A Real Time Biofeedback System Using Visual User Interface for Physical Rehabilitation

This study was undertaken to investigate the effectiveness of biofeedback training when compared to conventional physical rehabilitation. Real time biofeedback plays an important role for patients and therapists to assess the success and performance of the training process. Visual user interface allows new forms of therapy simplifying the process and increasing patients’ motivation. An experimental investigation was conducted to study the effect of using a visual interface as an add-on therapy to standard exercises for Range of Motion (ROM) measurements in glenohumeral movements. Human movement was collected by a Kinect sensor and the ROM measurements were computed using spatial coordinates provided by the official Microsoft Kinect SDK. The design allows patients to train therapeutic exercises, while receiving different types of real time feedback indicating measures of performance evaluation through the course of the training. Moreover, both the environment and the training task are customizable to the patient needs. In order to evaluate the biofeedback effectiveness, subjects that participated in the study were required to do a therapeutic exercise twice: firstly following the instructions of the therapist and secondly adding the visual biofeedback to the therapist instructions. The results obtained suggest that the proposed visual interface was an effective tool to achieve a significant improvement in the performance of the exercise. The exercises correctness when performed with visual feedback was significantly higher than the same exercises performed without this visual stimulus. In trials where subjects received visual feedback, it was observed a greater effort to achieve the proposed objective and superior movement control. Given the potential benefits of biofeedback, the proposed interface can become a helpful tool to patients that can confirm the correctness of their exercises and therapists that can adapt the prescribed exercises considering patients’ evolution and performance.

[1]  Takashi Masuda,et al.  KINECT applications for the physical rehabilitation , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[2]  Guodong Guo,et al.  Assessing Spinal Loading Using the Kinect Depth Sensor: A Feasibility Study , 2013, IEEE Sensors Journal.

[3]  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.

[4]  Vangelis Metsis,et al.  Computer aided rehabilitation for patients with rheumatoid arthritis , 2013, 2013 International Conference on Computing, Networking and Communications (ICNC).

[5]  Jake K. Aggarwal,et al.  Human detection using depth information by Kinect , 2011, CVPR 2011 WORKSHOPS.

[6]  Bryan Buchholz,et al.  ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion--Part II: shoulder, elbow, wrist and hand. , 2005, Journal of biomechanics.

[7]  P. Olivier,et al.  Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease. , 2014, Gait & posture.

[8]  R. Riener,et al.  Journal of Neuroengineering and Rehabilitation Open Access Biofeedback for Robotic Gait Rehabilitation , 2022 .

[9]  Ian M Franks,et al.  Enhancement of motor rehabilitation through the use of information technologies. , 2006, Clinical biomechanics.

[10]  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.

[11]  Francesco Piccione,et al.  Virtual Environment Training Therapy for Arm Motor Rehabilitation , 2005, Presence: Teleoperators & Virtual Environments.

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

[13]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[14]  Timothy D. Lee,et al.  Motor Control and Learning: A Behavioral Emphasis , 1982 .

[15]  Jacqui Crosbie,et al.  Adaptive Virtual Reality Games for Rehabilitation of Motor Disorders , 2007, HCI.

[16]  H. Hermens,et al.  Effect of augmented feedback on motor function of the affected upper extremity in rehabilitation patients: a systematic review of randomized controlled trials. , 2005, Journal of rehabilitation medicine.

[17]  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.