README.md

metadata

license: mit
tags:
  - keras
  - tensorflow
  - time-series-classification
  - sensor-data
  - deep-learning-lab

Wearable Activity Classifier — CNN

Model description

A 1D Convolutional Neural Network that classifies short wearable-sensor sequences into three physical activities: Stationary, Walking, and Running. Built as part of a beginner deep learning group lab comparing CNN, SimpleRNN, LSTM, and a CNN+LSTM hybrid on the same fixed dataset.

Intended use

Educational demonstration of sequence classification on wearable sensor data. Not intended for production health/fitness monitoring.

Architecture

Input (100 time steps, 1 sensor channel) → Conv1D(32 filters, kernel_size=5, activation="relu") → MaxPooling1D(pool_size=2) → Flatten() → Dense(32, activation="relu") → Dense(3, activation="softmax")

Total parameters: 49,475

Training data

Fixed Wearable_Activity_Dataset release (seed 42 split): 600 training sequences, 150 validation, 150 test — each sequence is 100 time steps of a single sensor reading. Training set is perfectly class-balanced (200 Stationary / 200 Walking / 200 Running).

Training procedure

• Optimizer: Adam (default learning rate)
• Loss: sparse categorical crossentropy
• Epochs: 6, batch size: 32
• Same training configuration used across all four models in this lab, for a fair comparison

Evaluation results

Model	Test Accuracy	Parameters	Train Time (s)
CNN	1.000	49,475	2.81
SimpleRNN	0.580	1,187	4.77
LSTM	0.693	4,451	7.12
CNN+LSTM (hybrid)	1.000	8,611	6.35

Limitations

• Trained on a small, synthetic/fixed dataset — accuracy may not generalize to real-world wearable sensor data with more noise, sensor drift, or additional activity classes
• Only 6 training epochs — SimpleRNN and LSTM in particular likely hadn't converged; their reported accuracy understates what they could achieve with more training
• Fixed 100-step sequence length — not tested on longer or variable-length sequences

How to use

import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense

model = Sequential([
    Conv1D(32, kernel_size=5, activation="relu", input_shape=(100, 1)),
    MaxPooling1D(pool_size=2),
    Flatten(),
    Dense(32, activation="relu"),
    Dense(3, activation="softmax")
])
model.load_weights("activity_model.weights.h5")

# X: numpy array of shape (n_samples, 100, 1)
predictions = model.predict(X)

Authors

Group lab submission — [Group 6 - Iqra University Main Campus], CNN–RNN–LSTM Model Challenge, Beginner Deep Learning Group Lab.