A portable assist-as-need upper-extremity hybrid exoskeleton for FES-induced muscle fatigue reduction in stroke rehabilitation

Abstract

Background: Hybrid exoskeletons are a recent development which combine Functional Electrical Stimulation with actuators to improve both the mental and physical rehabilitation of stroke patients. Hybrid exoskeletons have been shown capable of reducing the weight of the actuator and improving movement precision compared to Functional Electrical Stimulation alone. However little attention has been given towards the ability of hybrid exoskeletons to reduce and manage Functional Electrical Stimulation induced fatigue or towards adapting to user ability. This work details the construction and testing of a novel assist-as-need upper-extremity hybrid exoskeleton which uses modelbased Functional Electrical Stimulation control to delay Functional Electrical Stimulation induced muscle fatigue. The hybrid control is compared with Functional Electrical Stimulation only control on a healthy subject. Results: The hybrid system produced 24°less average angle error and 13.2°less Root Mean Square Error, than Functional Electrical Stimulation on its own and showed a reduction in Functional Electrical Stimulation induced fatigue. Conclusion: As far as the authors are aware, this is the study which provides evidence of the advantages of hybrid exoskeletons compared to use of Functional Electrical Stimulation on its own with regards to the delay of Functional Electrical Stimulation induced muscle fatigue.

Development of portable robotic orthosis and biomechanical validation in people with limited upper limb function after stroke

“…The electromechanical actuators that provide the necessary forces are generally heavy and require high power, limiting portability [71]. However, the actuators used in the orthosis meet biomechanical requirements without compromising portability.…”

“…In addition, the apparatus can be adapted for different users who need specific training for a certain period, with infrequent changes, adding or connecting the modules as they progress in the rehabilitation process [8]. It can have a positive impact on the setup time and cost of rehabilitation [71].…”

Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling

“…This proof-of-concept work is a particular showcase of human-in-the-loop AI in rehabilitation, displaying its feasibility and potential. Specifically, this real-world success of reinforcement learning in FES control is a step towards the development on AI-based intelligent controls that can power rehabilitation robots for the restoration of general movements [22], [28], [29].…”

The efficacy of hybrid neuroprostheses in the rehabilitation of upper limb impairment after stroke, a narrative and systematic review with a meta‐analysis

“…Kinematic variables for the stimulation parameters are exploited in Agnanto et al 19 A commercial device for wrist pronation/supination and flexion/extension, the Bi-Manu-Track (BMT; Reha-Stim Co., Berlin, Germany), is presented in Lee et al 31 ; FES was applied with constant parameters on extensor muscles and it was turned on/off by magnetic switches placed at the end range of the BMT handles. A hybrid FES-hand robot, in which non-adaptive assistance is delivered when EMG volitional activity is detected, is described by Qian et al (2019); in this study, 43 they compare hybrid distal actuation with proximal actuation delivered by a hybrid wrist and elbow exoskeleton, which was introduced in Qian et al 42 Regarding exoskeletons for proximal actuation, elbow devices investigating algorithms for online adaptation of assistance are developed by Qian above a certain threshold, and only during the extension phase (flexor muscles were stimulated); the latter 13 designs an adaptive algorithm for both FES and robot, based on the trajectory tracking error. Wang et al developed a system for the whole UL, actuating shoulder and hand with a robotic device while using FES for assisting elbow and wrist movements.…”