Open Access

Realtime Motion Assessment For Rehabilitation Exercises: Integration Of Kinematic Modeling With Fuzzy Inference


Cite

[1] W. Zhao, H. Feng, R. Lun, D. D. Espy, and M. Reinthal, “A kinect-based rehabilitation exercise monitoring and guidance systems,” in Proceedings of the 5th IEEE International Conference on Software Engineering and Service Science. IEEE, 2014, pp. 762–765.10.1109/ICSESS.2014.6933678Search in Google Scholar

[2] C.-J. Su, “Personal rehabilitation exercise assistant with kinect and dynamic time warping,” International Journal of Information and Education Technology, pp. 448–454, 2013.10.7763/IJIET.2013.V3.316Search in Google Scholar

[3] L. Zhang, J.-C. Hsieh, and J. Wang, “A kinectbased golf swing classification system using hmm and neuro-fuzzy,” in Computer Science and Information Processing (CSIP), 2012 International Conference on, 2012, pp. 1163–1166.Search in Google Scholar

[4] E. Velloso, A. Bulling, and H. Gellersen, “Motionma: motion modelling and analysis by demonstration,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2013, pp. 1309–1318.10.1145/2470654.2466171Search in Google Scholar

[5] W. Zhao, R. Lun, D. D. Espy, and M. A. Reinthal, “Rule based realtime motion assessment for rehabilitation exercises,” in Proceedings of the IEEE Symposium on Computational Intelligence in Healthcare and e-Health, December 2014, pp. 133–140.10.1109/CICARE.2014.7007845Search in Google Scholar

[6] Y. Visell and J. Cooperstock, “Enabling gestural interaction by means of tracking dynamical systems models and assistive feedback,” in Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on. IEEE, 2007, pp. 3373–3378.10.1109/ICSMC.2007.4414093Search in Google Scholar

[7] P. Hong, M. Turk, and T. S. Huang, “Gesture modeling and recognition using finite state machines,” in Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on. IEEE, 2000, pp. 410–415.Search in Google Scholar

[8] P. Turaga, R. Chellappa, V. S. Subrahmanian, and O. Udrea, “Machine recognition of human activities: A survey,” Circuits and Systems for Video Technology, IEEE Transactions on, vol. 18, no. 11, pp. 1473–1488, 2008.Search in Google Scholar

[9] S. Nomm and K. Buhhalko, “Monitoring of the human motor functions rehabilitation by neural networks based system with kinect sensor,” in Analysis, Design, and Evaluation of Human-Machine Systems, vol. 12, no. 1, 2013, pp. 249–253.10.3182/20130811-5-US-2037.00062Search in Google Scholar

[10] R. Poppe, “A survey on vision-based human action recognition,” Image and vision computing, vol. 28, no. 6, pp. 976–990, 2010.10.1016/j.imavis.2009.11.014Search in Google Scholar

[11] J.-S. Lin and D. Kulic, “Online segmentation of human motion for automated rehabilitation exercise analysis,” Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 22, no. 1, pp. 168–180, 2014.10.1109/TNSRE.2013.225964023661321Search in Google Scholar

[12] S. Schaal, A. Ijspeert, and A. Billard, “Computational approaches to motor learning by imitation,” Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, vol. 358, no. 1431, pp. 537–547, 2003.Search in Google Scholar

[13] N. Gordon, B. Ristic, and S. Arulampalam, “Beyond the kalman filter: Particle filters for tracking applications,” Artech House, London, 2004.Search in Google Scholar

[14] R. A. Clark, Y.-H. Pua, K. Fortin, C. Ritchie, K. E. Webster, L. Denehy, and A. L. Bryant, “Validity of the microsoft kinect for assessment of postural control,” Gait and posture, vol. 36, no. 3, pp. 372–377, 2012.10.1016/j.gaitpost.2012.03.03322633015Search in Google Scholar

[15] R. A. Clark, Y.-H. Pua, A. L. Bryant, and M. A. Hunt, “Validity of the microsoft kinect for providing lateral trunk lean feedback during gait retraining,” Gait & posture, vol. 38, no. 4, pp. 1064–1066, 2013.Search in Google Scholar

[16] A. Bo, M. Hayashibe, P. Poignet et al., “Joint angle estimation in rehabilitation with inertial sensors and its integration with kinect,” in EMBC’11: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 3479–3483.10.1109/IEMBS.2011.609094022255089Search in Google Scholar

[17] E. Akdoğan, E. Taçgın, and M. A. Adli, “Knee rehabilitation using an intelligent robotic system,” Journal of Intelligent Manufacturing, vol. 20, no. 2, pp. 195–202, 2009.10.1007/s10845-008-0225-ySearch in Google Scholar

[18] Q. Wang, P. Turaga, G. Coleman, and T. Ingalls, “Somatech: an exploratory interface for altering movement habits,” in CHI’14 Extended Abstracts on Human Factors in Computing Systems. ACM, 2014, pp. 1765–1770.10.1145/2559206.2581339Search in Google Scholar

[19] B. C. Bedregal, A. C. Costa, and G. P. Dimuro, “Fuzzy rule-based hand gesture recognition,” in Artificial Intelligence in Theory and Practice. Springer, 2006, pp. 285–294.10.1007/978-0-387-34747-9_30Search in Google Scholar

[20] T. Hachaj and M. R. Ogiela, “Rule-based approach to recognizing human body poses and gestures in real time,” Multimedia Systems, vol. 20, no. 1, pp. 81–99, 2014.10.1007/s00530-013-0332-2Search in Google Scholar

[21] W. Zhao, D. D. Espy, M. Reinthal, and H. Feng, “A feasibility study of using a single kinect sensor for rehabilitation exercises monitoring: A rule based approach,” in Computational Intelligence in Healthcare and e-health (CICARE), 2014 IEEE Symposium on. IEEE, 2014, pp. 1–8.10.1109/CICARE.2014.7007827Search in Google Scholar

eISSN:
2083-2567
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Databases and Data Mining, Artificial Intelligence