Online sequential extreme learning machine algorithm based human activity recognition using inertial data - Al Jeroudi, Yazan; Ali, M. A.; Latief, Marsad; Akmeliawati, Rini (IEEE, 2015)
Human activity recognition (HAR) is the basis for many real world applications concerning health care, sports and gaming industry. Different methodological perspectives have been proposed to perform HAR. One appealing methodology is to take an advantage of data that are collected from inertial sensors which are embedded in the individual's smartphone. These data contain rich amount of information about daily activities of the user. However, there is no straightforward analytical mapping between a performed activity and its corresponding data. Besides, online training for the classification in these types of applications is a concern. This paper aims at classifying human activities based on the inertial data collected from a user's smartphone. An Online Sequential Extreme Learning Machine (OSELM) method is implemented to train a single hidden layer feed-forward network (SLFN). Experimental results with an average accuracy of 82.05% are achieved.