A Human Body Posture Identification Algorithm Based on Kinect

This study was performed to improve the computer identify of the human body posture more accurately during human-computer interaction. In order to get a better algorithm, the author applies Kinect for Xbox device, Hausdorff Distance theory and Joint Angle measurement method to identify the human body posture. According to the actual situation, the author modifies the Hausdorff distance to mean Hausdorff distance; and sets one joint point as the reference point to improve the stability of angle measurement system. During the identification process, the Kinect gets the position of the human body joint points, and calculates the Euclidean distance between two joints, then uses cosine theorem to calculate the angle of two ligatures which are connecting three joints to define the body posture. The experimental results show that this algorithm can measure in time joint angles, and identify the body posture accurately. The study has an important reference meaning in human computer interaction. Keywords-posture identification;Mean Hausdorff Distance;angle measurement; Kinect; human-computer interaction

[1]  Jonathan T. Barron,et al.  A category-level 3-D object dataset: Putting the Kinect to work , 2011, ICCV Workshops.

[2]  Norman H. Villaroman,et al.  Teaching natural user interaction using OpenNI and the Microsoft Kinect sensor , 2011, SIGITE '11.

[3]  Linda Denehy,et al.  Validity of the Microsoft Kinect for assessment of postural control. , 2012, Gait & posture.

[4]  Shahram Izadi,et al.  Modeling Kinect Sensor Noise for Improved 3D Reconstruction and Tracking , 2012, 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission.

[5]  Yao-Jen Chang,et al.  A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. , 2011, Research in developmental disabilities.

[6]  Noel E. O'Connor,et al.  Evaluating a dancer's performance using kinect-based skeleton tracking , 2011, ACM Multimedia.

[7]  Stepán Obdrzálek,et al.  Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Ajmal S. Mian,et al.  Using Kinect for face recognition under varying poses, expressions, illumination and disguise , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[9]  Dieter Fox,et al.  RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments , 2012, Int. J. Robotics Res..

[10]  Antonis A. Argyros,et al.  Efficient model-based 3D tracking of hand articulations using Kinect , 2011, BMVC.

[11]  Marjorie Skubic,et al.  Passive in-home measurement of stride-to-stride gait variability comparing vision and Kinect sensing , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.