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This paper describes a new approach of the non-contact capturing method of breathing activities using the Kinect depth sensor. To process the data, we utilized feature extraction on time series of mean depth value and optional feature reduction step. The next process implemented a machine learning algorithm to execute clustering on the resulted data. The classification had been realized on four different subjects and then, continued to use 10-fold cross-validation and Support Vector Machine (SVM) classifier. The most efficient classifier is SVM radial with the grid reached the best accuracy for all of the subjects.

Clustering and Classification of Breathing Activities by Depth Image from Kinect