A Kinect based gesture recognition algorithm using GMM and HMM

Author

Yang Song ; Yu Gu ; Peisen Wang ; Yuanning Liu ; Ao Li

Author_Institution

Sch. of Inf. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China

fYear

2013

fDate

16-18 Dec. 2013

Firstpage

750

Lastpage

754

Abstract

Gesture recognition is a quite promising field in robotics and many Human-Computer Interaction (HCI) related areas. This research uses Microsoft® Kinect to capture the 3D position data of joints, and uses Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) to model full-body gestures. We propose a gesture recognition algorithm to segment gestures from real-time data flow, and finally achieved to recognize predefined full-body gestures in real-time. This proposed method gives a high recognition rate of 94.36%, indicating the capability of the new method.

Keywords

Gaussian processes; data flow analysis; gesture recognition; hidden Markov models; human computer interaction; image segmentation; medical image processing; mixture models; principal component analysis; 3D position data; GMM; Gaussian mixture model; HCI; HMM; Kinect based gesture recognition algorithm; Microsoft Kinect; full-body gestures; hidden Markov model; human-computer interaction related areas; image segmentation; real-time data flow; robotics; Accuracy; Feature extraction; Gesture recognition; Hidden Markov models; Joints; Principal component analysis; GMM; Gesture recognition; HMM; Kinect; PCA;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2013 6th International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4799-2760-9

Type

conf

DOI

10.1109/BMEI.2013.6747040

Filename

6747040