Wearable Activity Classifier – Group ___
Task
Classify a 100-step, one-feature sensor sequence into Stationary, Walking, or Running.
Model selected
- Architecture: LSTM
- Input shape:
(100, 1) - Output classes: 3
- Parameters: 4,451
Training data
Synthetic signals generated in the class notebook. The dataset was designed for teaching and is not a real wearable benchmark.
Evaluation
- Test accuracy: 0.913
- Training time in our run: 5.4 seconds
Why we selected this model
LSTM gave the second-highest accuracy (91.3%) among all four models while using far fewer parameters than the CNN (4,451 vs 49,475). It also clearly outperformed SimpleRNN (46.7%), showing that LSTM's gated memory handles this 100-step sequence much better than a basic RNN. We prioritized this balance of accuracy and efficiency over the CNN's marginally higher (100%) accuracy.
Limitations
- Synthetic, simplified data
- One sensor feature only
- No testing across real users/devices
- Not intended for health, safety, or production use
Team learning note
We learned that a more complex architecture doesn't always win — SimpleRNN struggled with this 100-step sequence, but LSTM's gating mechanism handled it far better with only a small increase in parameters over the RNN.
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