FES Cycling and Closed-Loop Feedback Control for Rehabilitative Human–Robot Interaction

Abstract

For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of daily living. Combining FES with rehabilitation robots results in intimately coordinated human–robot interaction. An example of such interaction is FES cycling, where motorized assistance can provide high-intensity and repetitive practice of coordinated limb motion, resulting in physiological and functional benefits. In this paper, the development of multiple FES cycling testbeds and safeguards is described, along with the switched nonlinear dynamics of the cycle–rider system. Closed-loop FES cycling control designs are described for cadence and torque tracking. For each tracking objective, the authors’ past work on robust and adaptive controllers used to compute muscle stimulation and motor current inputs is presented and discussed. Experimental results involving both able-bodied individuals and participants with neurological injuries are provided for each combination of controller and tracking objective. Trade-offs for the control algorithms are discussed based on the requirements for implementation, desired rehabilitation outcomes and resulting rider performance. Lastly, future works and the applicability of the developed methods to additional technologies including teleoperated robotics are outlined.

Closed-Loop Torque and Kinematic Control of a Hybrid Lower-Limb Exoskeleton for Treadmill Walking

“…A fundamental research question for the development of hybrid exoskeletons is how to allocate or segregate the control design for the powered actuators and muscles ( Alibeji et al, 2018a ; Alibeji et al, 2018b ; Ha et al, 2016 ). Kinematic tracking has been the primary control objective for rehabilitation devices and machines that combine FES and powered actuation, where the desired trajectories can be tracked by muscles, electric motors (i.e., the machine or robot) or both during walking and cycling ( Alibeji et al, 2018b ; Duenas et al, 2019 ; Cousin et al, 2021 ). Recently, torque tracking objectives have been developed for motorized FES-cycling using admittance-based or impedance-based strategies with a Lyapunov-based analysis ( Chang and Duenas, 2019 ; Duenas et al, 2020 ; Cousin et al, 2020 ).…”

A Novel Functional Electrical Stimulation-Induced Cycling Controller Using Reinforcement Learning to Optimize Online Muscle Activation Pattern

“…Several investigations have addressed closed-loop control strategies to track cadence, torque and power [ 7 , 8 , 9 ]. Besides the performance of the controller and cycling outcomes, the results obtained from investigations indicated that the choice of the strategy to be developed reflects a trade-off between what is expected from the rehabilitation goals and the level of complexity from a technological point of view.…”