Recognizing human motions through mixture modeling of inertial data
A method is proposed for unsupervised segment clustering of human motion capture data. Gaussian mixture models and dynamic time warping are used to compare similar data sequences. Human motion capture data was collected with a set of body-worn inertial sensors. The resultant classifier is compared with k-nearest-neighbor and support vector machine approaches.
human motion segmentation gaussian mixture model dynamic time warping inertial sensors classification recognition minimum message length