Effect analysis of age and gender on postural stability using PCA decomposition

This paper presents an analysis of stabilogram using the Principal Component Analysis (PCA) decomposition. It shows also the effects of different aspects on the human postural stability. The stabilogram measures either lateral displacement or forward-backward displacement of a subject. These measurements are taken to quantify posture while standing in one of four controlled positions. By using Principal Component Analysis (PCA), the stabilogram is decomposed into three components with biological meaning. The components are trend, rambling and trembling. This paper proposes to create analytic signals for rambling (deterministic) and trembling (random) and use the resulting complex trajectories to identify the effect of age and gender on postural stability. The proposed method employs a signal analysis front end (PCA analysis) and a signal interpretation backend (clustering of complex trajectories). Experimental results show the efficiency of the PCA analysis to identify the effect of age and gender on the postural stability. These results are able to discriminate between control and young groups and indicate a less well-controlled posture for control subjects (34.5 ± 7.5yrs) relatively to young subjects (22.5 ± 2.5yrs). Results are also able to discriminate between female subjects and male subjects and indicate a less well-controlled posture for female subjects relatively to males.