Multimodal Sensing for Pediatric Obesity Applications

Abstract

In this paper, a wireless body area network comprised of heterogeneous sensors is developed for wearable health monitoring applications. The ultimate application space is in the context of pediatric obesity. The specific task examined herein is activity detection based on heart rate monitor and accelerometer data. Based on statistical analysis of experimental data for different key states (lying down, sitting, standing, walking and running), a multimodal detection strategy is proposed. The resulting detector can achieve 85-95% accuracy in state detection. It is observed that the accelerometer is more informative for the active states, while the heart rate monitor is more informative for the passive states.