Bayesian classification for bed posture detection based on kurtosis and skewness estimation

Abstract

This study proposes a bed posture detection method using Bayesian classification for the elderly and bedridden. Only 16 long-narrow FSR (Force Sensing Resistor) sensors, rather than pressure distribution image from a set of sensor array are used for classification. Kurtosis and skewness are estimated as feature vector to represent the shape of pressure contour using the pressure values received from sensors. Gaussian distribution is adopted for statistical modeling and classification for bed postures including supine, left/right lying. Experimental results reveal that proposed method exhibits encouraging potential in bed posture detection.