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This paper presents an analysis of stabilogram using the modified Principal Component Analysis (mPCA) decomposition which will be employed to highlight the effects of different aspects on the human postural stability. The aim of this study is to analyze stabilogram center of pressure time series using the mPCA decomposition method. The mPCA is a decomposition method applied to a complex signal. It decomposes the stabilogram, considered as an additive model, into three components: trend, rambling and trembling. The study of the trace of analytic trembling (respectively of rambling) in the complex plan highlights a unique rotation center. So the phase is defined and two parameters are extracted: the area of the circle in which 95% of the trace's data points are located and the angular frequency. In this study 25 healthy volunteers (average age 31± 11 years) are required to stand upright on an electromagnetic platform either with eyes closed or open and with feet outspread or tighten. Experimental results show the efficiency of the parameter area to identify the effect of visual, proprioceptive and directional entries 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± 7.5y) relatively to young subjects (22.5 ±2. 5y). Results serve also to display that female subjects are more stable than males, that fat subjects are more stable than thin and that tall subjects are more stable than small.

Stabilogram mPCA Decomposition and Effects Analysis of Several Entries on The Postural Stability