Feedback control of arm movements using Neuro-Muscular Electrical Stimulation (NMES) combined with a lockable, passive exoskeleton for gravity compensation

Abstract

Within the European project MUNDUS, an assistive framework was developed for the support of arm and hand functions during daily life activities in severely impaired people. This contribution aims at designing a feedback control system for Neuro-Muscular Electrical Stimulation (NMES) to enable reaching functions in people with no residual voluntary control of the arm and shoulder due to high level spinal cord injury. NMES is applied to the deltoids and the biceps muscles and integrated with a three degrees of freedom (DoFs) passive exoskeleton, which partially compensates gravitational forces and allows to lock each DOF. The user is able to choose the target hand position and to trigger actions using an eyetracker system. The target position is selected by using the eyetracker and determined by a marker-based tracking system using Microsoft Kinect. A central controller, i.e., a finite state machine, issues a sequence of basic movement commands to the real-time arm controller. The NMES control algorithm sequentially controls each joint angle while locking the other DoFs. Daily activities, such as drinking, brushing hair, pushing an alarm button, etc., can be supported by the system. The robust and easily tunable control approach was evaluated with five healthy subjects during a drinking task. Subjects were asked to remain passive and to allow NMES to induce the movements. In all of them, the controller was able to perform the task, and a mean hand positioning error of less than five centimeters was achieved. The average total time duration for moving the hand from a rest position to a drinking cup, for moving the cup to the mouth and back, and for finally returning the arm to the rest position was 71 s.

The combined action of a passive exoskeleton and an EMG-controlled neuroprosthesis for upper limb stroke rehabilitation: First results of the RETRAINER project

“…2: three of them, e.g. shoulder elevation, shoulder rotation in the transversal plane and elbow flex-extension, are equipped with angle sensors (Vert-X 13 E, ConTelec AG) to measure the position and electromagnetic brakes to avoid the fatiguing and unnecessary use of FES to hold a target position once reached [26]- [28]. The additional DOF is provided by an inclination module, which enables the patient to move the trunk 20° forward without constriction.…”

A Hybrid Robotic System for Arm Training of Stroke Survivors: Concept and First Evaluation

“…FES was combined also with passive anti-gravity exoskeletons, e.g. the Hocoma ArmeoSpring [31] and a custom-built passive suspension exoskeleton developed during the European project MUNDUS [32], [33]. In the former study [31], FES was mediated by iterative learning control in virtual reality tracking trajectories; elbow extension and shoulder flexion and abduction were trained achieving promising results on 5 chronic stroke patients.…”

“…In the former study [31], FES was mediated by iterative learning control in virtual reality tracking trajectories; elbow extension and shoulder flexion and abduction were trained achieving promising results on 5 chronic stroke patients. In the latter study [32], FES was applied to the deltoids and the biceps muscles and a feedback control system sequentially controlling each joint angle was developed. However, the final aim of this system was assistive and non-rehabilitative.…”

A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation

“…In light of these challenges, researchers have explored several control strategies to develop effective NMES controllers; e.g., linear PID-based pure feedback methods (cf., Abbas and Chizeck, 1991;Lan et al, 1991a,b;Lynch and Popovic, 2012;Klauer et al, 2014; and the references therein), neural network (NN) based controllers (cf., Tong and Granat, 1999;Kordylewski and Graupe, 2001;Sepulveda, 2003;Zhang and Zhu, 2004;Giuffrida and Crago, 2005;Wang et al, 2013; and the references therein), and combined feedback and feedforward methods (Chang et al, 1997;Ferrarin et al, 2001;Chen et al, 2005;Ajoudani and Erfanian, 2009;Freeman et al, 2009;Freeman, 2014). Recently, Lyapunov-based techniques were utilized in Schauer et al (2005), Sharma et al (2009bSharma et al ( , 2011Sharma et al ( , 2012, and Downey et al (2015) to design NMES controllers and prove asymptotic stability for an uncertain nonlinear muscle model.…”