ICONIP 2014: Neural Information Processing pp 535-542 | Cite as
sEMG-Based Single-Joint Active Training with iLeg—A Horizontal Exoskeleton for Lower Limb Rehabilitation
Abstract
In this paper, surface electromyography (sEMG) from muscles of the lower limb is acquired and processed to estimate the single-joint voluntary motion intention, based on which, two single-joint active training strategies are proposed with iLeg, a horizontal exoskeleton for lower limb rehabilitation newly developed at our laboratory. In damping active training, the joint angular velocity is proportionally controlled by the voluntary effort derived from sEMG, performing as an ideal damper, while spring active training aims to create a spring-like environment where the joint angular displacement from the constant reference is proportionally controlled by the voluntary effort. Experiments are conducted with iLeg and one healthy male subject to validate the feasibility of the two single-joint active training strategies.
Keywords
sEMG single-joint active training lower limb rehabilitationPreview
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References
- 1.De Luca, C.: The use of surface electromyography in biomechanics. Journal of Applied Biomechanics 13(2), 135–163 (1997)Google Scholar
- 2.Hogan, N.: Impedance control - An approach to manipulation. Journal of Dynamic Systems Measurement and Control-Transactions of the ASME 107(1), 1–24 (1985)CrossRefMATHGoogle Scholar
- 3.Husemann, B., Mueller, F., Krewer, C., Heller, S., Koenig, E.: Effects of locomotion training with assistance of a robot-driven gait orthosis in hemiparetic patients after stroke - A randomized controlled pilot study. Stroke 38(2), 349–354 (2007)CrossRefGoogle Scholar
- 4.Jung, S., Hsia, T.: Neural network impedance force control of robot manipulator. IEEE Transactions on Industrial Electronics 45(3), 451–461 (1998)CrossRefGoogle Scholar
- 5.Liao, W.W., Wu, C.Y., Hsieh, Y.W., Lin, K.C., Chang, W.Y.: Effects of robot-assisted upper limb rehabilitation on daily function and real-world arm activity in patients with chronic stroke: A randomized controlled trial. Clinical Rehabilitation 26(2), 111–120 (2012)CrossRefGoogle Scholar
- 6.Pittaccio, S., Viscuso, S.: An EMG-controlled SMA device for the rehabilitation of the ankle joint in post-acute stroke. Journal of Materials Engineering and Performance 20(4-5, SI), 666–670 (2011)Google Scholar
- 7.Pons, J.L.: Wearable robots: Biomechatronic exoskeletons. John Wiley & Sons, Ltd (2008)Google Scholar
- 8.Robertson, G., Caldwell, G., Hamill, J., Kamen, G., Whittlesey, S.: Research Methods in Biomechanics, 2E. Human Kinetics (2013)Google Scholar
- 9.Sartori, M., Reggiani, M., Mezzato, C., Pagello, E.: A lower limb EMG-driven biomechanical model for applications in rehabilitation robotics. In: Proceedings of 2009 International Conference on Advanced Robotics, pp. 905–911 (June 2009)Google Scholar
- 10.Waldman, G., Yang, C.Y., Ren, Y., Liu, L., Guo, X., Harvey, R.L., Roth, E.J., Zhang, L.Q.: Effects of robot-guided passive stretching and active movement training of ankle and mobility impairments in stroke. Neurorehabilitation 32(3), 625–634 (2013)Google Scholar
- 11.Yin, Y.H., Fan, Y.J., Xu, L.D.: EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton. IEEE Transactions on Information Technology in Biomedicine 16(4), 542–549 (2012)CrossRefGoogle Scholar