Statistical recognition of breathing by MS Kinect depth sensor

Measuring of breathing with contact methods, like respiratory belts, is very uncomfortable for patients and in case of complex sleep analysis, cables from different sensors can substantially affect the quality of the sleep. This paper presents the contactless measuring of breathing using the MS Kinect depth sensor, and it compares the results obtained with records of breathing observed by the flowmetry. The methodological part of the paper is devoted to spectral analysis of data acquired, feature extraction, and their Bayesian classification. The proposed classifier is able to distinguish the Sleep and Wake classes with the accuracy of 100% (cross-validation: 0) for given data. The achieved accuracy of classification into 3 classes (Sleep, Falling Asleep and Wake) is 97% (cross-validation: 0.0248) in the given case.