Abstract
In order to solve the physical rehabilitation of stroke patients with apoplectic hemiplegia, an intelligent rehabilitation supporting system based on Kinect device is designed. The system uses Kinect device to capture the user’s limb joints point information. DTW algorithm is combined with RANSAC algorithm to match the action in standard action library, thus the system can obtain the evaluation results. Then the patients’ rehabilitation data is sent to the doctors with new treatment options intelligently. As a result, the experimental results show that this physical rehabilitation method is more convenient and efficient than the traditional training method.
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Brian, M., Zak, M., Caroline, A.: Utilizing explorable visual environments for experiential applications. Procedia Comput. Sci. 8, 261–266 (2012)
Boulos, M.N., Blanchard, B.J., Walker, C., et al.: Web GIS in practice X: a Microsoft Kinect natural user interface for Google Earth navigation. Int. J. Health Geogr. 10, 45–59 (2011)
Michael, Z., Michael, M., Günther, G., et al.: Automatic reconstruction of personalized avatars. Comput. Anim. Virtual Worlds 22(1), 195–202 (2011)
Luigi, G., Alessio, P.P., Mario, C.: Controller-free exploration of medical image data: experiencing the Kinect. In: 24th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6. IEEE, Bristol, Britain (2011)
Chang, C.-Y., Lange, B., Zhang, M., et al.: Towards pervasive physical rehabilitation using Microsoft Kinect. In: International Conference on Pervasive Computing Technologies for Healthcare and Workshops, pp. 159–162 (2012)
Chang, Y.J., Chen, S.F., Chuang, A.F.: A gesture recognition system to transition autonomously through vocational tasks for individuals with cognitive impairments. Res. Dev. Disabil. 32, 2064–2068 (2011)
Jiang, H., Xiang, J.: Kinect rehabilitation assistance system research. Artif. Intell. Robot. Res. 2, 79–82 (2013)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)
Chen, F., Wang, R.: Based on the consistency and rapid sampling algorithm. J. Softw. 16(8), 1431–1437 (2005)
Sempena, S., Maulidevi, N.U., Aryan, P.R.: Human action recognition using dynamic time warping. In: Proceedings of 2011 International Conference on Electrical Engineering and Informatics (ICEEI), pp. 1–5 (2011)
Yamato, J., Ohya, J., Ishii, K.: Recognizing human action in time sequential images using hidden Markov model. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 379–385. IEEE (1992)
Yu, X.Y., Wu, L.Y., Liu, Q.Y., et al.: Children tantrum behaviour analysis based on Kinect sensor. In: Third Chinese Intelligent Visual Surveillance (IVS) Conference, Beijing, China, pp. 49–52 (2011)
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Liu, H., Ma, H., Gu, J., Wu, F., Lv, J. (2016). A Rehabilitation Planning Based on Kinect Somatosensory Recognition and Cloud Computing. In: Pan, Z., Cheok, A., Müller, W., Zhang, M. (eds) Transactions on Edutainment XII. Lecture Notes in Computer Science(), vol 9292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-50544-1_7
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DOI: https://doi.org/10.1007/978-3-662-50544-1_7
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