Human activity detection from inertial data using RNN and LSTM network Online publication date: Thu, 28-Jul-2022
by Thanina Boultache; Brahim Achour; Mourad Laghrouche
International Journal of Sensor Networks (IJSNET), Vol. 39, No. 3, 2022
Abstract: With the advancement of smartphones and the rapid development of artificial intelligence, human activity detection systems help to improve human welfare and health. However, these systems need to be constantly renewed, improved and updated. In this paper, we propose an automatic and real-time classification system of physical activities using deep learning architecture, recurrent neural networks (RNN) and short-term memory networks (LSTM). To develop and validate the learning model we gathered a dataset of 697,964 recordings of accelerometer and gyroscope signals embedded in smartphones. The data was collected from 20 people aged 13 to 82 years old of different categories (sportsman, elderly, etc.). Then, the raw data is preprocessed and the learning model is trained. The results of the experiment show that the proposed algorithm achieves an average accuracy of 95%. The developed model was exported to an Android application for real-time predictions.
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