Human Activity Recognition (HAR) using mobile devices makes it possible to classify
an individual’s physical activities with non-intrusive and low-cost sensor data. Thus,
HAR can collaborate with physiotherapeutic treatment by allowing the monitoring
of activities such as walking, running, and going up or down stairs. UniMib SHAR
database provides data collected from mobile device accelerometers to be a reference for
evaluating machine learning algorithms for HAR. This work aims to compare different
architectures for classifying the UniMib SHAR database, combining LSTM, GRU, and
BLSTM models, with optimization algorithms, and regularization techniques, resulting
in almost 40 different neural network models. The results show that the BLSTM and
GRU networks, combined with the RMSProp and Adam optimization algorithms, and
Dropout regularization method, obtained superior performance compared to the other
combinations.
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