Abstract
With the development of human computer interaction technology, the computer virtual reality technology makes people’s body language signals by simulating the human body language and other natural means of communication, so as to drive instruction of human-computer interaction. Therefore, demand for such a natural and harmonious interaction, the voice, face, gesture recognition is an important issue, identify the effect of experience is critical for interactive applications. The paper presents the forearm rehabilitation system based on vision-based gesture recognition, including pre-processing of images collected by the camera, gesture segmentation based on skin color information, gesture recognition based on hand shape feature. This system can effectively extract the gesture area, combined with the capture sphere of action to achieve rehabilitation exercise on the forearm.
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References
Yoon, H.S., Soh, J., Bae, Y.J., Yang, H.S.: Hand gesture recognition using combined features of location, angle and velocity. Pattern Recogn. 34(7), 1491–1501 (2001)
Liu, N., Lovel, B., Kootsookos, P.: Evaluation of hmm training algorithms for letter hand gesture recognition. In: American: Proceedings of the 2003 IEEE International Symposium on Signal Processing and Information Technology (2003)
Dardas, N.H., Georganas, N.D.: Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans. Instrum. Measur. 60(11), 3592–3607 (2011)
Freeman, W., Roth, M.:. Orientation histograms for hand gesture recognition. In: American: Proceedings of International Workshop on Automatic Face and Gesture Recognition (1994)
Zarit, B.D., Super, B.J.: Comparison of five color models in skin pixel classification. In: Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-time Systems, Corfu, Greece, pp. 58–63 (1999)
Jayaram, S., Schmugge, S.:. Effect of color space transformation, the illuminance component and color modeling on skin detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), Washington, DC, USA, pp. 813–818 (2004)
Tenilion, J.C., Shirazi, M.N., Fukamachi, H.: Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. In: Proceedings of the Conference of Automatic Face and Gesture Recognition, Grenoble, France, pp. 54–61 (2003)
Acknowledgment
This study was supported by the Science and Technology of Jilin province development plan projects with grants No. 20170204023GX, the Education Department of Jilin Province with grants No. 2016292, No. JJKH20170496KJ, the spring plan of Ministry of Education with grants Z2016013.
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Zhu, D., Li, Z., Huang, K., Reika, S. (2018). Design and Implementation of the Forearm Rehabilitation System Based on Gesture Recognition. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_27
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DOI: https://doi.org/10.1007/978-981-13-2206-8_27
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