Advanced SearchSearch Tips
A Remote Rehabilitation System using Kinect Stereo Camera
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Remote Rehabilitation System using Kinect Stereo Camera
Kim, Kyungah; Chung, Wan-Young; Kim, Jong-Jin;
  PDF(new window)
Rehabilitation exercises are the treatments designed to help patients who are in the process of recovery from injury or illness to restore their body functions back to the original status. However, many patients suffering from chronic diseases have found difficulties visiting hospitals for the rehabilitation program due to lack of transportation, cost of the program, their own busy schedules, etc. Also, the program usually contains a few medical check-ups which can cause patients to feel uncomfortable. In this paper, we develop a remote rehabilitation system with bio-signals by a stereo camera. A Kinect stereo camera manufactured by Microsoft corporation was used to recognize the body movement of a patient by using its infrared(IR) camera. Also, we detect the chest area of a user from the skeleton data and process to gain respiratory status. ROI coordinates are created on a user`s face to detect photoplethysmography(PPG) signals to calculate heart rate values from its color sensor. Finally, rehabilitation exercises and bio-signal detecting features are combined into a Windows application for the cost effective and high performance remote rehabilitation system.
Photopletysmography(PPG);Rehabilitation;Bio-signal;Motion recognition;
 Cited by
B. Pavya, M.-C. Ilioub, B. Verges-Patoisc, R. Briond, C. Monperee, F. Carref, P. Aeberhardg, C. Argouachh, A. Borgnei, S. Consolij, S. Coronek, M. Fischbachl, L. Fourcadem, J.-M. Lecerfn, C. Mounier-Vehiero, F. Paillardf, B. Pierrep, B. Swynghedauwq, Y. Theodoser, D. Thomass, F. Claudott, A. Cohen-Solalq, H. Douardu, D. Marcadetv and Exercise, Rehabilitation Sport Group (GERS), "French Society of Cardiology guidelines for cardiac rehabilitation in adults", Archives of Cardiovascular Diseases, Vol. 105, No. 5, pp. 309-328, 2012. crossref(new window)

2. (retrieved on Feb. 11, 2016).

J. Park, J. Cho, T. Nam and J. Choi, "A unconstrained multi-channel heart rate monitoring system for exercising rehabilitation patients", 29th Annual International Conf. of the IEEE, Engineering in Medicine and Biology Society, pp. 3512-3515, Lyon, France, 2007.

C. Wang, L. Wang, J. Qin, Z. Wu, L. Duan, Z. Li, X. Ou, Weiguangli, Z. Lu, M. Li, Y. Wang, J. Long, M. Huang and Q. Wang, "Development of a novel finger and wrist rehabilitation robot for finger and wrist training", TENCON 2015 - 2015 IEEE Region 10 Conf., pp. 1-5, Macao, China, 2015.

A. Koenig, A. Caruso, M. Bolliger, L. Somaini, X. Omlin, M. Morari and R. Riener, "Model-based heart rate control during robot-assisted gait training", 2011 IEEE International Conf. on Robotics and Automation, pp. 4151-4156, Shanghai, China, 2011.

R. B. Ambar, H. B. M. Poad, A. M. B. M. Ali, M. S. B. Ahmad and M. M. B. A. Jamil, "Multi-sensor arm rehabilitation monitoring device", 2012 International Conf. on Biomedical Engineering (ICoBE), pp. 424-429, Penang, Malaysia, 2012.

C.-K. Tey, Y.-S. Lee and W.-Y. Chung, "Healthcare monitoring system combined with noncontact kinect-based rehabilitation for outpatients", KISPS Summer Conf. 2014, pp.27-28, Gyeongsan, Korea, 2014.

C.-L. Lai, Y.-L. Huang, T.-K. Liao, C.-M. Tseng, Y.-F. Chen and D. Erdenetsogt, "A microsoft kinect-based virtual rehabilitation system to train balance ability for stroke patients", 2015 International Conference on Cyberworlds (CW), pp.54-60, Gotland, Sweden, 2015.

C.-M. Tseng, C.-L. Lai, D. Erdenetsogt and Y.-F. Chen, "Microsoft kinect based virtual rehabilitation system", 2014 International Symposium on Computer, Consumer and Control (IS3C), pp.934-937, Taichung, Taiwan, 2014.

R. Banerjee, A. Sinha, A. D. Choudhury and A. Visvanathan, "PhotoECG: Photoplethysmographyto estimate ECG parameters", 2014 IEEE International Conf. on Acoustic, Speech and Signal Processing (ICASSP), pp. 4404-4408, Florence, Italy, 2014.

A. B. Hertzman, "The blood supply of various skin areas as estimated by the photoelectric plethysmography", AM. J. physiol., Vol. 124, pp. 329-340, 1938.

T.-H. Lu, H.-C. Lin, Y.-H. Lee, R.-R. Chen, H.-L. Chen, S.-Y. Chang, J.-D. Chen, B.-R. Wu and T.-H. Wu "A Motion-Sensing Enabled Personalized Exercise System for Cardiac Rehabilitation", 2012 IEEE 14th International Conf. on e-Health Networking, Applications and Services (Healthcom), pp. 167-171, Beijing, China, 2012.

J. Park, J. Cho, J. Choi and T. Nam, "A zigbee network-based multi-channel heart rate monitoring system for exercising rehabilitation patients", TENCON 2007 - 2007 IEEE Region 10 Conf., pp. 1-4, Taipei, Taiwan, 2007.

W. Verkruysse, L. O Svaasand, and J S. Nelson, "Remote plethysmographic imaging using ambient light", Opt. Express, Vol. 16, pp. 21434-21445, 2008. crossref(new window)

W. J. Jiang, S. C. Gao, P. Wittek and L. Zhao, "Real-time Quantifying Heart Beat Rate from Facial Video Recording on a Smart Phone using Kalman Filters", 2014 IEEE 16th International Conf. on e-Health Networking, Applications and Services (Healthcom), pp. 393-396, Natal, Brazil, 2014.

R. E. Kalman, "A new approach to linear filtering and prediction problems", J. of Basic Engineering, Vol. 82, No. 1, pp. 35-45, 1960. crossref(new window)

17. on Jan. 10, 2016).