Abstract
This paper presents a control method of grasping force of prosthetic hand. Firstly, the correlated features of surface electromyogram (sEMG) signal that collected by MYO are calculated, and then principal component analysis (PCA) dimension reduction is processed. According to pattern classification model and sEMG-force regression model which based on support vector machine (SVM) to gain the force prediction value. In this approach, force is divided into different grades. The predicted force value is used as the given signal, and grasping force of prosthetic hand is controlled by a fuzzy controller, and combined with vibration feedback device to feedback grasping force value to patient’s arm. The test results show that the method of prosthetic hand grasping force control is effective.
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References
Bingke, Z., Xiaogang, D., Hua, D.: Force estimation in different grasping mode from electromyography. J. Comput. Appl. 35(7), 2109–2112 (2015)
Huatao, Z., Aiguo, S., Yan, G., Jiatong, B., Kui, Q.: Control method for myoelectric prosthetic hand’s grip force based on neural network and fuzzy logic controller. Adv. Mater. Res. 403–408, 2843–2847 (2011)
Fukuda, O., Tsuji, T., Kaneko, M., Otsuka, A.: A human-assisting manipulator teleoperated by sEMG signals and armmotions. IEEE Trans. Robot. Autom. 19(2), 210–222 (2003)
Erdemir, A., Mclean, S.G., Herzog, W., van den Bogert, A.J.: Model-based estimation of muscle forces during movements. Clin. Biomech. 22(2), 131–154 (2007)
Peidong, L., Chenguang, Y., Ning, W., Ruifeng, L.: A discrete-time algorithm for stiffness extraction from sEMG and its application in antidisturbance teleoperation. Discrete Dyn. Nat. Soc. 2016(2), 1–11 (2016)
Zhijun, L., Baocheng, W., Fuchun, S., Chenguang, Y., Qing, X., Weidong, Z.: sEMG-based joint force control for an upper-limb power-assist exoskeleton robot. IEEE J. Biomed. Health Inf. 18(3), 1043–1050 (2014)
Ng, G.Y.F., Zhang, A.Q., Li, C.K.: Biofeedback exercise improved the EMG activity ratio of the medial and lateral vasti muscles in subjects with patellofemoral pain syndrome. J. Electromyogr. Kinesiol. 18(1), 128–133 (2008)
Li, G., Schultz, A.E., Kuiken, T.A.: Quantifying pattern recognition based myoelectric control of multifunctional transradial prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 18(2), 185–192 (2010)
Kamavuako, E.N., Farina, D., Yoshida, K., Jensen, W.: Relationship between grasping force and features of single-channel intramuscular EMG signals. J. Neurosci. Methods 185(1), 143–150 (2009)
Hanliang, Y., Chase, R.A., Mosby, B.S.: Atlas of hand anatomy and clinical implications. J. Hand Surg. Br. Eur. 29(4), 409 (2004)
Choi, C., Kwon, S., Park, W., Lee, H., Kim, J.: Real-time pinch force estimation by surface electromyography using an artificial neural network. Med. Eng. Phys. 32(5), 429–436 (2010)
Xinqing, W., Yiwei, L., Dapeng, Y., Shaowei, F., Hong, L.: Tracking control of real-time force for prosthetic hand based on EMG. J. Shenyang Univ. Technol. 34(1), 63–67 (2012)
Dapeng, Y., Jingdong, Z., Li, J., Hong, L.: Force regression from EMG signals under different grasping patterns. J. Harbin Inst. Technol. 44(1), 83–87 (2012)
Xinhua, D., Zengqiang, C., Zhuzhi, Y.: Function approximation research based on support vector machine. Comput. Eng. 32(8), 52–54 (2006)
David, G., Lloyd, T.F.B.: An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. J. Biomech. 36(6), 765–776 (2003)
Gaofeng, W., Feng, T., Gang, T., Chengtao, W.: A wavelet-based method to predict muscle forces from surface electromyography signals in weightlifting. J. Bionic Eng. 09(9), 48–58 (2012)
Asefi, M., Moghimi, S., Kalani, H., Moghimi, A.: Dynamic modeling of SEMG–force relation in the presence of muscle fatigue during isometric contractions. Biomed. Signal Process. Control 28, 41–49 (2016)
Soo, Y., Sugi, M., Yokoi, H., Arai, T., Nishino, M., Kato, R., Nakamura, T., Ota, J.: Estimation of handgrip force using frequency-band technique during fatiguing muscle contraction. J. Electromyogr. Kinesiol. 20(5), 888–895 (2010)
Englehart, K., Hudgins, B., Parker, P.A.: A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 48(3), 302–311 (2001)
Kamavuako, E.N., Rosenvang, J.C., Bøg, M.F., Smidstrup, A., Erkocevic, E., Niemeier, M.J., Jensen, W., Farina, D.: Influence of the feature space on the estimation of hand grasping force from intramuscular EMG. Biomed. Signal Process. Control 8(1), 1–5 (2013)
Dongmei, W., Xin, S., Zhicheng, Z., Zhijiang, D.: Feature collection and analysis of surface electromyography signals. J. Clin. Rehabil. Tissue Eng. Res. 14(43), 8073–8076 (2010)
Yutao, J., Zhizeng, L.: Summary of EMG feature extraction. Chin. J. Electron Devices 30(1), 326–330 (2007)
Yapeng, Z.: Research on face recognition system based on parallel pca algorithm. Comput. Knowl. Technol. 12(19), 147–148 (2016)
Yuan, Z., Yanping, Z.: An algorithm of PCA and its application. Comput. Technol. Dev. 15(2), 67–68 (2005)
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Ren, J. et al. (2017). Grasping Force Control of Prosthetic Hand Based on PCA and SVM. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_22
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DOI: https://doi.org/10.1007/978-981-10-6370-1_22
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