From cc9abd6756f94f56376ba0d1886b1789a9f586dd Mon Sep 17 00:00:00 2001 From: qadirq01 <67214834+qusayqadir@users.noreply.github.com> Date: Sat, 10 May 2025 22:23:51 -0400 Subject: [PATCH] ipynb CNN gait classifier --- .DS_Store | Bin 0 -> 6148 bytes .../processed_john_5_stair.json | 2978 +++++++ .../processed_john_stair_6.json | 2992 +++++++ .../processed_john_stair_7.json | 2008 +++++ .../processed_john_stair_8.json | 70 + .../processed_john_stair_8_fix.json | 3009 +++++++ .../processed_john_static_squat.json | 207 + .../processed_juan_8.json | 2507 ++++++ .../processed_juan_boots_8.json | 2977 +++++++ .../imu_data_treadmill_4min_1.7mph.json | 6980 +++++++++++++++++ .../imu_data_treadmill_4min_1.9mph.json | 2507 ++++++ .../imu_data_treadmill_5min_1.9mph.json | 3069 ++++++++ requirements.txt | 6 +- src/gaitClassiferCNN.ipynb | 1521 ++++ 14 files changed, 30829 insertions(+), 2 deletions(-) create mode 100644 .DS_Store create mode 100644 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a/data/processed_stair_data/processed_john_static_squat.json b/data/processed_stair_data/processed_john_static_squat.json new file mode 100644 index 0000000..eed18ad --- /dev/null +++ b/data/processed_stair_data/processed_john_static_squat.json @@ -0,0 +1,207 @@ +{"sensors": [{"euler": {"heading": -0.1506249999999998, "pitch": -0.21062499999999318, "roll": 0.3237499999999969}, "location": "Left Knee"}, {"euler": {"heading": -0.2943750000000023, "pitch": -0.7506249999999994, "roll": -0.31937499999999375}, "location": "Left Ankle"}, {"euler": {"heading": 1.0962499999999977, "pitch": 1.9224999999999994, "roll": 0.8443749999999994}, "location": "Left Hip"}, {"euler": {"heading": 0.6356250000000045, "pitch": -0.140625, "roll": 0.4625000000000057}, "location": "Right Ankle"}, {"euler": {"heading": 0.6081250000000011, "pitch": 0.125, "roll": 0.3243750000000034}, "location": "Right Knee"}, {"euler": {"heading": -0.06187499999998636, "pitch": -4.103125000000006, "roll": 1.75}, "location": 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Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Hip"}],"timestamp":"2025-03-06 01:24:34.910"} +{"sensors":[{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Hip"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Hip"}],"timestamp":"2025-03-06 01:24:35.10"} +{"sensors":[{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Hip"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right 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Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Hip"}],"timestamp":"2025-03-06 01:24:35.312"} +{"sensors":[{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Hip"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Hip"}],"timestamp":"2025-03-06 01:24:36.608"} +{"sensors":[{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Hip"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Hip"}],"timestamp":"2025-03-06 01:24:38.918"} +{"sensors":[{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Knee"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Left Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Ankle"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Hip"},{"euler":{"heading":0.0,"pitch":0.0,"roll":0.0},"location":"Right Knee"},{"euler":{"heading":0.0,"pitch":-0.0625,"roll":0.0},"location":"Left Hip"}],"timestamp":"2025-03-06 01:24:39.20"} +{"sensors":[{"euler":{"heading":0.0,"pitch":-0.0625,"roll":0.0},"location":"Left Knee"},{"euler":{"heading":0.0,"pitch":-0.0625,"roll":0.0},"location":"Left Ankle"},{"euler":{"heading":0.0,"pitch":-0.0625,"roll":0.0},"location":"Right Ankle"},{"euler":{"heading":0.0,"pitch":-0.0625,"roll":0.0},"location":"Right Hip"},{"euler":{"heading":0.0,"pitch":-0.0625,"roll":0.0},"location":"Right Knee"},{"euler":{"heading":0.0,"pitch":-0.0625,"roll":0.0},"location":"Left Hip"}],"timestamp":"2025-03-06 01:24:39.121"} diff --git a/requirements.txt b/requirements.txt index 790838d..5a0b657 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,8 @@ -numpy==2.2.1 +numpy==1.24.3 pandas==2.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 -six==1.17.0 +six==1.16.0 tzdata==2024.2 +tensorflow==2.15.0 +scikit-learn==1.3.0 diff --git a/src/gaitClassiferCNN.ipynb b/src/gaitClassiferCNN.ipynb new file mode 100644 index 0000000..efc74c9 --- /dev/null +++ b/src/gaitClassiferCNN.ipynb @@ -0,0 +1,1521 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "23d75257", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World\n" + ] + } + ], + "source": [ + "print(\"Hello World\")" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "99aa741b", + "metadata": {}, + "outputs": [], + "source": [ + "import json \n", + "import pandas as pd\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential \n", + "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout\n", + "from sklearn.model_selection import train_test_split # used to break the training data \n", + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "from tensorflow.keras.utils import to_categorical \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "104dfa8c", + "metadata": {}, + "outputs": [], + "source": [ + "df_stair = pd.read_json(\"data/processed_stair_data/processed_john_5_stair.json\", lines=True) \n", + "df_treadmill = pd.read_json(\"data/treadmill_imu_data/imu_data_treadmill_5min_1.9mph.json\", lines=True) \n" + ] + }, + { + "cell_type": "markdown", + "id": "2c3dccac", + "metadata": {}, + "source": [ + "### Exploratory Data Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "3bb7d72f", + "metadata": {}, + "outputs": [], + "source": [ + "expected_sensors = {'Left Knee', 'Left Ankle', 'Left Hip', 'Right Knee', 'Right Ankle', 'Right Hip'}" + ] + }, + { + "cell_type": "markdown", + "id": "183b4b82", + "metadata": {}, + "source": [ + "check if any timestamp has a missing sensor value" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "e261288d", + "metadata": {}, + "outputs": [], + "source": [ + "# staircase \n", + "\n", + "with open(\"data/processed_stair_data/processed_john_5_stair.json\", \"r\") as stair: \n", + " entiresStairs = [json.loads(line) for line in stair] \n", + " \n", + "for entry in entiresStairs: \n", + " sensor_locations = {s[\"location\"] for s in entry[\"sensors\"]} \n", + " if sensor_locations != expected_sensors: \n", + " print(f\"Missing sensor at {entry}\") \n", + " \n", + "# treadmill \n", + "\n", + "with open(\"data/treadmill_imu_data/imu_data_treadmill_5min_1.9mph.json\", \"r\") as treadmill: \n", + " entriesTreadMill = [json.loads(line) for line in treadmill] \n", + "\n", + "for entry in entriesTreadMill: \n", + " sensor_locations = {s[\"location\"] for s in entry[\"sensors\"]} \n", + " if sensor_locations != expected_sensors: \n", + " print(f\"Missing sensor at {entry}\") \n", + " \n", + "#runs with no errors, consistent sensor readings per timestamp " + ] + }, + { + "cell_type": "markdown", + "id": "5cc821e5", + "metadata": {}, + "source": [ + "check for uniform time intervals" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "7bfdec8e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 days 00:00:00.101000 1817\n", + "0 days 00:00:00.100000 505\n", + "0 days 00:00:00.102000 67\n", + "-1 days +23:59:59.345000 8\n", + "0 days 00:00:00.002000 8\n", + " ... \n", + "-1 days +23:59:59.336000 1\n", + "-1 days +23:59:59.272000 1\n", + "0 days 00:00:00.227000 1\n", + "-1 days +23:59:59.975000 1\n", + "-1 days +23:59:59.965000 1\n", + "Name: count, Length: 271, dtype: int64\n", + "2025-03-06 01:21:49.560 2\n", + "2025-03-06 01:23:44.670 2\n", + "2025-03-06 01:19:11.599 1\n", + "2025-03-06 01:22:38.890 1\n", + "2025-03-06 01:22:37.282 1\n", + " ..\n", + "2025-03-06 01:20:54.814 1\n", + "2025-03-06 01:20:54.915 1\n", + "2025-03-06 01:20:55.170 1\n", + "2025-03-06 01:20:55.118 1\n", + "2025-03-06 01:24:39.121 1\n", + "Name: count, Length: 3067, dtype: int64\n" + ] + } + ], + "source": [ + "timestampsStair = pd.to_datetime([entry['timestamp'] for entry in entiresStairs]) \n", + "timestampsTreadMill= pd.to_datetime([entry['timestamp'] for entry in entriesTreadMill]) \n", + "\n", + "time_diffsStair = timestampsStair.diff().dropna()\n", + "time_diffsTreadMill = timestampsTreadMill.dropna()\n", + "\n", + "print(time_diffsStair.value_counts())\n", + "print(time_diffsTreadMill.value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "b4aaa96d", + "metadata": {}, + "source": [ + "flatten JSON into DF friendly for staircase" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3e351d6", + "metadata": {}, + "outputs": [], + "source": [ + "flat_stair = [] \n", + "\n", + "for entry in entiresStairs: \n", + " row = {'timestamp':entry['timestamp']}\n", + " for sensor in entry['sensors']: \n", + " loc = sensor['location'].replace(\" \", \"_\") \n", + " for axis in ['heading','pitch','roll']:\n", + " row[f\"{loc}_{axis}\"] = sensor['euler'][axis] \n", + " \n", + " flat_stair.append(row) \n", + " \n", + "df_stair = pd.DataFrame(flat_stair) \n", + "df_stair['timestamp'] = pd.to_datetime(df_stair['timestamp']) \n", + "df_stair.set_index('timestamp', inplace=True) " + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "c1386b56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Left_Knee_heading 0\n", + "Left_Knee_pitch 0\n", + "Left_Knee_roll 0\n", + "Left_Ankle_heading 0\n", + "Left_Ankle_pitch 0\n", + "Left_Ankle_roll 0\n", + "Left_Hip_heading 0\n", + "Left_Hip_pitch 0\n", + "Left_Hip_roll 0\n", + "Right_Ankle_heading 0\n", + "Right_Ankle_pitch 0\n", + "Right_Ankle_roll 0\n", + "Right_Knee_heading 0\n", + "Right_Knee_pitch 0\n", + "Right_Knee_roll 0\n", + "Right_Hip_heading 0\n", + "Right_Hip_pitch 0\n", + "Right_Hip_roll 0\n", + "dtype: int64\n", + "\n", + "DatetimeIndex: 2978 entries, 2025-03-29 17:16:16.424000 to 2025-03-29 17:21:16.554000\n", + "Data columns (total 18 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Left_Knee_heading 2978 non-null float64\n", + " 1 Left_Knee_pitch 2978 non-null float64\n", + " 2 Left_Knee_roll 2978 non-null float64\n", + " 3 Left_Ankle_heading 2978 non-null float64\n", + " 4 Left_Ankle_pitch 2978 non-null float64\n", + " 5 Left_Ankle_roll 2978 non-null float64\n", + " 6 Left_Hip_heading 2978 non-null float64\n", + " 7 Left_Hip_pitch 2978 non-null float64\n", + " 8 Left_Hip_roll 2978 non-null float64\n", + " 9 Right_Ankle_heading 2978 non-null float64\n", + " 10 Right_Ankle_pitch 2978 non-null float64\n", + " 11 Right_Ankle_roll 2978 non-null float64\n", + " 12 Right_Knee_heading 2978 non-null float64\n", + " 13 Right_Knee_pitch 2978 non-null float64\n", + " 14 Right_Knee_roll 2978 non-null float64\n", + " 15 Right_Hip_heading 2978 non-null float64\n", + " 16 Right_Hip_pitch 2978 non-null float64\n", + " 17 Right_Hip_roll 2978 non-null float64\n", + "dtypes: float64(18)\n", + "memory usage: 442.0 KB\n", + "None\n" + ] + } + ], + "source": [ + "print(df_stair.isnull().sum()) \n", + "print(df_stair.info()) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "6a8678a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timestamp
2025-03-29 17:16:16.424-6.06125-1.761255.293125-1.773125-23.038751.67375-7.312524.524375-19.4537513.822561.93562511.71062511.881255.816259.4025-10.88375-12.468757.883125
2025-03-29 17:16:16.525-5.873750.113757.1056253.664375-20.538755.04875-5.312525.461875-18.0162515.072561.99812511.77312512.193754.628759.2150-11.75875-17.281258.008125
2025-03-29 17:16:16.627-1.49875-1.261255.3556257.289375-17.288757.86125-2.250025.274375-16.7662514.947560.87312510.64812511.318752.441258.0900-11.57125-20.093758.195625
2025-03-29 17:16:16.7271.68875-1.636253.35562510.726875-14.2262510.361252.625031.024375-13.2662514.8850-11.5643759.21062510.818750.316256.8400-10.25875-23.093757.695625
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Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Left_Knee_heading 3069 non-null float64\n", + " 1 Left_Knee_pitch 3069 non-null float64\n", + " 2 Left_Knee_roll 3069 non-null float64\n", + " 3 Left_Ankle_heading 3069 non-null float64\n", + " 4 Left_Ankle_pitch 3069 non-null float64\n", + " 5 Left_Ankle_roll 3069 non-null float64\n", + " 6 Right_Ankle_heading 3069 non-null float64\n", + " 7 Right_Ankle_pitch 3069 non-null float64\n", + " 8 Right_Ankle_roll 3069 non-null float64\n", + " 9 Right_Hip_heading 3069 non-null float64\n", + " 10 Right_Hip_pitch 3069 non-null float64\n", + " 11 Right_Hip_roll 3069 non-null float64\n", + " 12 Right_Knee_heading 3069 non-null float64\n", + " 13 Right_Knee_pitch 3069 non-null float64\n", + " 14 Right_Knee_roll 3069 non-null float64\n", + " 15 Left_Hip_heading 3069 non-null float64\n", + " 16 Left_Hip_pitch 3069 non-null float64\n", + " 17 Left_Hip_roll 3069 non-null float64\n", + "dtypes: float64(18)\n", + "memory usage: 455.6 KB\n", + "None\n" + ] + } + ], + "source": [ + "print(df_treadmill.isnull().sum()) \n", + "print(df_treadmill.info()) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "63dd065a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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window_size, stride): \n", + " seq = data[i:i+window_size]\n", + " label = labels[i+window_size - 1 ] \n", + " X.append(seq)\n", + " y.append(label)\n", + " \n", + " return X, y \n", + "\n", + "\n", + "X_walk, y_walk = create_sequence(df_treadmill, window_size=25, stride=5) \n", + "X_stair, y_stair = create_sequence(df_stair, window_size=25, stride=5) \n", + "\n", + "X = np.array(X_walk + X_stair) \n", + "y = np.array(y_walk + y_stair) " + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "78a77781", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1200, 25, 18)\n" + ] + }, + { + "data": { + "text/plain": [ + "(1200,)" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(X.shape)\n", + "y.shape" + ] + }, + { + "cell_type": "markdown", + "id": "bf934ada", + "metadata": {}, + "source": [ + "### 1D CNN Model" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "47354c7a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/qusayqadir/Documents/github/ExoSkeleton Gait Classifer /.venv/lib/python3.9/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.6872 - loss: 0.6180 - val_accuracy: 1.0000 - val_loss: 0.0337\n", + "Epoch 2/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9941 - loss: 0.0506 - val_accuracy: 1.0000 - val_loss: 6.6663e-04\n", + "Epoch 3/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0027 - val_accuracy: 1.0000 - val_loss: 1.8220e-04\n", + "Epoch 4/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 9.3235e-04 - val_accuracy: 1.0000 - val_loss: 1.0043e-04\n", + "Epoch 5/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 9.6703e-04 - val_accuracy: 1.0000 - val_loss: 5.6222e-05\n", + "Epoch 6/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 5.0916e-04 - val_accuracy: 1.0000 - val_loss: 3.5478e-05\n", + "Epoch 7/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 5.1362e-04 - val_accuracy: 1.0000 - val_loss: 2.2875e-05\n", + "Epoch 8/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 2.6237e-04 - val_accuracy: 1.0000 - val_loss: 1.6641e-05\n", + "Epoch 9/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 4.5533e-04 - val_accuracy: 1.0000 - val_loss: 1.3164e-05\n", + "Epoch 10/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 1.8864e-04 - val_accuracy: 1.0000 - val_loss: 1.0138e-05\n", + "Epoch 11/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 2.8777e-04 - val_accuracy: 1.0000 - val_loss: 8.5606e-06\n", + "Epoch 12/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 9.7337e-05 - val_accuracy: 1.0000 - val_loss: 7.0313e-06\n", + "Epoch 13/20\n", + "\u001b[1m30/30\u001b[0m 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- loss: 6.1044e-05 - val_accuracy: 1.0000 - val_loss: 2.9479e-06\n", + "Epoch 18/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 6.7863e-05 - val_accuracy: 1.0000 - val_loss: 2.5650e-06\n", + "Epoch 19/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 9.7559e-05 - val_accuracy: 1.0000 - val_loss: 2.0568e-06\n", + "Epoch 20/20\n", + "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 2.6362e-04 - val_accuracy: 1.0000 - val_loss: 1.7707e-06\n" + ] + } + ], + "source": [ + "y_cat = to_categorical(y) \n", + "\n", + "#train-test-split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_cat, test_size=0.2, train_size=0.8, random_state=42, stratify=y)\n", + "\n", + "# 1D CNN model \n", + "\n", + "model = Sequential([\n", + " Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(25,18)), MaxPooling1D(pool_size=2),Dropout(0.3), \n", + " \n", + " Conv1D(filters=128, kernel_size=3, activation='relu'), MaxPooling1D(pool_size=2), Dropout(0.3),\n", + " \n", + " Flatten(), Dense(64, activation='relu'),Dropout(0.3), Dense(2, activation='softmax') \n", + "])\n", + "\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "hist = model.fit(X_train, y_train, epochs=20, batch_size = 32, validation_data=(X_test, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "8b6d257c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 1.7181e-06 \n", + "Loss: 0.0000017707, Accuracy: 1.00\n" + ] + } + ], + "source": [ + "loss, accuracy = model.evaluate(X_test, y_test)\n", + "print(f\"Loss: {loss:.10f}, Accuracy: {accuracy:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3efd3c3c", + "metadata": {}, + "source": [ + "loss is nearing 0 and accuracy is 100% " + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "cee23f09", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(hist.history['accuracy'], label='Train Acc')\n", + "plt.plot(hist.history['val_accuracy'], label='Val Acc')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.title('CNN Training Performance')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fa650389", + "metadata": {}, + "source": [ + "## FeedForward Neural Network: FNN " + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "9ee71615", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label\n", + "0 3069\n", + "1 2978\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Left_Knee_heading Left_Knee_pitch Left_Knee_roll Left_Ankle_heading \\\n", + "0 0.342644 1.195885 0.282976 0.719184 \n", + "1 -0.594986 0.515785 0.849676 -1.013153 \n", + "2 -0.878331 -0.081995 0.470372 0.058396 \n", + "3 1.350145 -1.670199 -1.810845 -0.570946 \n", + "4 -0.776901 0.358745 0.885323 -0.286441 \n", + "5 1.441594 1.430842 1.264074 -0.109343 \n", + "6 -0.524481 -0.260353 0.194658 -0.091023 \n", + "7 -0.757525 0.331565 0.836308 -0.174076 \n", + "8 -0.411995 -0.393233 -0.736624 0.263173 \n", + "9 -0.773133 -0.109353 0.658072 0.342561 \n", + "\n", + " Left_Ankle_pitch Left_Ankle_roll Right_Ankle_heading Right_Ankle_pitch \\\n", + "0 0.609635 0.789210 0.322825 -0.485249 \n", + "1 -0.393919 -1.395918 -0.176574 -0.206970 \n", + "2 0.046909 -0.017122 -0.883532 -0.647893 \n", + "3 -0.263343 -0.135574 -0.908130 -0.447645 \n", + "4 0.109362 -0.551213 -1.182496 0.674725 \n", + "5 0.216933 -0.458295 -0.617393 1.524551 \n", + "6 0.090152 -0.047205 0.139961 -0.669064 \n", + "7 0.397499 -0.427323 -1.172786 0.563185 \n", + "8 0.243826 0.766528 0.897315 -0.318510 \n", + "9 0.750949 0.456802 0.095296 0.435711 \n", + "\n", + " Right_Ankle_roll Right_Hip_heading Right_Hip_pitch Right_Hip_roll \\\n", + "0 -0.430982 0.015477 -0.217667 0.465924 \n", + "1 -1.597901 0.906516 -0.296808 0.816408 \n", + "2 0.048743 0.280704 0.484231 -0.325619 \n", + "3 0.231575 -0.603568 0.104750 0.488847 \n", + "4 -0.403414 0.175715 0.094901 -0.187153 \n", + "5 0.846631 -1.105014 0.035551 1.411111 \n", + "6 0.318834 0.287162 -0.115791 -1.060622 \n", + "7 -0.283039 0.144656 0.258113 -0.001308 \n", + "8 0.892929 -0.262766 -0.388800 -0.236711 \n", + "9 0.869780 -0.288344 0.177990 -1.314610 \n", + "\n", + " Right_Knee_heading Right_Knee_pitch Right_Knee_roll Left_Hip_heading \\\n", + "0 -0.507773 -0.224058 -0.991062 -0.203080 \n", + "1 1.863429 -0.060511 0.677008 -0.372630 \n", + "2 -0.504380 -0.284309 0.021560 1.658435 \n", + "3 -0.154514 -0.350397 0.421475 -0.161846 \n", + "4 -0.466009 -0.044670 -0.776367 -0.248993 \n", + "5 0.394762 -0.998476 -1.618322 -0.069721 \n", + "6 -1.108162 0.161252 0.465087 -0.168630 \n", + "7 -0.408084 -0.074088 -0.860849 -0.230448 \n", + "8 -1.295015 0.039056 0.798719 -0.082967 \n", + "9 -0.522065 -0.037882 -0.575901 0.035370 \n", + "\n", + " Left_Hip_pitch Left_Hip_roll label \n", + "0 0.369226 0.148463 1 \n", + "1 0.356488 -0.167473 0 \n", + "2 3.084669 1.545197 1 \n", + "3 0.073508 0.078563 1 \n", + "4 -0.029437 -0.091420 0 \n", + "5 0.465119 -0.430201 0 \n", + "6 -0.383915 0.682939 0 \n", + "7 -0.140926 -0.125989 0 \n", + "8 -1.038558 1.201482 0 \n", + "9 -0.849884 0.489349 0 " + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_treadmill['label'] = 0 \n", + "df_stair['label'] = 1 \n", + "# do not need to worry about missing or incomplete data \n", + "\n", + "#combine dataframes\n", + "df = pd.concat([df_treadmill, df_stair], axis=0)\n", + "\n", + "#shuffle the columns\n", + "df = df.sample(frac=1, random_state=42).reset_index(drop=True) \n", + "\n", + "\n", + "print(df['label'].value_counts())\n", + "df.head(n=10) " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}