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Project comparing traditional ML and Deep Learning approcahes to fall detection

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adrian-soch/fall_detector

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Fall-Detector

Preprocessing

  1. Download and unzip datasets.
  2. Confirm annotations included the start and stop frames at the beginning of the .txt.
  3. Excludes dataset without annotations.
  4. Run fallDetectorV0\preprocess.py.
    • this creates the MHIs and the contour feature csv files
    • PCA mean and eigenvalues are saved in fallDetectorV0\pcaEig.joblib

Training

SVM

  1. Run fallDetectorV0\training.py
    • sklearn SVM object is saved in fallDetectorV0\svm.joblib

CNN

  1. Run fallDetectorV0\training.ipynb.
    • Weights are saved to trained_model\fdnet.pt

Online fall detection

SVM

  1. Run fallDetectorV0\onlineFallDetector.py

CNN

Not implemented

Note: Code reference for MHI and CNN training: https://medium.com/diving-in-deep/fall-detection-with-pytorch-b4f19be71e80

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Project comparing traditional ML and Deep Learning approcahes to fall detection

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