Automatic characterization of stroke patients’ posturography based on probability density analysis

Abstract

Objective: The probability density analysis was applied to automatically characterize the center of pressure (COP) data for evaluation of the stroke patients’ balance ability. Methods: The real-time COP coordinates of 38 stroke patients with eyes open and closed during quiet standing were obtained, respectively, from a precision force platform. The COP data were analyzed and characterized by the commonly used parameters: total sway length (SL), sway radius (SR), envelope sway area (EA), and the probability density analysis based parameters: projection area (PA), skewness (SK) and kurtosis (KT), and their statistical correlations were analyzed. The differences of both conventional parameters and probability density parameters under the conditions of eyes open (EO) and eyes closed (EC) were compared. Results: The PA from probability density analysis is strongly correlated with SL and SR. Both the traditional parameters and probability density parameters in the EC state are significantly different from those in the EO state. The obtained various statokinesigrams were calculated and categorized into typical sway types through probability density function for clinical evaluation of the balance ability of stroke patients. Conclusions: The probability density analysis of COP data can be used to characterize the posturography for evaluation of the balance ability of stroke patients.