A novel depth image analysis for sleep posture estimation

Abstract

Recognition of sleep posture and its changes are related to information monitoring in a number of health-related applications such as apnea prevention and elderly care. This paper uses a less privacy-invading approach to classify sleep postures of a person in various configurations including side and supine postures. In order to accomplish this, a single depth sensor has been utilized to collect selective depth signals and populated a dataset associated with the depth data. The data is then analyzed by a novel frequency-based feature selection approach. These extracted features were then correlated in order to rank their information content in various 2D scans from the 3D point cloud in order to train a support vector machine (SVM). The data of subjects are collected under two conditions. First when they were covered with a thin blanket and second without any blanket. In order to reduce the dimensionality of the feature space, a T-test approach is employed to determine the most dominant set of features in the frequency domain. The proposed recognition approach based on the frequency domain is also compared with an approach using feature vector defined based on skeleton joints. The comparative studies are performed given various scenarios and by a variety of datasets. Through our study, it is shown that our proposed method offers better performance to that of the joint-based method.