一种手指姿态识别系统设计
Design of Finger Gesture Recognition System
DOI: 10.12677/CSA.2016.611080, PDF,    国家自然科学基金支持
作者: 熊宏锦：海军装备部驻重庆地区军事代表局，重庆；苑秉成：海军工程大学兵器工程系，湖北 武汉；熊鹏文*, 任倩茹, 张发辉：南昌大学信息工程学院，江西 南昌
关键词: 姿态识别；手指康复；多分类支持向量机；Gesture Recognition； Finger Rehabilitation； Multi Classification Support Vector Machine
摘要: 手指康复运动具有高移动精度与高控制分辨率的需求，加上因人而异的数据多样性，以及在运动过程中的模糊信号识别等问题，实时准确的手指姿态识别能够大大提高手指的康复效果。针对这一问题，本文提出了一种简易、便携的手指姿态识别系统设计。使用多分类支持向量机对手部运动进行分析和识别，大量训练者所采集到的数据分为离线训练集和在线测试集，经过大量的离线训练与在线测试，结果表明，多分类支持向量机在分类和识别过程中具有高效性和实用性，并且极大可能有助于手指的康复过程。
Abstract: With high precision and high resolution of the mobile control demand, plus the data, it differs from man to man. Diversity, and in the process of movement of fuzzy signal recognition, real-time and accurate finger gesture recognition can greatly improve the effect of rehabilitation of finger. To solve this problem, this paper presents a simple, portable finger gesture recognition system design. The use of multi class support vector machine hand motion analysis and recognition, a large number of training data collected is divided into offline and online training set test set; after the test, a large number of online and offline training results show that the multi class support vector machine is efficient and practical in classification and recognition in the process of the rehabilitation process and which may contribute to the finger.
文章引用：熊宏锦, 苑秉成, 熊鹏文, 任倩茹, 张发辉. 一种手指姿态识别系统设计[J]. 计算机科学与应用, 2016, 6(11): 648-656. https://dx.doi.org/10.12677/CSA.2016.611080

参考文献

[1] 吴军. 上肢康复机器人及相关控制问题研究[D]: [博士学位论文]. 武汉: 华中科技大学, 2012.
[2] 刑科新. 手功能康复机器人系统若干关键技术研究[D]: [博士学位论文]. 武汉: 华中科技大学, 2010.
[3] Qin, X.Y. (2011) Application of Rehabilitation Equipment in Functional Rehabilitation of Hemiplegia. Journal of Clinical Rehabilitative Tissue Engineering Research, 15, 9088-9092.
[4] 王茂斌. 脑卒中康复研究的进展[J]. 中国康复医学杂志, 2001, 16(5): 264-265.
[5] 甘增康. 手部功能康复机器人电气控制系统的设计与研究[D]: [硕士学位论文]. 哈尔滨: 哈尔滨工业大学, 2011.
[6] Polotto, A., Modulo, F., Flumian, F., Xiao, Z.G., Boscariol, P. and Menon, C. (2012) Index Finger Rehabilitation/Assis- tive Device. IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob, 1518-1523.
https:/doi.org/10.1109/biorob.2012.6290676
[7] Lee, J.S., Lee, E.H., et al. (2014) Hand Region Extraction and Gesture Recognition from Video Stream with Complex Background through Entropy Analysis. IEEE Annual International Conference on Engineering in Medicine and Bioblgy Society, 1513-1516.
[8] Mohamaddan, S. and Komeda, T. (2010) Wire-Driven Mechanism for Finger Rehabilitation Device. International Conference on Mechatronics and Automation (ICMA), Xi’an, 1015-1018.
[9] Yu, C., Wang, X., Huang, H., et al. (2010) Vision-Based Hand Gesture Recognition Using Combinational Features. Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 543- 546.
[10] Ding, Y.D., Pang, H.B. and Wu, X.C. (2011) Recognition of Hand-Gesture Using Improved Local Binary Pattern. International Conference on Multimedia Technolo-gy.
[11] http://news.ifeng.com/gundong/detail_2012_04/27/14196749_0.shtml
[12] http://www.techtrend24.com/microsoft-developing-kinect-like-controller-that-uses-sound-wave
[13] http://china.rs-online.com/web/p/microcontrollers/7926050/
[14] http://easydatasheet.cn/search/stm32f407ZGT6
[15] Chapelle, O., Vapnik, V. and Bousquet, O. (2002) Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 46, 131-159.
https:/doi.org/10.1023/A:1012450327387
[16] 邓乃扬, 田英杰. 数据挖掘中的新方法——支持向量机[M]. 北京: 科学出版社, 2004.
[17] Huang, C., Davis, L.S. and Townshend, J. (2002) An Assessment of Support Vector Machines for Land Cover Classification. International Journal of Remote Sensing, 23, 725-749.
https:/doi.org/10.1080/01431160110040323
[18] 张学工. 关于统计学习理论与支持向量机[J]. 自动化学报, 2000, 26(1): 32-42.
[19] Foody, G. and Mathur, A. (2004) A Relative Evaluation of Multiclass Image Classification by Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing, 42, 1335-1343.
https:/doi.org/10.1109/TGRS.2004.827257