The deep learning training of the GAIT dataset generated from the gait-logger-server and gait-logger-firmware are available in the src folder. The gait-deep learning mostly studies on using three architectures CNN, CNN-Statistics and CNN-LSTM whose implementation and training is done using a Keras API. Then using different parameters and benchmarking factors, the best model is generated, and converted using tensor-js library to be deployed to the Raspberry Pi for inference.

The overall deeplearning architecture for training all the data was trained using this particular flow. Email [email protected] for the full dataset.

The detailed implementation of CNN architecture is shown below:

The detail implementation of CNN-statistics is shown below:

the statistical features are computed using

And the final CNN architecture is given by

These were the results of the best models of a. CNN

b. CNN-stats

c. CNN-LSTM

All the three architecture were deployed to the single board computer Raspberry Pi and the inference results were as follows: