  Unobtrusive and smart recognition of human activities using smartphones
inertial sensors is an interesting topic in the field of artificial
intelligence acquired tremendous popularity among researchers, especially in
recent years. A considerable challenge that needs more attention is the
real-time detection of physical activities, since for many real-world
applications such as health monitoring and elderly care, it is required to
recognize users' activities immediately to prevent severe damages to
individuals' wellness. In this paper, we propose a human activity recognition
(HAR) approach for the online prediction of physical movements, benefiting from
the capabilities of incremental learning algorithms. We develop a HAR system
containing monitoring software and a mobile application that collects
accelerometer and gyroscope data and send them to a remote server via the
Internet for classification and recognition operations. Six incremental
learning algorithms are employed and evaluated in this work and compared with
several batch learning algorithms commonly used for developing offline HAR
systems. The Final results indicated that considering all performance
evaluation metrics, Incremental K-Nearest Neighbors and Incremental Naive
Bayesian outperformed other algorithms, exceeding a recognition accuracy of 95%
in real-time.
