Neural network based bed posture classification enhanced by Bayesian approach

This paper describes bed posture classification by using a Neural Network model for elderly care. Data collected from a sensor panel (composed of piezoelectric sensors and pressure sensors), which is placed under a mattress in the thoracic area, we use Neural Network for posture classification. Bayesian approach is used for estimating the likelihood of consecutive postures. The sensing data are normalized into a range of 0 to 1 by the unity-based normalization (or feature scaling) method for eliminating the bias between the different types of sensors. Also, the accumulated signal data in one second time slots (120-inputs set) can improve the coverage of the trained model. The results from Neural Network and Bayesian network estimation are combined by the weighted arithmetic mean. Our proposed technique is applied to elderly patient data with five different postures i.e., out of bed, sitting, lying down, lying left, and lying right. This resulted in 91.50% accuracy when the proportion of coefficient for Neural Network and Bayesian probability is 0.3 and 0.7 respectively.