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Unobtrusive and smart recognition of human activities using smartphonesinertial sensors is an interesting topic in the field of artificialintelligence acquired tremendous popularity among researchers, especially inrecent years. A considerable challenge that needs more attention is thereal-time detection of physical activities, since for many real-worldapplications such as health monitoring and elderly care, it is required torecognize users' activities immediately to prevent severe damages toindividuals' wellness. In this paper, we propose a human activity recognition(HAR) approach for the online prediction of physical movements, benefiting fromthe capabilities of incremental learning algorithms. We develop a HAR systemcontaining monitoring software and a mobile application that collectsaccelerometer and gyroscope data and send them to a remote server via theInternet for classification and recognition operations. Six incrementallearning algorithms are employed and evaluated in this work and compared withseveral batch learning algorithms commonly used for developing offline HARsystems. The Final results indicated that considering all performanceevaluation metrics, Incremental K-Nearest Neighbors and Incremental NaiveBayesian outperformed other algorithms, exceeding a recognition accuracy of 95%in real-time.

Incremental Learning Techniques for Online Human Activity Recognition