Abstract
In this paper, we propose a capacitive sensing based method for knee joint angle continuous estimation. The motion capture system is used to record the position information of knee joint and output knee joint angel as a fitting target. Two capacitance rings are fixed on the thigh and shank to record the relaxation and contraction of muscles. Three healthy subjects participate in the study. Based on BP (back propagation) neural networks, the map relationships (i.e. model) between capacitance signals and knee joint angles are built. In the continuous estimation of angle, capacitance signals are fed into the model, and then we can get the estimated angles. The error (root mean square) and the cross-correlation coefficient are 4.4\(^\circ \) and 97.01% between the estimated knee joint angles (based on capacitance signals) and actual keen knee-angle. The results show that capacitive sensing method is effective and feasible to estimate the joint angles.
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References
Wang, Q., Yuan, K., Zhu, J., Wang, L.: Walk the walk: a lightweight active transtibial prosthesis. IEEE Robot. Autom. Mag. 22(4), 80–89 (2015)
Baunsgaard, C.B., Nissen, U.V., Brust, A.K., et al.: Exoskeleton gait training after spinal cord injury: an exploratory study on secondary health conditions. J. Rehabil. Med. 50(9), 806–813 (2018)
Morone, G., Paolucci, S., Cherubini, A., et al.: Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics. Neuropsychiatr. Dis. Treat. 13, 1303–1311 (2017)
Feng, Y., Wang, Q.: Combining push-off power and nonlinear damping behaviors for a lightweight motor-driven transtibial prosthesis. IEEE/ASME Transact. Mechatron. 22(6), 2512–2523 (2017)
Huo, W., Mohammed, S., Amirat, Y., Kong, K.: Fast gait mode detection and assistive torque control of an exoskeletal robotic orthosis for walking assistance. IEEE Transact. Robot. 34(4), 1035–1052 (2018)
Scott, S.H.: Optimal feedback control and the neural basis of volitional motor control. Nature Rev. Neurosci. 5(7), 532–546 (2004)
Tucker, M.R., et al.: Control strategies for active lower extremity prosthetics and orthotics: a review. J. NeuroEng. Rehabil. 12(1), 1 (2015)
Shi, J., Zheng, Y.P., Huang, Q.H., Chen, X.: Continuous monitoring of sonomyography, electromyography and torque generated by normal upper arm muscles during isometric contraction: sonomyography assessment for arm muscles. IEEE Transact. Biomed. Eng. 55(3), 1191–1198 (2008)
Chen, B., Wang, Q., Wang, L.: Adaptive slope walking with a robotic transtibial prosthesis based on volitional EMG control. IEEE/ASME Transact. Mechatron. 20(5), 2146–2157 (2015)
Joshi, D., Nakamura, B.H., Hahn, M.E.: High energy spectrogram with integrated prior knowledge for EMG-based locomotion classification. Med. Eng. Phys. 37(5), 518–524 (2015)
Li, Q., Song, Y., Hou, Z.G.: Estimation of lower limb periodic motions from semg using least squares support vector regression. Neural Process. Lett. 41, 371–388 (2015)
Chena, J., Zhang, X., Cheng, Y., Xi, N.: Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks. Biomed. Signal Process. Control 40, 335–342 (2018)
Anwar, T., Aung, Y.M., Jumaily, A.A.: The estimation of knee joint angle based on generalized regression neural network (GRNN). In: Proceedings of the IEEE International Symposium on Robotics and Intelligent Sensors, pp. 208–213. Langkawi, Malaysia (2015)
Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., Laurillau, Y.: EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst. Appl. 40(12), 4832–4840 (2013)
Zheng, E., Wang, L., Wei, K., Wang, Q.: A noncontact capacitive sensing system for recognizing locomotion modes of transtibial amputees. IEEE Transact. Biomed. Eng. 61(12), 2911–2920 (2014)
Zheng, E., Wang, Q.: Noncontact capacitive sensing based locomotion transition recognition for amputees with robotic transtibial prostheses. IEEE Transact. Neural Syst. Rehabil. 25(2), 161–170 (2017)
Zheng, E., Wang, Q., Qiao, H.: A preliminary study of upper-limb motion recognition with noncontact capacitive sensing. In: Proceedings of the 10th International Conference on Intelligent Robotics and Applications, Wuhan, China, pp. 251–261 (2017)
Xu, D., Yang, Y., Mai, J., Wang, Q.: Muscle redistribution surgery based capacitive sensing for upper-limb motion recognition: Preliminary results. In: Proceedings of the IEEE International Conference on Cyborg and Bionic Systems, Beijing, China, pp. 125–129 (2017)
Zheng, E., Manca, S., Yan, T., Parri, A., Vitiello, N., Wang, Q.: Gait phase estimation based on noncontact capacitive sensing and adaptive oscillators. IEEE Transact. Biomed. Eng. 64(10), 2419–2430 (2017)
Zheng, E., Wang, Q., Qiao, H.: Identification of the relationships between noncontact capacitive sensing signals and continuous grasp forces: preliminary study. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, USA, pp. 3922–3925 (2018)
Min, S., Lee, B., Yoon, S.: Deep learning in bioinformatics. Brief. Bioinf. 18(5), 851–869 (2017)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)
Acknowledgment
This work was supported by the National Key R&D Program of China (No. 2018YFB1307302, 2018YFF0300606), the National Natural Science Foundation of China (No. 91648207, 61533001), the Beijing Nova Program (No. Z141101001814001) and the Beijing Municipal Science and Technology Project (No. Z181100009218007).
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Xu, D., Wang, Q. (2019). Capacitive Sensing Based Knee-Angle Continuous Estimation by BP Neural Networks. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11740. Springer, Cham. https://doi.org/10.1007/978-3-030-27526-6_6
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DOI: https://doi.org/10.1007/978-3-030-27526-6_6
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