Biomechanical Validation of Upper-Body and Lower-Body Joint Movements of Kinect Motion Capture Data for Rehabilitation Treatments

New and powerful hardware like Kinect introduces the possibility of changing biomechanics paradigm, usually based on expensive and complex equipment. Kinect is a markerless and cheap technology recently introduced from videogame industry. In this work we conduct a comparison study of the precision in the computation of joint angles between Kinect and an optical motion capture professional system. We obtain a range of disparity that guaranties enough precision for most of the clinical rehabilitation treatments prescribed nowadays for patients. This way, an easy and cheap validation of these treatments can be obtained automatically, ensuring a better quality control process for the patient's rehabilitation.

[1]  R. B. Davis,et al.  A gait analysis data collection and reduction technique , 1991 .

[2]  Gutemberg Guerra-Filho,et al.  Optical Motion Capture: Theory and Implementation , 2005, RITA.

[3]  Bobby Bodenheimer,et al.  The Process of Motion Capture: Dealing with the Data , 1997, Computer Animation and Simulation.

[4]  A. Pedotti,et al.  Functionally oriented and clinically feasible quantitative gait analysis method , 1998, Medical and Biological Engineering and Computing.

[5]  David Geerts,et al.  Videogames in therapy: a therapist's perspective , 2010, Fun and Games '10.

[6]  Miriam Vollenbroek-Hutten,et al.  Chronic pain rehabilitation with a serious game using multimodal input , 2011, 2011 International Conference on Virtual Rehabilitation.

[7]  Lorenzo Chiari,et al.  Human movement analysis using stereophotogrammetry. Part 4: assessment of anatomical landmark misplacement and its effects on joint kinematics. , 2005, Gait & posture.

[8]  Albert A. Rizzo,et al.  Towards pervasive physical rehabilitation using Microsoft Kinect , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[9]  A Leardini,et al.  Position and orientation in space of bones during movement: anatomical frame definition and determination. , 1995, Clinical biomechanics.

[10]  A Leardini,et al.  Position and orientation in space of bones during movement: experimental artefacts. , 1996, Clinical biomechanics.

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

[12]  C. Cobelli,et al.  A Markerless Motion Capture System to Study Musculoskeletal Biomechanics: Visual Hull and Simulated Annealing Approach , 2006, Annals of Biomedical Engineering.

[13]  Marjorie Skubic,et al.  Evaluation of an inexpensive depth camera for in-home gait assessment , 2011, J. Ambient Intell. Smart Environ..

[14]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[15]  J. Henriksson.  Human movement science , 2012, Acta physiologica.

[16]  HiltonAdrian,et al.  A survey of advances in vision-based human motion capture and analysis , 2006 .

[17]  Fraser Anderson,et al.  Lean on Wii: physical rehabilitation with virtual reality Wii peripherals. , 2010, Studies in health technology and informatics.

[18]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[19]  A. Cappozzo,et al.  Human movement analysis using stereophotogrammetry. Part 2: instrumental errors. , 2004, Gait & posture.

[20]  P. Verschure,et al.  The rehabilitation gaming system: a review. , 2009, Studies in health technology and informatics.

[21]  R. Mann,et al.  A comparison of lower-extremity skeletal kinematics measured using skin- and pin-mounted markers , 1997 .

[22]  Tilak Dutta,et al.  Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace. , 2012, Applied ergonomics.