Analyzing and Visualizing Jump Performance Using Wireless Body Sensors

Abstract

Advancement in technology has led to the deployment of body sensor networks (BSN) to monitor and sense human activity in pervasive environments. Using multiple wireless on-body systems, such as physiological data monitoring and motion capture systems, body sensor network data consists of heterogeneous physiologic and motoric streams that form a multidimensional framework. In this article, we analyze such high-dimensional body sensor network data by proposing an efficient, multidimensional factor analysis technique for quantifying human performance and, at the same time, providing visualization for performances of participants in a low-dimensional space for easier interpretation.