Clustering of human motions based on feature-level fusion of multiple body sensor data

Authors:
Gaurav N. Pradhan;B. Prabhakaran
Affiliations:
Arizona State University, Tempe, AZ, USA;University of Texas at Dallas, Richardson, TX, USA
Venue:
Proceedings of the 1st ACM International Health Informatics Symposium
Year:
2010

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Abstract

Human joints and muscles are the crucial biomechanical factors that control and drive body movements. Hence, assessing the role of joint movements and muscular activities is important for analyzing human motions. This quantification of integrated biomechanical kinematics with electrophysiology is useful for gait and posture analysis, prosthetic design and other orthopedic applications. We study the effect of integrating multiple body sensor data on the clustering of human motions by fusing their corresponding relevant features extracted from the raw data. In this paper, our objective is to perform cluster analysis on human motions based on the fused feature vectors that underline the joint relationship between body movements and muscular activity.