at_peaks, candidate_idx + + +# --------------------------------------------------------------------------- +# Plotting +# --------------------------------------------------------------------------- + +def plot_results(raw_signal, filtered_signal, accel_col, + candidate_idx, rep_indices, + t1_at_peaks, t2_at_peaks, + title): + """ + Two-panel figure: + Top — all raw accel axes (context) + Bottom — selected axis: raw, filtered, thresholds, and counted reps + """ + samples = np.arange(len(raw_signal)) + + fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), sharex=True) + fig.suptitle(f"{title} — {len(rep_indices)} reps counted", fontsize=13) + + # ── Top panel: raw selected axis ──────────────────────────────────────── + ax1.plot(samples, raw_signal, color="steelblue", alpha=0.5, + linewidth=1, label=f"{accel_col} raw") + ax1.plot(samples, filtered_signal, color="steelblue", linewidth=1.8, + label=f"{accel_col} filtered") + ax1.set_ylabel("Acceleration") + ax1.set_title(f"Selected axis: {accel_col} (highest variance)") + ax1.legend(loc="upper right", fontsize=8) + ax1.grid(True, alpha=0.3) + + # ── Bottom panel: thresholds + rep markers ────────────────────────────── + ax2.plot(samples, filtered_signal, color="steelblue", linewidth=1.5, + label="filtered signal") + + # Scatter thresholds at candidate peak positions + if len(candidate_idx): + ax2.scatter(candidate_idx, t1_at_peaks, color="orange", s=20, + zorder=4, marker="^", label="T1: rolling mean+std") + ax2.scatter(candidate_idx, t2_at_peaks, color="purple", s=20, + zorder=4, marker="v", label="T2: rep-history mean") + # Candidate peaks (not counted) + rejected = [idx for idx in candidate_idx if idx not in rep_indices] + if rejected: + ax2.scatter(rejected, filtered_signal[rejected], + color="grey", s=30, zorder=4, label="rejected candidate") + + # Counted reps + if rep_indices: + ax2.scatter(rep_indices, filtered_signal[rep_indices], + color="red", s=120, zorder=5, marker="*", + label=f"counted rep ({len(rep_indices)})") + + ax2.set_xlabel("Sample") + ax2.set_ylabel("Acceleration") + ax2.set_title("Dual-threshold rep detection") + ax2.legend(loc="upper right", fontsize=8) + ax2.grid(True, alpha=0.3) + + plt.tight_layout() + plt.show() + + +# --------------------------------------------------------------------------- +# Per-file pipeline +# --------------------------------------------------------------------------- + +def process(filepath): + print(f"\nProcessing: {os.path.relpath(filepath, DATA_DIR)}") + + df = load_csv(filepath) + accel_col, raw_signal = best_accel_axis(df) + + filtered_signal = lowpass(raw_signal) + + rep_indices, t1_at_peaks, t2_at_peaks, candidate_idx = \ + count_reps_dual_threshold(filtered_signal) + + print(f" Candidate peaks : {len(candidate_idx)}") + print(f" Reps counted : {len(rep_indices)}") + + title = os.path.relpath(filepath, DATA_DIR) + plot_results(raw_signal, filtered_signal, accel_col, + candidate_idx, rep_indices, + t1_at_peaks, t2_at_peaks, + title) + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + +def main(): + while True: + filepath = select_file() + process(filepath) + again = input("\nProcess another file? [y/N]: ").strip().lower() + if again != "y": + break + + +if __name__ == "__main__": + main() From c8279d2bc602fcbebaf8985f3d43e3badb8a5d20 Mon Sep 17 00:00:00 2001 From: denjev03 Date: Thu, 9 Apr 2026 17:32:59 +0200 Subject: [PATCH 15/28] feat: added some shi --- wearable/firmware/exercise_classifier.ino | 213 +++++++++++++++++++++ wearable/python/IMU_stream.py | 61 ++++++ wearable/python/bleak_test.py | 27 --- wearable/python/data_visualization.py | 8 - wearable/python/exercise_clasification.py | 35 ---- wearable/python/exercise_classification.py | 43 +++++ 6 files changed, 317 insertions(+), 70 deletions(-) create mode 100644 wearable/firmware/exercise_classifier.ino create mode 100644 wearable/python/IMU_stream.py delete mode 100644 wearable/python/bleak_test.py delete mode 100644 wearable/python/data_visualization.py delete mode 100644 wearable/python/exercise_clasification.py create mode 100644 wearable/python/exercise_classification.py diff --git a/wearable/firmware/exercise_classifier.ino b/wearable/firmware/exercise_classifier.ino new file mode 100644 index 0000000..badf48f --- /dev/null +++ b/wearable/firmware/exercise_classifier.ino @@ -0,0 +1,213 @@ +#include +#include "Arduino_BMI270_BMM150.h" + + +#include +#include +#include +#include +#include + +#include "model.h" + +const float accelerationThreshold = 2.5; // threshold of significant in G's +const int numSamples = 119; + +int samplesRead = numSamples; + +//Setting up BLE +BLEService classService("12345678-1234-1234-1234-1234567890ab"); + +BLECharacteristic gestureChar("12345678-1234-1234-1234-1234567890ac", + BLERead | BLENotify, 24); + +// pull in all the TFLM ops, you can remove this line and +// only pull in the TFLM ops you need, if would like to reduce +// the compiled size of the sketch. +tflite::AllOpsResolver tflOpsResolver; + +const tflite::Model* tflModel = nullptr; +tflite::MicroInterpreter* tflInterpreter = nullptr; +TfLiteTensor* tflInputTensor = nullptr; +TfLiteTensor* tflOutputTensor = nullptr; + +// Create a static memory buffer for TFLM, the size may need to +// be adjusted based on the model you are using +constexpr int tensorArenaSize = 48 * 1024; +byte tensorArena[tensorArenaSize] __attribute__((aligned(16))); + +static uint8_t interpreterBuffer[sizeof(tflite::MicroInterpreter)] __attribute__((aligned(16))); + +// array to map gesture index to a name +const char* GESTURES[] = { + "biceps-curl", + "shoulder-pres" +}; + +#define NUM_GESTURES (sizeof(GESTURES) / sizeof(GESTURES[0])) + + +void setup() { + Serial.begin(9600); + while(!Serial); + Serial.println("Start"); + Serial.print("Number of exercises: "); + Serial.println(NUM_GESTURES); + + + + + // initialize the IMU + if (!IMU.begin()) { + Serial.println("Failed to initialize IMU!"); + while (1); + } + + if (!BLE.begin()) { + Serial.println("Failed to initialize BLE"); + while (1); + } + + Serial.println("BLE OK"); + + // print out the samples rates of the IMUs + Serial.print("Accelerometer sample rate = "); + Serial.print(IMU.accelerationSampleRate()); + Serial.println(" Hz"); + Serial.print("Gyroscope sample rate = "); + Serial.print(IMU.gyroscopeSampleRate()); + Serial.println(" Hz"); + + Serial.println(); + + //Start advertising + BLE.setLocalName("CogitoIMU"); + BLE.setAdvertisedService(classService); + + classService.addCharacteristic(gestureChar); + BLE.addService(classService); + + BLE.advertise(); + Serial.println("Advertising.."); + + // get the TFL representation of the model byte array + tflModel = tflite::GetModel(model); + + + // Create an interpreter to run the model + //tflInterpreter = new tflite::MicroInterpreter(tflModel, tflOpsResolver, tensorArena, tensorArenaSize); + tflInterpreter = new(interpreterBuffer) tflite::MicroInterpreter( + tflModel, tflOpsResolver, tensorArena, tensorArenaSize); + + // Allocate memory for the model's input and output tensors + //tflInterpreter->AllocateTensors(); + + if (tflInterpreter->AllocateTensors() != kTfLiteOk) { + Serial.println("AllocateTensors() failed!"); + while (1); + } + + Serial.print("Arena used bytes: "); + Serial.println(tflInterpreter->arena_used_bytes()); + Serial.print("Arena total bytes: "); + Serial.println(tensorArenaSize); + + // Get pointers for the model's input and output tensors + tflInputTensor = tflInterpreter->input(0); + tflOutputTensor = tflInterpreter->output(0); + + Serial.print("Output size: "); + Serial.println(tflOutputTensor->bytes); +} + +void loop() { + BLEDevice central = BLE.central(); + + if (central) { + Serial.println("Connected to central"); + + while(central.connected()) { + + + + float aX, aY, aZ, gX, gY, gZ; + + // wait for significant motion + while (samplesRead == numSamples) { + BLE.poll(); + if (IMU.accelerationAvailable()) { + // read the acceleration data + IMU.readAcceleration(aX, aY, aZ); + + // sum up the absolutes + float aSum = fabs(aX) + fabs(aY) + fabs(aZ); + //Serial.print("aSUM: "); + //Serial.println(aSum); + // check if it's above the threshold + if (aSum >= accelerationThreshold) { + // reset the sample read count + samplesRead = 0; + break; + } + } + } + + // check if the all the required samples have been read since + // the last time the significant motion was detected + while (samplesRead < numSamples) { + BLE.poll(); + // check if new acceleration AND gyroscope data is available + if (IMU.accelerationAvailable() && IMU.gyroscopeAvailable()) { + // read the acceleration and gyroscope data + IMU.readAcceleration(aX, aY, aZ); + IMU.readGyroscope(gX, gY, gZ); + + // normalize the IMU data between 0 to 1 and store in the model's + // input tensor + tflInputTensor->data.f[samplesRead * 6 + 0] = (aX + 4.0) / 8.0; + tflInputTensor->data.f[samplesRead * 6 + 1] = (aY + 4.0) / 8.0; + tflInputTensor->data.f[samplesRead * 6 + 2] = (aZ + 4.0) / 8.0; + tflInputTensor->data.f[samplesRead * 6 + 3] = (gX + 2000.0) / 4000.0; + tflInputTensor->data.f[samplesRead * 6 + 4] = (gY + 2000.0) / 4000.0; + tflInputTensor->data.f[samplesRead * 6 + 5] = (gZ + 2000.0) / 4000.0; + + samplesRead++; + + if (samplesRead == numSamples) { + Serial.println("Running inferencing"); + // Run inferencing + TfLiteStatus invokeStatus = tflInterpreter->Invoke(); + if (invokeStatus != kTfLiteOk) { + Serial.println("Invoke failed!"); + while (1); + return; + } + + + + int maxIndex = 0; + float maxValue = tflOutputTensor->data.f[0]; + for (int i = 1; i < NUM_GESTURES; i++){ + if (tflOutputTensor->data.f[i] > maxValue){ + maxValue = tflOutputTensor->data.f[i]; + maxIndex = i; + } + } + Serial.print("Gesture: "); + Serial.print(GESTURES[maxIndex]); + Serial.print(" ("); + Serial.print(maxValue * 100, 1); + Serial.println("% confidence)"); + + + char buffer[32]; + snprintf(buffer, sizeof(buffer), "%s|%.1f", GESTURES[maxIndex], maxValue * 100); + + gestureChar.writeValue(buffer); + } + } + } + } + } +} + diff --git a/wearable/python/IMU_stream.py b/wearable/python/IMU_stream.py new file mode 100644 index 0000000..fc45bfe --- /dev/null +++ b/wearable/python/IMU_stream.py @@ -0,0 +1,61 @@ +import asyncio +import struct +import csv +import os +from dotenv import load_dotenv +from bleak import BleakClient, BleakScanner + +load_dotenv() +DEVICE_NAME = "CogitoIMU" +CHAR_UUID = os.getenv("UUID") + +person = "dj" +exercise = "squat" + +folder = "data0\\" +os.makedirs(folder, exist_ok=True) # lager mappen hvis den ikke finnes + +filename = f"{exercise}_{person}.csv" +filepath = os.path.join(folder, filename) + +file = open(filepath, "a", newline="") +writer = csv.writer(file) + + +def notification_handler(sender, data): + values = struct.unpack("ffffff", data) + row = list(values) + writer.writerow(row) + print(row) + + +async def main(): + # Scan for the device + print("Scanning for devices...") + device = await BleakScanner.find_device_by_name(DEVICE_NAME, timeout=10.0) + if device is None: + print(f"Device '{DEVICE_NAME}' not found.") + return + + async with BleakClient(device) as client: + print(f"Connected to {DEVICE_NAME}") + + csv.writer(file).writerow(["aX", "aY", "aZ", "gX", "gY", "gZ"]) + + await client.start_notify(CHAR_UUID, notification_handler) + + print("Recording... Press Ctrl+C to stop.") + + try: + while True: + await asyncio.sleep(1) + except KeyboardInterrupt: + print("\nStopping recording...") + + await client.stop_notify(CHAR_UUID) + + file.close() + print("File saved.") + + +asyncio.run(main()) diff --git a/wearable/python/bleak_test.py b/wearable/python/bleak_test.py deleted file mode 100644 index 1dd1df0..0000000 --- a/wearable/python/bleak_test.py +++ /dev/null @@ -1,27 +0,0 @@ -import asyncio -from bleak import BleakScanner, BleakClient - -DEVICE_NAME = "CogitoIMU" - - -async def main(): - print("Scanning...") - devices = await BleakScanner.discover() - - device = None - for d in devices: - print(d) - if d.name == DEVICE_NAME: - device = d - - if device is None: - print("Device not found") - return - - print("Connecting to", device) - - async with BleakClient(device) as client: - print("Connected:", client.is_connected) - - -asyncio.run(main()) diff --git a/wearable/python/data_visualization.py b/wearable/python/data_visualization.py deleted file mode 100644 index 7cc29a1..0000000 --- a/wearable/python/data_visualization.py +++ /dev/null @@ -1,8 +0,0 @@ -import pandas as pd -from pathlib import Path - -data_path = Path("./data/shoulder_press/shoulder_press_dennis_1.csv") -df = pd.read_csv(data_path) -print(df.head()) - -df.to diff --git a/wearable/python/exercise_clasification.py b/wearable/python/exercise_clasification.py deleted file mode 100644 index 02e42a6..0000000 --- a/wearable/python/exercise_clasification.py +++ /dev/null @@ -1,35 +0,0 @@ -import asyncio -import os -from dotenv import load_dotenv -from bleak import BleakClient - -load_dotenv() -address = os.getenv("MAC_ADRESS") -char_uuid = os.getenv("UUID2") - -ADDRESS = address -CHAR_UUID = char_uuid - - -def notification_handler(sender, data): - print(f"Notification from {sender}: {data}") - - -async def main(): - async with BleakClient(ADDRESS) as client: - print("Connected:", client.is_connected) - - await client.start_notify(CHAR_UUID, notification_handler) - - print("Recording... Press Ctrl+C to stop.") - - try: - while True: - await asyncio.sleep(1) - except KeyboardInterrupt: - print("\nStopping recording...") - - await client.stop_notify(CHAR_UUID) - - -asyncio.run(main()) diff --git a/wearable/python/exercise_classification.py b/wearable/python/exercise_classification.py new file mode 100644 index 0000000..53fe72b --- /dev/null +++ b/wearable/python/exercise_classification.py @@ -0,0 +1,43 @@ +import asyncio +from bleak import BleakScanner, BleakClient +from dotenv import load_dotenv +import os + +load_dotenv() +DEVICE_NAME = "CogitoIMU" +GEST_UUID = os.getenv("UUID2") + + +def gest_callback(sender, data): + data = data.decode() + gesture, confidence = data.split("|") + print(f"Gesture: {gesture}, Confidence: {confidence}") + + +async def main(): + # Scan for the device + print("Scanning for devices...") + device = await BleakScanner.find_device_by_name(DEVICE_NAME, timeout=10.0) + if device is None: + print(f"Device '{DEVICE_NAME}' not found.") + return + + # Connect to the device + async with BleakClient(device) as client: + print(f"Connected to {DEVICE_NAME}") + + # Start notifications for gesture data + await client.start_notify(GEST_UUID, gest_callback) + + print("Listening for data... Press Ctrl+C to stop.") + try: + while True: + await asyncio.sleep(1) + except KeyboardInterrupt: + print("Stopping notifications...") + + # Stop notifications + await client.stop_notify(GEST_UUID) + + +asyncio.run(main()) From 5111096985162356a0d90bb717baf3f30fca10e2 Mon Sep 17 00:00:00 2001 From: denjev03 Date: Thu, 9 Apr 2026 18:20:15 +0200 Subject: [PATCH 16/28] feat: added counter --- .../exercise_classification.py | 15 + wearable/firmware/model.h | 12353 +++++++++++++++ wearable/firmware/model3.h | 12358 ++++++++++++++++ 3 files changed, 24726 insertions(+) rename wearable/{python => Front}/exercise_classification.py (66%) create mode 100644 wearable/firmware/model.h create mode 100644 wearable/firmware/model3.h diff --git a/wearable/python/exercise_classification.py b/wearable/Front/exercise_classification.py similarity index 66% rename from wearable/python/exercise_classification.py rename to wearable/Front/exercise_classification.py index 53fe72b..3d897c4 100644 --- a/wearable/python/exercise_classification.py +++ b/wearable/Front/exercise_classification.py @@ -7,11 +7,26 @@ DEVICE_NAME = "CogitoIMU" GEST_UUID = os.getenv("UUID2") +bicep_curl_counter = 0 +shoulder_press_counter = 0 + def gest_callback(sender, data): data = data.decode() gesture, confidence = data.split("|") print(f"Gesture: {gesture}, Confidence: {confidence}") + if gesture == "biceps-curl" and float(confidence) > 0.8: + global bicep_curl_counter + bicep_curl_counter += 1 + print(f"Bicep Curl Count: {bicep_curl_counter}") + elif gesture == "shoulder-press" and float(confidence) > 0.8: + global shoulder_press_counter + shoulder_press_counter += 1 + print(f"Shoulder Press Count: {shoulder_press_counter}") + + print( + f"Current Counts - Bicep Curls: {bicep_curl_counter}, Shoulder Presses: {shoulder_press_counter}" + ) async def main(): diff --git a/wearable/firmware/model.h b/wearable/firmware/model.h new file mode 100644 index 0000000..efa0995 --- /dev/null +++ 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b/wearable/firmware/model3.h new file mode 100644 index 0000000..ee8dc98 --- /dev/null +++ b/wearable/firmware/model3.h @@ -0,0 +1,12358 @@ +const unsigned char model[] = { + 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00, + 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, + 0x08, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, + 0x98, 0x00, 0x00, 0x00, 0xf0, 0x00, 0x00, 0x00, 0x5c, 0x3d, 0x02, 0x00, + 0x6c, 0x3d, 0x02, 0x00, 0xc8, 0x42, 0x02, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0a, 0x00, + 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, + 0x0c, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67, 0x5f, + 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x04, 0x00, 0x00, 0x00, 0x90, 0xff, 0xff, 0xff, 0x0a, 0x00, 0x00, 0x00, + 0x04, 0x00, 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0x6f, 0x72, 0x3a, 0x30, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0xca, 0x02, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xf4, 0xff, 0xff, 0xff, + 0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0x0c, 0x00, 0x0c, 0x00, + 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, + 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09 +}; From 8c45c8ed3b0ea4abce89027e11f770fd151c14c1 Mon Sep 17 00:00:00 2001 From: Odin-ntnu Date: Thu, 9 Apr 2026 18:43:57 +0200 Subject: [PATCH 17/28] mordi --- wearable/ferdig front/Index.html | 576 ++++++++++++++++++ wearable/ferdig front/counts.json | 4 + wearable/ferdig front/dashboard_sync.py | 41 ++ .../exercise_classification.py | 3 + 4 files changed, 624 insertions(+) create mode 100644 wearable/ferdig front/Index.html create mode 100644 wearable/ferdig front/counts.json create mode 100644 wearable/ferdig front/dashboard_sync.py rename wearable/{Front => ferdig front}/exercise_classification.py (90%) diff --git a/wearable/ferdig front/Index.html b/wearable/ferdig front/Index.html new file mode 100644 index 0000000..c45ffad --- /dev/null +++ b/wearable/ferdig front/Index.html @@ -0,0 +1,576 @@ + + + + + +GymBro AI — Rep Tracker + + + + +
+ +
+
+ GymBro AI Logo +
+

GYMBROAI

+

Rep Tracker

+
+
+
+ +
+
+ +
+ +
+
+
+
+ + + +
+
+ +
+ + + + diff --git a/wearable/ferdig front/counts.json b/wearable/ferdig front/counts.json new file mode 100644 index 0000000..890537a --- /dev/null +++ b/wearable/ferdig front/counts.json @@ -0,0 +1,4 @@ +{ + "bicep_curl_counter": 0, + "shoulder_press_counter": 0 +} diff --git a/wearable/ferdig front/dashboard_sync.py b/wearable/ferdig front/dashboard_sync.py new file mode 100644 index 0000000..8c45ec8 --- /dev/null +++ b/wearable/ferdig front/dashboard_sync.py @@ -0,0 +1,41 @@ +""" +Add this to BOTH your counter files. +Place counts.json in the same folder as Index.html. +""" + +import json +import os + +COUNTS_FILE = os.path.join(os.path.dirname(__file__), 'counts.json') + +def update_count(exercise_key: str, count: int): + """Write the current rep count to counts.json so the dashboard can read it.""" + try: + # Read existing counts + if os.path.exists(COUNTS_FILE): + with open(COUNTS_FILE, 'r') as f: + data = json.load(f) + else: + data = {} + + # Update this exercise + data[exercise_key] = count + + # Write back atomically + with open(COUNTS_FILE, 'w') as f: + json.dump(data, f) + except Exception as e: + print(f"[dashboard sync] Could not write counts.json: {e}") + + +# ───────────────────────────────────────────────────────────────── +# In bicep_curl_counter.py — call this wherever you increment reps: +# +# bicep_count += 1 +# update_count('bicep_curl_counter', bicep_count) +# +# In shoulder_press_counter.py — call this wherever you increment reps: +# +# shoulder_count += 1 +# update_count('shoulder_press_counter', shoulder_count) +# ───────────────────────────────────────────────────────────────── diff --git a/wearable/Front/exercise_classification.py b/wearable/ferdig front/exercise_classification.py similarity index 90% rename from wearable/Front/exercise_classification.py rename to wearable/ferdig front/exercise_classification.py index 3d897c4..d5e77cc 100644 --- a/wearable/Front/exercise_classification.py +++ b/wearable/ferdig front/exercise_classification.py @@ -2,6 +2,7 @@ from bleak import BleakScanner, BleakClient from dotenv import load_dotenv import os +from dashboard_sync import update_count load_dotenv() DEVICE_NAME = "CogitoIMU" @@ -18,10 +19,12 @@ def gest_callback(sender, data): if gesture == "biceps-curl" and float(confidence) > 0.8: global bicep_curl_counter bicep_curl_counter += 1 + update_count('bicep_curl_counter', bicep_curl_counter) print(f"Bicep Curl Count: {bicep_curl_counter}") elif gesture == "shoulder-press" and float(confidence) > 0.8: global shoulder_press_counter shoulder_press_counter += 1 + update_count('shoulder_press_counter', shoulder_press_counter) print(f"Shoulder Press Count: {shoulder_press_counter}") print( From 3b8514ad18dc2a6c11e2c9161c85fe75c0e5e528 Mon Sep 17 00:00:00 2001 From: denjev03 Date: Tue, 14 Apr 2026 17:27:04 +0200 Subject: [PATCH 18/28] feat: added ino file --- wearable/Front/Index.html | 511 ------------------ wearable/ferdig front/counts.json | 5 +- .../ferdig front/exercise_classification.py | 36 +- wearable/firmware/classifier.ino | 171 ++++++ ...cise_classifier.ino => classifier_BLE.ino} | 33 +- wearable/firmware/classifier_BLEv2.ino | 229 ++++++++ 6 files changed, 446 insertions(+), 539 deletions(-) delete mode 100644 wearable/Front/Index.html create mode 100644 wearable/firmware/classifier.ino rename wearable/firmware/{exercise_classifier.ino => classifier_BLE.ino} (91%) create mode 100644 wearable/firmware/classifier_BLEv2.ino diff --git a/wearable/Front/Index.html b/wearable/Front/Index.html deleted file mode 100644 index c77a969..0000000 --- a/wearable/Front/Index.html +++ /dev/null @@ -1,511 +0,0 @@ - - - - - -GymBro AI — Rep Tracker - - - - -
- -
-
- GymBro AI Logo -
-

GYMBROAI

-

Rep Tracker

-
-
-
- -
-
- -
- -
-
-
-
- - - -
-
- -
- - - - diff --git a/wearable/ferdig front/counts.json b/wearable/ferdig front/counts.json index 890537a..4382dd0 100644 --- a/wearable/ferdig front/counts.json +++ b/wearable/ferdig front/counts.json @@ -1,4 +1 @@ -{ - "bicep_curl_counter": 0, - "shoulder_press_counter": 0 -} +{"bicep_curl_counter": 2, "shoulder_press_counter": 5, "rows_counter": 3, "squats_counter": 5} \ No newline at end of file diff --git a/wearable/ferdig front/exercise_classification.py b/wearable/ferdig front/exercise_classification.py index d5e77cc..77100ad 100644 --- a/wearable/ferdig front/exercise_classification.py +++ b/wearable/ferdig front/exercise_classification.py @@ -1,34 +1,52 @@ import asyncio from bleak import BleakScanner, BleakClient -from dotenv import load_dotenv -import os from dashboard_sync import update_count -load_dotenv() DEVICE_NAME = "CogitoIMU" -GEST_UUID = os.getenv("UUID2") +GEST_UUID = "db6d5260-ae3e-4421-a65c-73ca64cc7d3b" bicep_curl_counter = 0 shoulder_press_counter = 0 +rows_counter = 0 +squats_counter = 0 +triceps_extension_counter = 0 def gest_callback(sender, data): data = data.decode() gesture, confidence = data.split("|") print(f"Gesture: {gesture}, Confidence: {confidence}") - if gesture == "biceps-curl" and float(confidence) > 0.8: + if gesture == "bicep_curl" and float(confidence) > 0.8: global bicep_curl_counter bicep_curl_counter += 1 - update_count('bicep_curl_counter', bicep_curl_counter) + update_count("bicep_curl_counter", bicep_curl_counter) print(f"Bicep Curl Count: {bicep_curl_counter}") - elif gesture == "shoulder-press" and float(confidence) > 0.8: + elif gesture == "shoulder_press" and float(confidence) > 0.8: global shoulder_press_counter shoulder_press_counter += 1 - update_count('shoulder_press_counter', shoulder_press_counter) + update_count("shoulder_press_counter", shoulder_press_counter) print(f"Shoulder Press Count: {shoulder_press_counter}") + elif gesture == "rows" and float(confidence) > 0.8: + global rows_counter + rows_counter += 1 + update_count("rows_counter", rows_counter) + print(f"Rows Count: {rows_counter}") + + elif gesture == "squat" and float(confidence) > 0.8: + global squats_counter + squats_counter += 1 + update_count("squats_counter", squats_counter) + print(f"Squats Count: {squats_counter}") + + elif gesture == "tricep_extension" and float(confidence) > 0.8: + global triceps_extension_counter + triceps_extension_counter += 1 + update_count("triceps_extension_counter", triceps_extension_counter) + print(f"Triceps Extension Count: {triceps_extension_counter}") + print( - f"Current Counts - Bicep Curls: {bicep_curl_counter}, Shoulder Presses: {shoulder_press_counter}" + f"Current Counts - Bicep Curls: {bicep_curl_counter}, Shoulder Presses: {shoulder_press_counter}, Rows: {rows_counter}, Squats: {squats_counter}, Triceps Extensions: {triceps_extension_counter}" ) diff --git a/wearable/firmware/classifier.ino b/wearable/firmware/classifier.ino new file mode 100644 index 0000000..8041a73 --- /dev/null +++ b/wearable/firmware/classifier.ino @@ -0,0 +1,171 @@ +#include "Arduino_BMI270_BMM150.h" +#include +#include +#include +#include +#include +#include "modelq.h" + +const float accelerationThreshold = 2.5; // threshold of significant in G's +const int numSamples = 119; + +int samplesRead = numSamples; + + +// pull in all the TFLM ops, you can remove this line and +// only pull in the TFLM ops you need, if would like to reduce +// the compiled size of the sketch. +static tflite::MicroMutableOpResolver<4> tflOpsResolver; + +const tflite::Model* tflModel = nullptr; +tflite::MicroInterpreter* tflInterpreter = nullptr; +TfLiteTensor* tflInputTensor = nullptr; +TfLiteTensor* tflOutputTensor = nullptr; + +// Create a static memory buffer for TFLM, the size may need to +// be adjusted based on the model you are using +constexpr int tensorArenaSize = 16 * 1024; +byte tensorArena[tensorArenaSize] __attribute__((aligned(16))); + +static uint8_t interpreterBuffer[sizeof(tflite::MicroInterpreter)] __attribute__((aligned(16))); + +// array to map gesture index to a name +const char* GESTURES[] = { + "biceps-curl", + "shoulder-pres", + "row" +}; + +#define NUM_GESTURES (sizeof(GESTURES) / sizeof(GESTURES[0])) + + +void setup() { + Serial.begin(9600); + while(!Serial); + Serial.println("Start"); + Serial.print("Number of exercises: "); + Serial.println(NUM_GESTURES); + + + // initialize the IMU + if (!IMU.begin()) { + Serial.println("Failed to initialize IMU!"); + while (1); + } + + // print out the samples rates of the IMUs + Serial.print("Accelerometer sample rate = "); + Serial.print(IMU.accelerationSampleRate()); + Serial.println(" Hz"); + Serial.print("Gyroscope sample rate = "); + Serial.print(IMU.gyroscopeSampleRate()); + Serial.println(" Hz"); + + Serial.println(); + + + tflOpsResolver.AddFullyConnected(); + tflOpsResolver.AddRelu(); + tflOpsResolver.AddSoftmax(); + tflOpsResolver.AddReshape(); + + // get the TFL representation of the model byte array + tflModel = tflite::GetModel(model); + + // Create an interpreter to run the model + //tflInterpreter = new tflite::MicroInterpreter(tflModel, tflOpsResolver, tensorArena, tensorArenaSize); + tflInterpreter = new(interpreterBuffer) tflite::MicroInterpreter( + tflModel, tflOpsResolver, tensorArena, tensorArenaSize); + + // Allocate memory for the model's input and output tensors + //tflInterpreter->AllocateTensors(); + + if (tflInterpreter->AllocateTensors() != kTfLiteOk) { + Serial.println("AllocateTensors() failed!"); + while (1); + } + + + Serial.print("Arena used bytes: "); + Serial.println(tflInterpreter->arena_used_bytes()); + Serial.print("Arena total bytes: "); + Serial.println(tensorArenaSize); + + + // Get pointers for the model's input and output tensors + tflInputTensor = tflInterpreter->input(0); + tflOutputTensor = tflInterpreter->output(0); + + Serial.print("Output size: "); + Serial.println(tflOutputTensor->bytes); +} + +void loop() { + + + float aX, aY, aZ, gX, gY, gZ; + + // wait for significant motion + while (samplesRead == numSamples) { + if (IMU.accelerationAvailable()) { + // read the acceleration data + IMU.readAcceleration(aX, aY, aZ); + + // sum up the absolutes + float aSum = fabs(aX) + fabs(aY) + fabs(aZ); + //Serial.print("aSUM: "); + //Serial.println(aSum); + // check if it's above the threshold + if (aSum >= accelerationThreshold) { + // reset the sample read count + samplesRead = 0; + break; + } + } + } + + // check if the all the required samples have been read since + // the last time the significant motion was detected + while (samplesRead < numSamples) { + // check if new acceleration AND gyroscope data is available + if (IMU.accelerationAvailable() && IMU.gyroscopeAvailable()) { + // read the acceleration and gyroscope data + IMU.readAcceleration(aX, aY, aZ); + IMU.readGyroscope(gX, gY, gZ); + + // normalize the IMU data between 0 to 1 and store in the model's + // input tensor + tflInputTensor->data.f[samplesRead * 6 + 0] = (aX + 4.0) / 8.0; + tflInputTensor->data.f[samplesRead * 6 + 1] = (aY + 4.0) / 8.0; + tflInputTensor->data.f[samplesRead * 6 + 2] = (aZ + 4.0) / 8.0; + tflInputTensor->data.f[samplesRead * 6 + 3] = (gX + 2000.0) / 4000.0; + tflInputTensor->data.f[samplesRead * 6 + 4] = (gY + 2000.0) / 4000.0; + tflInputTensor->data.f[samplesRead * 6 + 5] = (gZ + 2000.0) / 4000.0; + + samplesRead++; + + if (samplesRead == numSamples) { + Serial.println("Start invoke"); + // Run inferencing + TfLiteStatus invokeStatus = tflInterpreter->Invoke(); + if (invokeStatus != kTfLiteOk) { + Serial.println("Invoke failed!"); + while (1); + return; + } + + Serial.println("Invoke ended"); + + // Loop through the output tensor values from the model + for (int i = 0; i < NUM_GESTURES; i++) { + Serial.print(GESTURES[i]); + Serial.print(": "); + Serial.println(tflOutputTensor->data.f[i], 6); + } + Serial.println(); + } + } + } + + +} diff --git a/wearable/firmware/exercise_classifier.ino b/wearable/firmware/classifier_BLE.ino similarity index 91% rename from wearable/firmware/exercise_classifier.ino rename to wearable/firmware/classifier_BLE.ino index badf48f..a61b9b0 100644 --- a/wearable/firmware/exercise_classifier.ino +++ b/wearable/firmware/classifier_BLE.ino @@ -1,13 +1,10 @@ #include #include "Arduino_BMI270_BMM150.h" - - #include -#include +#include #include #include #include - #include "model.h" const float accelerationThreshold = 2.5; // threshold of significant in G's @@ -16,15 +13,15 @@ const int numSamples = 119; int samplesRead = numSamples; //Setting up BLE -BLEService classService("12345678-1234-1234-1234-1234567890ab"); +BLEService classService("db6d5260-ae3e-4421-a65c-73ca64cc7d3a"); -BLECharacteristic gestureChar("12345678-1234-1234-1234-1234567890ac", +BLECharacteristic gestureChar("db6d5260-ae3e-4421-a65c-73ca64cc7d3b", BLERead | BLENotify, 24); // pull in all the TFLM ops, you can remove this line and // only pull in the TFLM ops you need, if would like to reduce // the compiled size of the sketch. -tflite::AllOpsResolver tflOpsResolver; +static tflite::MicroMutableOpResolver<4> tflOpsResolver; const tflite::Model* tflModel = nullptr; tflite::MicroInterpreter* tflInterpreter = nullptr; @@ -33,7 +30,7 @@ TfLiteTensor* tflOutputTensor = nullptr; // Create a static memory buffer for TFLM, the size may need to // be adjusted based on the model you are using -constexpr int tensorArenaSize = 48 * 1024; +constexpr int tensorArenaSize = 16 * 1024; byte tensorArena[tensorArenaSize] __attribute__((aligned(16))); static uint8_t interpreterBuffer[sizeof(tflite::MicroInterpreter)] __attribute__((aligned(16))); @@ -41,7 +38,8 @@ static uint8_t interpreterBuffer[sizeof(tflite::MicroInterpreter)] __attribute__ // array to map gesture index to a name const char* GESTURES[] = { "biceps-curl", - "shoulder-pres" + "shoulder-pres", + "tri" }; #define NUM_GESTURES (sizeof(GESTURES) / sizeof(GESTURES[0])) @@ -54,8 +52,6 @@ void setup() { Serial.print("Number of exercises: "); Serial.println(NUM_GESTURES); - - // initialize the IMU if (!IMU.begin()) { @@ -89,11 +85,16 @@ void setup() { BLE.advertise(); Serial.println("Advertising.."); + + + tflOpsResolver.AddFullyConnected(); + tflOpsResolver.AddRelu(); + tflOpsResolver.AddSoftmax(); + tflOpsResolver.AddReshape(); // get the TFL representation of the model byte array tflModel = tflite::GetModel(model); - - + // Create an interpreter to run the model //tflInterpreter = new tflite::MicroInterpreter(tflModel, tflOpsResolver, tensorArena, tensorArenaSize); tflInterpreter = new(interpreterBuffer) tflite::MicroInterpreter( @@ -107,11 +108,13 @@ void setup() { while (1); } + Serial.print("Arena used bytes: "); Serial.println(tflInterpreter->arena_used_bytes()); Serial.print("Arena total bytes: "); Serial.println(tensorArenaSize); + // Get pointers for the model's input and output tensors tflInputTensor = tflInterpreter->input(0); tflOutputTensor = tflInterpreter->output(0); @@ -174,7 +177,7 @@ void loop() { samplesRead++; if (samplesRead == numSamples) { - Serial.println("Running inferencing"); + Serial.println("Start invoke"); // Run inferencing TfLiteStatus invokeStatus = tflInterpreter->Invoke(); if (invokeStatus != kTfLiteOk) { @@ -182,6 +185,7 @@ void loop() { while (1); return; } + Serial.println("Invoke ended"); @@ -210,4 +214,3 @@ void loop() { } } } - diff --git a/wearable/firmware/classifier_BLEv2.ino b/wearable/firmware/classifier_BLEv2.ino new file mode 100644 index 0000000..f679515 --- /dev/null +++ b/wearable/firmware/classifier_BLEv2.ino @@ -0,0 +1,229 @@ +#include +#include "Arduino_BMI270_BMM150.h" +#include +#include +#include +#include +#include + +// ─── Include the int8-quantized model header ──────────────────── +// This header defines: g_model[], g_model_len, kNumSamples, +// kNumFeatures, kNumClasses, kGestureNames[] +#include "model5_int8.h" + +// ─── Configuration ────────────────────────────────────────────── +const float accelerationThreshold = 2.5; // G's to trigger capture +const int numSamples = kNumSamples; // 119 (from model header) + +int samplesRead = numSamples; + +// ─── BLE Setup ────────────────────────────────────────────────── +BLEService classService("db6d5260-ae3e-4421-a65c-73ca64cc7d3a"); +BLECharacteristic gestureChar("db6d5260-ae3e-4421-a65c-73ca64cc7d3b", + BLERead | BLENotify, 32); + +// ─── TFLite Micro Setup ───────────────────────────────────────── +// Op resolver with 6 ops: +// FullyConnected, Relu, Softmax, Reshape — model layers +// Quantize, Dequantize — added by converter for float32 I/O on int8 model +static tflite::MicroMutableOpResolver<6> tflOpsResolver; + +const tflite::Model* tflModel = nullptr; +tflite::MicroInterpreter* tflInterpreter = nullptr; +TfLiteTensor* tflInputTensor = nullptr; +TfLiteTensor* tflOutputTensor = nullptr; + +// Tensor arena — 16KB is plenty for this int8 model +// (int8 uses ~4x less working memory than float32) +constexpr int tensorArenaSize = 16 * 1024; +byte tensorArena[tensorArenaSize] __attribute__((aligned(16))); + +// Placement-new buffer for interpreter (avoids heap fragmentation) +static uint8_t interpreterBuffer[sizeof(tflite::MicroInterpreter)] + __attribute__((aligned(16))); + + +void setup() { + Serial.begin(9600); + //while (!Serial); + + Serial.println("=== Exercise Recognition ==="); + Serial.print("Classes: "); + Serial.println(kNumClasses); + for (int i = 0; i < kNumClasses; i++) { + Serial.print(" "); + Serial.print(i); + Serial.print(": "); + Serial.println(kGestureNames[i]); + } + + // ── Initialize IMU ── + if (!IMU.begin()) { + Serial.println("ERROR: IMU init failed!"); + while (1); + } + Serial.print("Accel rate: "); + Serial.print(IMU.accelerationSampleRate()); + Serial.println(" Hz"); + + // ── Initialize BLE ── + if (!BLE.begin()) { + Serial.println("ERROR: BLE init failed!"); + while (1); + } + BLE.setLocalName("CogitoIMU"); + BLE.setAdvertisedService(classService); + classService.addCharacteristic(gestureChar); + BLE.addService(classService); + BLE.advertise(); + Serial.println("BLE advertising..."); + + // ── Register TFLite ops ── + // These 6 ops cover the full int8-quantized model: + tflOpsResolver.AddFullyConnected(); + tflOpsResolver.AddRelu(); + tflOpsResolver.AddSoftmax(); + tflOpsResolver.AddReshape(); + tflOpsResolver.AddQuantize(); // ← CRITICAL: converts float32 input → int8 + tflOpsResolver.AddDequantize(); // ← CRITICAL: converts int8 output → float32 + + // ── Load model ── + tflModel = tflite::GetModel(g_model); + if (tflModel->version() != TFLITE_SCHEMA_VERSION) { + Serial.print("ERROR: Model schema mismatch! Got "); + Serial.print(tflModel->version()); + Serial.print(", expected "); + Serial.println(TFLITE_SCHEMA_VERSION); + while (1); + } + + // ── Create interpreter ── + tflInterpreter = new (interpreterBuffer) tflite::MicroInterpreter( + tflModel, tflOpsResolver, tensorArena, tensorArenaSize); + + if (tflInterpreter->AllocateTensors() != kTfLiteOk) { + Serial.println("ERROR: AllocateTensors() failed!"); + while (1); + } + + tflInputTensor = tflInterpreter->input(0); + tflOutputTensor = tflInterpreter->output(0); + + // ── Print diagnostics ── + Serial.print("Arena used: "); + Serial.print(tflInterpreter->arena_used_bytes()); + Serial.print(" / "); + Serial.print(tensorArenaSize); + Serial.println(" bytes"); + + Serial.print("Input: type="); + Serial.print(tflInputTensor->type); // 1=float32, 9=int8 + Serial.print(", bytes="); + Serial.println(tflInputTensor->bytes); + + Serial.print("Output: type="); + Serial.print(tflOutputTensor->type); + Serial.print(", bytes="); + Serial.println(tflOutputTensor->bytes); + + Serial.println("Ready — waiting for motion...\n"); +} + + +void loop() { + BLEDevice central = BLE.central(); + + if (central) { + Serial.print("Connected: "); + Serial.println(central.address()); + + while (central.connected()) { + float aX, aY, aZ, gX, gY, gZ; + + // ── Wait for significant motion ── + while (samplesRead == numSamples) { + BLE.poll(); // Keep BLE alive while waiting + if (IMU.accelerationAvailable()) { + IMU.readAcceleration(aX, aY, aZ); + float aSum = fabs(aX) + fabs(aY) + fabs(aZ); + if (aSum >= accelerationThreshold) { + samplesRead = 0; + break; + } + } + } + + // ── Collect samples ── + while (samplesRead < numSamples) { + // NOTE: No BLE.poll() during data collection to avoid + // interrupt-driven corruption of the sample buffer + if (IMU.accelerationAvailable() && IMU.gyroscopeAvailable()) { + IMU.readAcceleration(aX, aY, aZ); + IMU.readGyroscope(gX, gY, gZ); + + // Normalize to [0, 1] — same formula as Python training + int idx = samplesRead * 6; + tflInputTensor->data.f[idx + 0] = (aX + 4.0f) / 8.0f; + tflInputTensor->data.f[idx + 1] = (aY + 4.0f) / 8.0f; + tflInputTensor->data.f[idx + 2] = (aZ + 4.0f) / 8.0f; + tflInputTensor->data.f[idx + 3] = (gX + 2000.0f) / 4000.0f; + tflInputTensor->data.f[idx + 4] = (gY + 2000.0f) / 4000.0f; + tflInputTensor->data.f[idx + 5] = (gZ + 2000.0f) / 4000.0f; + + samplesRead++; + } + } + + // ── Run inference (BLE paused) ── + Serial.println("Invoking..."); + + // CRITICAL: Do NOT call BLE.poll() between filling input and + // reading output. SoftDevice interrupts can corrupt FPU state + // and tensor arena during inference. + TfLiteStatus invokeStatus = tflInterpreter->Invoke(); + + if (invokeStatus != kTfLiteOk) { + Serial.println("ERROR: Invoke failed!"); + samplesRead = numSamples; // Reset for next attempt + continue; + } + + // ── Read results ── + int maxIndex = 0; + float maxValue = tflOutputTensor->data.f[0]; + + // Check for NaN (safety net) + if (isnan(maxValue)) { + Serial.println("WARNING: NaN detected in output — skipping"); + samplesRead = numSamples; + continue; + } + + for (int i = 1; i < kNumClasses; i++) { + float val = tflOutputTensor->data.f[i]; + if (!isnan(val) && val > maxValue) { + maxValue = val; + maxIndex = i; + } + } + + // Print result + Serial.print(">>> "); + Serial.print(kGestureNames[maxIndex]); + Serial.print(" ("); + Serial.print(maxValue * 100.0f, 1); + Serial.println("%)"); + + // ── Send via BLE ── + char buffer[32]; + snprintf(buffer, sizeof(buffer), "%s|%.1f", + kGestureNames[maxIndex], maxValue * 100.0f); + gestureChar.writeValue(buffer); + + // Resume BLE polling + BLE.poll(); + } + + Serial.println("Disconnected"); + } +} From f40653989fff828fbf749448f293af4522d7256c Mon Sep 17 00:00:00 2001 From: denjev03 Date: Tue, 14 Apr 2026 17:57:51 +0200 Subject: [PATCH 19/28] feat: added counters --- wearable/ferdig front/Index.html | 6 +++--- wearable/ferdig front/counts.json | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/wearable/ferdig front/Index.html b/wearable/ferdig front/Index.html index c45ffad..157ae99 100644 --- a/wearable/ferdig front/Index.html +++ b/wearable/ferdig front/Index.html @@ -444,11 +444,11 @@

GYMBROAI<
-
- GymBro AI Logo -
-

GYMBROAI

-

Rep Tracker

-
-
-
- +
+

GYMBROAI

+

Training Log

+
-
- -
-
-
-
- - - +
+ + + +
+ +
+
+
+ + ✓ Workout saved! +
-
+
+
+
+
+ +
+
+
+ + +
+
- + \ No newline at end of file diff --git a/wearable/ferdig front/counts.json b/wearable/ferdig front/counts.json index f99801f..098fb71 100644 --- a/wearable/ferdig front/counts.json +++ b/wearable/ferdig front/counts.json @@ -1 +1 @@ -{"bicep_curl_counter": 1, "shoulder_press_counter": 3, "rows_counter": 3, "squats_counter": 1, "triceps_extension_counter": 9} \ No newline at end of file +{"bicep_curl_counter": 1, "shoulder_press_counter": 3, "rows_counter": 3, "squats_counter": 6, "triceps_extension_counter": 1} \ No newline at end of file From bfecb0b565c59d225e311f9bf984eac946746aa1 Mon Sep 17 00:00:00 2001 From: denjev03 Date: Thu, 16 Apr 2026 16:46:23 +0200 Subject: [PATCH 22/28] feat: clean up crew --- .gitignore | 7 +- wearable/README.md | 167 + wearable/arduino/imu_deploy.ino | 236 + wearable/arduino/imu_stream.ino | 93 + wearable/arduino/model.h | 4487 +++++++++++++++++ .../collect_data.py} | 8 +- wearable/data_collection/data/bicep_curl.csv | 2362 +++++++++ wearable/data_collection/data/rows.csv | 2362 +++++++++ .../data_collection/data/shoulder_press.csv | 2193 ++++++++ wearable/data_collection/data/squat.csv | 2293 +++++++++ .../data_collection/data/tricep_extension.csv | 2315 +++++++++ wearable/ferdig front/counts.json | 1 - .../{ => other_shi}/firmware/classifier.ino | 0 .../firmware/classifier_BLE.ino | 0 .../firmware/classifier_BLEv2.ino | 0 wearable/{ => other_shi}/firmware/model.h | 0 wearable/{ => other_shi}/firmware/model3.h | 0 .../{ => other_shi}/firmware/rep_counter.ino | 0 .../firmware/send_IMU_data.ino | 2 +- .../python/IMU_data_collection.py | 0 wearable/{ => other_shi}/python/ML.py | 0 .../data/bicep_curl/bicep_curl_dennis_1.csv | 0 .../data/bicep_curl/bicep_curl_gustav_1.csv | 0 .../python/data/rows/rows_dennis_1.csv | 0 .../python/data/rows/rows_gustav_1.csv | 0 .../shoulder_press_dennis_1.csv | 0 .../shoulder_press_gustav_1.csv | 0 .../shoulder_press_henrik_1.csv | 0 .../python/data/squat/squat_dennis_1.csv | 0 .../python/data/squat/squat_dj_1.csv | 65 + .../python/data/squat/squat_gustav_1.csv | 0 .../tricep_extension_dennis_1.csv | 0 .../tricep_extension_gustav_1.csv | 0 .../tricep_extension_henrik_1.csv | 0 .../other_shi/python/data0/bicep_curl.csv | 997 ++++ wearable/other_shi/python/data0/rows.csv | 897 ++++ .../other_shi/python/data0/shoulder_press.csv | 998 ++++ wearable/other_shi/python/data0/squat.csv | 1000 ++++ .../python/data0/tricep_extension.csv | 996 ++++ .../{ => other_shi}/python/plot_IMU_data.py | 32 +- .../{ => other_shi}/python/rep_counter.py | 64 +- .../python/rep_counter_variance.py | 129 +- wearable/python/device_scanner.py | 11 - .../Index.html | 0 wearable/visualisation/counts.json | 1 + .../dashboard_sync.py | 7 +- .../receive_results.py} | 0 47 files changed, 21640 insertions(+), 83 deletions(-) create mode 100644 wearable/README.md create mode 100644 wearable/arduino/imu_deploy.ino create mode 100644 wearable/arduino/imu_stream.ino create mode 100644 wearable/arduino/model.h rename wearable/{python/IMU_stream.py => data_collection/collect_data.py} (86%) create mode 100644 wearable/data_collection/data/bicep_curl.csv create mode 100644 wearable/data_collection/data/rows.csv create mode 100644 wearable/data_collection/data/shoulder_press.csv create mode 100644 wearable/data_collection/data/squat.csv create mode 100644 wearable/data_collection/data/tricep_extension.csv delete mode 100644 wearable/ferdig front/counts.json rename wearable/{ => other_shi}/firmware/classifier.ino (100%) rename wearable/{ => other_shi}/firmware/classifier_BLE.ino (100%) rename wearable/{ => other_shi}/firmware/classifier_BLEv2.ino (100%) rename wearable/{ => other_shi}/firmware/model.h (100%) rename wearable/{ => other_shi}/firmware/model3.h (100%) rename wearable/{ => other_shi}/firmware/rep_counter.ino (100%) rename wearable/{ => other_shi}/firmware/send_IMU_data.ino (97%) rename wearable/{ => other_shi}/python/IMU_data_collection.py (100%) rename wearable/{ => other_shi}/python/ML.py (100%) rename wearable/{ => other_shi}/python/data/bicep_curl/bicep_curl_dennis_1.csv (100%) rename wearable/{ => other_shi}/python/data/bicep_curl/bicep_curl_gustav_1.csv (100%) rename wearable/{ => other_shi}/python/data/rows/rows_dennis_1.csv (100%) rename wearable/{ => other_shi}/python/data/rows/rows_gustav_1.csv (100%) rename wearable/{ => other_shi}/python/data/shoulder_press/shoulder_press_dennis_1.csv (100%) rename wearable/{ => other_shi}/python/data/shoulder_press/shoulder_press_gustav_1.csv (100%) rename wearable/{ => other_shi}/python/data/shoulder_press/shoulder_press_henrik_1.csv (100%) rename wearable/{ => other_shi}/python/data/squat/squat_dennis_1.csv (100%) create mode 100644 wearable/other_shi/python/data/squat/squat_dj_1.csv rename wearable/{ => other_shi}/python/data/squat/squat_gustav_1.csv (100%) rename wearable/{ => other_shi}/python/data/tricep_extension/tricep_extension_dennis_1.csv (100%) rename wearable/{ => other_shi}/python/data/tricep_extension/tricep_extension_gustav_1.csv (100%) rename wearable/{ => other_shi}/python/data/tricep_extension/tricep_extension_henrik_1.csv (100%) create mode 100644 wearable/other_shi/python/data0/bicep_curl.csv create mode 100644 wearable/other_shi/python/data0/rows.csv create mode 100644 wearable/other_shi/python/data0/shoulder_press.csv create mode 100644 wearable/other_shi/python/data0/squat.csv create mode 100644 wearable/other_shi/python/data0/tricep_extension.csv rename wearable/{ => other_shi}/python/plot_IMU_data.py (85%) rename wearable/{ => other_shi}/python/rep_counter.py (88%) rename wearable/{ => other_shi}/python/rep_counter_variance.py (79%) delete mode 100644 wearable/python/device_scanner.py rename wearable/{ferdig front => visualisation}/Index.html (100%) create mode 100644 wearable/visualisation/counts.json rename wearable/{ferdig front => visualisation}/dashboard_sync.py (89%) rename wearable/{ferdig front/exercise_classification.py => visualisation/receive_results.py} (100%) diff --git a/.gitignore b/.gitignore index 25af7c5..785fd46 100644 --- a/.gitignore +++ b/.gitignore @@ -253,5 +253,8 @@ lerna-debug.log .DS_Store Thumbs.db -#CSV files -*.csv \ No newline at end of file +# Runtime file, changes every inference session +src/wearable/visualisation/counts.json + +# Raw data (optional - remove these lines if you want data tracked) +src/wearable/data_collection/data/*.csv diff --git a/wearable/README.md b/wearable/README.md new file mode 100644 index 0000000..61090e1 --- /dev/null +++ b/wearable/README.md @@ -0,0 +1,167 @@ +# Wearable — Exercise Recognition + +IMU-based exercise classification running on an Arduino Nano 33 BLE Sense. The device detects motion, runs on-device inference using a TensorFlow Lite Micro model, and broadcasts the recognised exercise and confidence score over BLE. + +Recognised exercises: **bicep curl**, **shoulder press**, **row** + +______________________________________________________________________ + +## Project structure + +``` +wearable/ +├── arduino/ +│ ├── imu_stream.ino # Data collection — streams raw IMU over BLE +│ ├── imu_deploy.ino # Inference + BLE output +│ └── model.h # Pre-trained quantized model (ready to flash) +├── data_collection/ +│ ├── collect_data.py # Reads BLE stream, saves labelled CSVs +│ └── data/ +│ ├── rows.csv +│ ├── shoulder_press.csv +│ └── bicep_curl.csv +├── training/ +│ └── train_model.ipynb # Google Colab notebook +├── models/ +│ ├── model.h5 # Trained Keras model +│ └── model_quantized.tflite # Quantized TFLite model +└── visualisation/ + ├── receive_results.py # Receives BLE output, writes results.json + ├── index.html # Live results webpage + └── results.json # Written at runtime, not tracked in git +``` + +______________________________________________________________________ + +## Arduino libraries + +Install these via the Arduino Library Manager before opening any sketch: + +- `Arduino_BMI270_BMM150` +- `ArduinoBLE` +- `Arduino_TensorFlowLite` + +______________________________________________________________________ + +## Quick start — flash the pre-trained model + +A finished `model.h` is already included in `arduino/`. If you just want to try the system without collecting data or training: + +1. Open `arduino/imu_deploy.ino` in the Arduino IDE +1. Make sure `model.h` is in the same folder +1. Flash to the board and follow [Step 3](#step-3--deploy-to-arduino) below + +______________________________________________________________________ + +## Step 1 — Collect training data + +### 1.1 Flash the streaming sketch + +Open `arduino/imu_stream.ino` and flash it to the board. It advertises over BLE and streams raw IMU readings in the format: + +``` +aX,aY,aZ,gX,gY,gZ +``` + +### 1.2 Record a session + +```bash +python data_collection/collect_data.py --label bicep_curl +``` + +The script uses `bleak` to connect to the Arduino over BLE and appends incoming rows to `data_collection/data/