Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applications

Encouraging rehabilitation by the use of technology in the home can be a cost-effective strategy, particularly if consumer-level equipment can be used. We present a clinical qualitative and quantitative analysis of the pose estimation algorithms of a typical consumer unit (Xbox One Kinect), to assess its suitability for technology supervised rehabilitation and guide development of future pose estimation algorithms for rehabilitation applications. We focused the analysis on upper-body stroke rehabilitation as a challenging use case. We found that the algorithms require improved joint tracking, especially for the shoulder, elbow and wrist joints, and exploiting temporal information for tracking when there is full or partial occlusion in the depth data.

[1]  Toby Sharp,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR.

[2]  Darryl Charles,et al.  Optimising engagement for stroke rehabilitation using serious games , 2009, The Visual Computer.

[3]  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.

[4]  David Webster,et al.  Experimental evaluation of Microsoft Kinect's accuracy and capture rate for stroke rehabilitation applications , 2014, 2014 IEEE Haptics Symposium (HAPTICS).

[5]  Albert A. Rizzo,et al.  Adapting user interfaces for gestural interaction with the flexible action and articulated skeleton toolkit , 2013, Comput. Graph..

[6]  Lin Yang,et al.  Evaluating and Improving the Depth Accuracy of Kinect for Windows v2 , 2015, IEEE Sensors Journal.

[7]  W. Kabsch.  A solution for the best rotation to relate two sets of vectors , 1976 .

[8]  Joel C. Perry,et al.  Improving patient motivation in game development for motor deficit rehabilitation , 2008, ACE '08.

[9]  Adso Fernández-Baena,et al.  Biomechanical Validation of Upper-Body and Lower-Body Joint Movements of Kinect Motion Capture Data for Rehabilitation Treatments , 2012, 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems.

[10]  Bernadette A. Thomas,et al.  Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 , 2012, The Lancet.

[11]  Ruzena Bajcsy,et al.  Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect , 2015, 2015 International Conference on Healthcare Informatics.

[12]  Ruzena Bajcsy,et al.  Evaluation of upper extremity reachable workspace using Kinect camera. , 2013, Technology and health care : official journal of the European Society for Engineering and Medicine.

[13]  Raymond W. McGorry,et al.  The validity of the first and second generation Microsoft Kinect™ for identifying joint center locations during static postures. , 2015, Applied ergonomics.

[14]  Winson C.C. Lee,et al.  Evaluation of the Microsoft Kinect as a clinical assessment tool of body sway. , 2014, Gait & posture.

[15]  R. Macko,et al.  Testing a Model of Post‐Stroke Exercise Behavior , 2006, Rehabilitation nursing : the official journal of the Association of Rehabilitation Nurses.

[16]  Saeed Behzadipour,et al.  Accuracy of Kinect’s skeleton tracking for upper body rehabilitation applications , 2014, Disability and rehabilitation. Assistive technology.

[17]  Pedro Miguel Moreira,et al.  Serious games for rehabilitation: A survey and a classification towards a taxonomy , 2010, 5th Iberian Conference on Information Systems and Technologies.