Classification of body movements based on posturographic data

Abstract

The human body, standing on two feet, produces a continuous sway pattern. Intended movements, sensory cues, emotional states, and illnesses can all lead to subtle changes in sway appearing as alterations in ground reaction forces and the body’s center of pressure (COP). The purpose of this study is to demonstrate that carefully selected COP parameters and classification methods can differentiate among specific body movements while standing, providing new prospects in camera-free motion identification. Force platform data were collected from participants performing 11 choreographed postural and gestural movements. Twenty-three different displacement- and frequency-based features were extracted from COP time series, and supplied to classification-guided feature extraction modules. For identification of movement type, several linear and nonlinear classifiers were explored; including linear discriminants, nearest neighbor classifiers, and support vector machines. The average classification rates on previously unseen test sets ranged from 67% to 100%. Within the context of this experiment, no single method was able to uniformly outperform the others for all movement types, and therefore a set of movement-specific features and classifiers is recommended.