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#

step-counting

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Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

  • Updated Dec 8, 2022
  • Python

Step counting and human-activity recognition from wearable IMU sensors (LilyGo wristband): a peak-detection step counter (MAE ≈ 4.7 on labeled traces) and a Random-Forest activity & smartwatch-placement classifier (≈0.83 acc).

  • Updated Jun 4, 2026
  • Jupyter Notebook

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