Feedback Control of Functional Electrical Stimulation for 2-D Arm Reaching Movements

Abstract

Functional electrical stimulation (FES) can be used as a neuroprosthesis in which muscles are stimulated by electrical pulses to compensate for the loss of voluntary movement control. Modulating the stimulation intensities to reliably generate movements is a challenging control problem. This paper introduces a feedback controller for a multi-muscle FES system to control hand movements in a 2-D (table-top) task space. This feedback controller is based on a recent human motor control model, which uses muscle synergies to simplify its calculations and improve the performance. This synergy-based controller employs direct relations between the muscle synergies and the produced hand force, therefore allowing for the real-time calculation of six muscle stimulation levels required to reach an arbitrary target. The experimental results show that this control scheme can perform arbitrary point-to-point reaching tasks in the 2-D task space in real time, with an average of ~2 cm final hand position error from the specified targets. The success of this prototype demonstrates the potential of the proposed method for the feedback control of functional tasks with FES.

On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems

“…As a practical conclusion, the control of motion in the task and redundant spaces can be separated in a computational model by employing orthogonal basis vectors, and the results are not far from reality. Therefore, we can build mathematical motor control models more confidently using this orthogonality assumption, which are especially useful for the real-time model-based control of bio-mechatronic systems (Mehrabi and McPhee, 2019), rehabilitation robots (Ghannadi et al, 2018), exoskeletons (Kuhn et al, 2018), and functional electrical systems (Sharif Razavian et al, 2017, 2018).…”

“…In general, the muscle activation estimated from an inverse approach will not result in the same desired motion, mostly due to estimation errors, disturbance/noise, and unknown dynamics. However, by putting this inverse mapping in a hierarchical feedback control scheme, it is possible to compensate for much of the error, and achieve acceptable control performance (Sharif Razavian, 2017; Sharif Razavian et al, 2018; Sharif Razavian et al, 2019.…”

Data-driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

“…Both controllers used model-learning methods to determine configuration dependent models of forces produced by the muscles along with a feedback controller to move the wrist along a straight-line path to a desired hand position. Razavian achieved 2D reaching motions using FES in a healthy individual [9]. Our own work previously achieved 3D reaching motions using straight-line paths in a participant with a spinal cord injury with reasonable accuracy, but there were areas of the workspace where the accuracy was limited [14].…”

“…To our knowledge, there have been two main attempts to control full-arm reaching motions without robots actively controlling degrees of freedom [9,13]. Both controllers used model-learning methods to determine configuration dependent models of forces produced by the muscles along with a feedback controller to move the wrist along a straight-line path to a desired hand position.…”

An In Vitro Hand Simulator for Simultaneous Control of Hand and Wrist Movements

“…A few control models are presented that have the potential for feedback motion control of complex biomechanical systems. Among the published approaches are the controllers based on artificial neural networks [15], [16], advanced optimal controllers [17]- [19], and controllers based on muscle synergies and task-space [20], [21]. Most of these controllers require detailed knowledge about the dynamics of the system, or rely on extensive training data.…”