View independent human posture identification using Kinect

• Published in: 2012 5th International Conference on Biomedical Engineering and Informatics pp. 1590 - 1593
• Main Authors: Liu, Yuanning, Zhang, Zequn, Li, Ao, Wang, Minghui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: Accuracy; Data models; Estimation; Feature extraction; Human computer interaction; Kinect; PCA; posture identification; Principal component analysis; Skeleton; Support vector machines; SVM; Training; Vectors; viewpoint idependent
• DOI: 10.1109/BMEI.2012.6513027
• ISBN: 9781467311830; 1467311839

As an important part of human computer interaction (HCI) system, posture identification has been extensively studied over last years. Recently, Microsoft Kinect Senor has become a hot spot for posture identification because it is efficient in acquiring body joint location information. In this study, based on Kinect, we proposed a framework for view independent human posture identification. In this framework, a viewpoint rotation transformation was performed on original skeleton location data and then total 9 features were extracted for building a SVM classifier. About 4200 samples including five postures taken from different body orientations were collected to construct a dataset for performance evaluation. The results of PCA analysis showed that the transformation was efficient in distinguishing different postures. Further analysis demonstrated that this method achieved a superior performance of 98.0% when the orientation angle was between −60° and 60°. These results show that this view independent framework is powerful and efficient in viewpoint invariant posture identification.