A Wearable Accelerometer Array for Human Movement Analysis Based on rBSN Approach

摘要

In this paper, we introduce a wearable accelerometer array system and propose a method for analysis of human movements. While many studies of sensor networks have aimed at scalability and/or accuracy, our concept is to introduce "redundancy" in the array to measure movements in human bodies. In particular, we gather data from as many points as possible, more than kinematic degrees of freedom, and then obtain various information by data mining. We developed an accelerometer array system based on our "redundant numbered BSN (rBSN)" concept. It measures 46 channels of 3-axis (15 channels of 6-axis) acceleration data in 1500Hz synchronously. We also evaluate our accelerometer array system in two applications discuss the applicability of our rBSN concept. Specifically, we describe methods to estimate arm stiffness as well as center of gyration of human movement.