sEMG-Based Single-Joint Active Training with iLeg - A Horizontal Exoskeleton for Lower Limb Rehabilitation

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.

[1]  Lida Xu,et al.  EMG and EPP-Integrated Human–Machine Interface Between the Paralyzed and Rehabilitation Exoskeleton , 2012, IEEE Transactions on Information Technology in Biomedicine.

[2]  Simone Pittaccio,et al.  An EMG-Controlled SMA Device for the Rehabilitation of the Ankle Joint in Post-Acute Stroke , 2011, Journal of Materials Engineering and Performance.

[3]  Carlo J. De Luca,et al.  The Use of Surface Electromyography in Biomechanics , 1997 .

[4]  F. Müller,et al.  Effects of Locomotion Training With Assistance of a Robot-Driven Gait Orthosis in Hemiparetic Patients After Stroke: A Randomized Controlled Pilot Study , 2007, Stroke.

[5]  D. Gordon E. Robertson,et al.  Research Methods in Biomechanics , 2004 .

[6]  Y. Hsieh,et al.  Effects of robot-assisted upper limb rehabilitation on daily function and real-world arm activity in patients with chronic stroke: a randomized controlled trial , 2012, Clinical rehabilitation.

[7]  Massimo Sartori,et al.  A lower limb EMG-driven biomechanical model for applications in rehabilitation robotics , 2009, 2009 International Conference on Advanced Robotics.

[8]  Seul Jung,et al.  Neural network impedance force control of robot manipulator , 1998, IEEE Trans. Ind. Electron..

[9]  Yupeng Ren,et al.  Effects of robot-guided passive stretching and active movement training of ankle and mobility impairments in stroke. , 2013, NeuroRehabilitation.

[10]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation: Part I—Theory , 1985 .

[11]  Jose L Pons,et al.  Wearable Robots: Biomechatronic Exoskeletons , 2008 .

[12]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation , 1984, 1984 American Control Conference.