Recognizing human motions through mixture modeling of inertial data

作者：

Highlights：

• 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.

摘要

Highlights•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.

论文关键词：Human motion,Classification,Recognition,Segmentation,Inertial sensors,Gaussian mixture model,Minimum message length,Dynamic time warping

论文评审过程：Received 21 December 2012, Revised 3 January 2015, Accepted 5 March 2015, Available online 12 March 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.03.004