Abstract
In this paper, a new estimation model based on least squares support vector machine (LS-SVM) is proposed to build up the relationship between Surface electromyogram (sEMG) signal and joint angle of the lower limb. The input of the model is 2 channels of preprocessed sEMG signal. The outputs of the model are joint angles of the hip and the knee. sEMG signal is acquired from 7 motion muscles in treadmill exercise. And two channels of them are selected for dynamic angle estimation for their strong correlation with angle data. Angle estimation model is constructed by 2 independent LS-SVM based regression model with radial basis function (RBF). It is trained using part of the sample sets acquired in 10s exercise duration and test by all data. Experimental result shows proposed method has good performance on joint angles estimation based sEMG. Root mean square error (RMSE) of prediction knee and hip joint angles is 3.02° and 2.09° respectively. It provide new human-machine interface for active rehabilitation training of SCI, stroke or neurological injury patients.
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Mayr, A., Kofler, M., Quirbach, E., et al.: Prospective, blinded, randomized crossover study of gait rehabilitation in stroke patients using the Lokomat gait orthosis. Neurorehabilitation and Neural Repair 21(4), 307–314 (2007)
Krebs, H.I., Volpe, B.T., Aisen, M.L., et al.: Increasing productivity and quality of care:Robot-aided neuro-rehabilitation. Journal of Rehabilitation Research and Development 37(6), 639–652 (2000)
Lum, P.S., Burgar, C.G., Van der Loos, H.F.M., et al.: The MIME robotic system for upper-limb neuro-rehabilitation: results from a clinical trial in subacute stroke. In: Proceeding of the 9th IEEE International Conference on Rehabilitation Robotics, pp. 511–514 (2005)
Ando, T., Okamoto, J., Fujie, M.G.: Optimal Design of a Micro Macro Neural Network to Recognize Rollover Movement. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2009 IEEE IROS), pp. 1615–1620 (2009)
Kim, J., Mastnik, S., André, E.: EMG-based hand gesture recognition for real-time biosignal interfacing. In: The 13th International Conference on IUI, pp. 30–39 (2008)
Hashemi, J., Morin, E., Mousavi, P., Hashtrudi-Zaad, K.: Joint Angle-based EMG Amplitude Calibration. In: The 33rd Annual International Conference of the IEEE EMBS, pp. 4439–4442 (2011)
Ngeo, J., Tamei, T., Shibata, T.: Continuous Estimation of Finger Joint Angles using Muscle Activation Inputs from Surface EMG Signals. In: The 34th Annual International Conference of the IEEE EMBS, pp. 2756–2759 (2012)
Wang, S., Gao, Y., Zhao, J., Yang, T., Zhu, Y.: Prediction of sEMG-Based Tremor Joint Angle Using the RBF Neural Network. In: Proceeding of 2012 IEEE International Conference on Mechatronics and Automation, pp. 2103–2108 (2012)
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Li, Q., Song, Y., Hou, Z., Zhu, B. (2013). sEMG Based Joint Angle Estimation of Lower Limbs Using LS-SVM. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_37
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DOI: https://doi.org/10.1007/978-3-642-42054-2_37
Publisher Name: Springer, Berlin, Heidelberg
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