Abstract
In clinical rehabilitation, biofeedback increases patient’s motivation making it one of the most effective motor rehabilitation mechanisms. In this field, it is very helpful for the patient and even for the therapist to know the level of success and performance of the training process. New rehabilitation technologies allow new forms of therapy for patients with Range of Motion (ROM) disorders. The aim of this work is to introduce a simple biofeedback system in a clinical environment for ROM measurements, since there is currently a lack of practical and cost-efficient methods available for this purpose. The Microsoft Kinect™ introduces the possibility of low cost, non intrusive human motion analysis in the rehabilitation field. In this paper we conduct a comparison study of the accuracy in the computation of ROM measurements between the Kinect™ Skeleton Tracking provided by Microsoft and the proposed algorithm based on depth analysis. Experimental results showed that our algorithm is able to overcome the limitations of the Microsoft algorithm when the pose estimation is used as a measuring system making it a valuable rehabilitation tool.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lünenburger, L., Colombo, G., Riener, R.: Biofeedback for robotic gait rehabilitation. J. NeuroEng. Rehabil. 4(1), 1 (2007)
Begg, R., Palaniswami, M.: Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques. Idea Group Publishing, Hershey (2006)
Dutta, T.: Evaluation of the kinect sensor for 3-D kinematic measurement in the workplace. Appl. Ergon. 43, 645–649 (2011)
Pece, F., Kautz, J., Weyrich, T.: Three depth-camera technologies compared. In: Engineering in Medicine and Biology Society, pp. 1188–1193 (2012)
Kourosh Khoshelham and Sander Oude Elberink: Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12(2), 1437–1454 (2012)
Clark, R.A., Pua, Y.-H., Fortin, K., Ritchie, C., Webster, K.E., Denehy, L., Bryant, A.L.: Validity of the microsoft kinect for assessment of postural control. Gait & Posture 36(3), 372–377 (2012)
Obdrzálek, Š., Kurillo, G., Ofli, F., Bajcsy, R., Seto, E., Jimison, H., Pavel, M.: Accuracy and robustness of kinect pose estimation in the context of coaching of elderly population. In: Engineering in Medicine and Biology Society, pp. 1188–1193 (2012)
Fernández-Baena, A., Susin, A., Lligadas, X.: Biomechanical validation of upper-body and lower-body joint movements of kinect motion capture data for rehabilitation treatments. In: 2012 4th International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 656–661. IEEE (2012)
Kitsunezaki, N., Adachi, E., Masuda, T., Mizusawa, J.: Kinect applications for the physical rehabilitation. In: 2013 IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA), pp. 294–299. IEEE (2013)
Metsis, V., Jangyodsuk, P., Athitsos, V., Iversen, M., Makedon, F.: Computer aided rehabilitation for patients with rheumatoid arthritis. In: 2013 International Conference on Computing, Networking and Communications (ICNC), pp. 97–102. IEEE (2013)
Beckert, J., Silva, F., Palma, S.: Inter-rater reliability of the visual estimation of shoulder abduction angles and the agreement of measurements with an accelerometer. In: Proceedings of ECSS2009, Oslo, Norway (2009)
Bernmark, E., Wiktorin, C.: A triaxial accelerometer for measuring arm movements. Appl. Ergon. 33(6), 541–547 (2002)
Loomis, A.: Figure Drawing for All It’s Worth. Penguin Group (USA) Incorporated, New York (1943)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barandas, M., Gamboa, H., Fonseca, J.M. (2014). Upper Body Joint Angle Measurements for Physical Rehabilitation Using Visual Feedback. In: da Silva, H., Holzinger, A., Fairclough, S., Majoe, D. (eds) Physiological Computing Systems. PhyCS 2014. Lecture Notes in Computer Science(), vol 8908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45686-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-662-45686-6_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45685-9
Online ISBN: 978-3-662-45686-6
eBook Packages: Computer ScienceComputer Science (R0)