Deep Analysis for Smartphone-based Human Activity Recognition

Wearable-based approach and vision-based approach are two of the most common approaches in human activity recognition. However, the concern of privacy issues may limit the application of the vision-based approach. Besides, some individuals are reluctant to wear sensor devices. Hence, smartphone-based human physical activity recognition is a popular alternative. In this paper, we propose a deep analysis to interpret and predict accelerometer data captured using a smartphone for activity recognition. The proposed deep model is able to extract deep features from both spatial and temporal domains of the inertial data. The recognition accuracy of the proposed model is assessed using UCI and WISDM accelerometer data. Empirical results exhibit a promising performance, attaining accuracy score of 90% in UCI and 87% in WISDM database.