Activity Recognition Based on Online Sequential Kernel Extreme Learning Machine

Activity recognition (AR) is the basis for many applications concerning health care, sports, security and gaming industry. Traditionally, batching learning recognition algorithms is adopted to train network. However, the amount of data is considerable and not all training data arrives instantly, the learning procedure is time-consuming and the network weights cannot be updated online. In this paper, Classification of the human activities is performed with Online Sequential Kernel Extreme Learning Machine (OS-KELM). The method of feature selection based on Fisher criterion and feature clustering has been adopted to reduce difficulty and improve efficiency of learning. A tri-axial accelerometer and gyros data from a user's smart phone are used to recognize walking, waling downstairs, walking upstairs, standing, sitting and laying. Experimental results with an average accuracy of 91.89% are achieved.