Sleep Posture Classification Using Bed Sensor Data and Neural Networks

Abstract

Sleep posture has been shown to be important in monitoring health conditions such as congestive heart failure (CHF), sleep apnea, pressure ulcers, and even blood pressure abnormalities. In this paper, we investigate the use of four hydraulic bed transducers placed underneath the mattress to classify different sleep postures. For classification, we employed a simple neural network. Different combinations of parameters were studied to determine the best configuration. Data were collected on four major postures from 58 subjects. We report the results of classification for different combinations of these four postures. Both 10-Fold and Leave-One-Subject-Out (LOSO) Cross-validations (CV) were used to evaluate the accuracy of our predictions. Our results show that there are multiple configuration settings that make classification accuracy as high as 100% using k-Fold CV for all postures. Maximum classification accuracy after applying LOSO is 93% for a two-class classification of separating Left vs. Right lateral positions. The second-best classification accuracy with LOSO is 92% for the classification of lateral versus non-lateral.