Abstract
Motor learning mediated by motor training has in the past been explored for rehabilitation. Myoelectric interfaces together with exoskeletons allow patients to receive real-time feedback about their muscle activity. However, the number of degrees of freedom that can be simultaneously controlled is limited, which hinders the training of functional tasks and the effectiveness of the rehabilitation therapy. The objective of this study was to develop a myoelectric interface that would allow multi-degree-of-freedom control of an exoskeleton involving arm, wrist and hand joints, with an eye toward rehabilitation. We tested the effectiveness of a myoelectric decoder trained with data from one upper limb and mirrored to control a multi-degree-of-freedom exoskeleton with the opposite upper limb (i.e., mirror myoelectric interface) in 10 healthy participants. We demonstrated successful simultaneous control of multiple upper-limb joints by all participants. We showed evidence that subjects learned the mirror myoelectric model within the span of a five-session experiment, as reflected by a significant decrease in the time to execute trials and in the number of failed trials. These results are the necessary precursor to evaluating if a decoder trained with EMG from the healthy limb could foster learning of natural EMG patterns and lead to motor rehabilitation in stroke patients.
Highlights
We believe that hemiplegic stroke patients could reshape the pathological muscle activity of their paretic limb by learning a fixed mapping or model built with EMG activity of their healthy upper limb, as suggested in Sarasola-Sanz et al (2018)
We investigated the viability of using a novel EMG decoding strategy to control an upper limb multi-joint exoskeleton in real-time during functional tasks, based on a mirror model from the contralateral arm (Sarasola-Sanz et al, 2018)
Where Vnet is the velocity sent to each DoF of the exoskeleton; Vassist is the assistive component that redirects the exoskeleton toward the target [computed with a Linear Quadratic Regulator (LQR) (Shanechi et al, 2016)], and partially corrects any possible deviation due to an erroneous EMG control; VEMG is the velocity predicted by the mirror decoder from the EMG activity exerted by the left arm; and γ ∈ [0, 1] is the weight determining the influence of each component on the net velocity command sent to the exoskeleton (e.g., γ = 0.7 during the trials with 30% of assistance)
Summary
A voluntary movement is the result of complex mechanisms in which the central nervous system recruits groups of muscles in a coordinated way, with different activation patterns and temporal profiles that are encoded at the spinal or brainstem levels (Tresch et al, 1999; d’Avella et al, 2003; Bizzi et al, 2008; Bizzi and Cheung, 2013). Ison and colleagues recently demonstrated that using a fixed mapping between the EMG and the output control command could induce learning and the creation of novel muscle activation patterns or synergies in healthy individuals These synergies were retained after 1 week, facilitating the generalization to new tasks and the increase in performance over time without the need of recalibrating the decoder (i.e., changing the mapping) (Antuvan et al, 2014; Ison et al, 2014b; Ison and Artemiadis, 2015). We believe that hemiplegic stroke patients could reshape the pathological muscle activity of their paretic limb by learning a fixed mapping or model built with EMG activity of their healthy upper limb (i.e., a mirror myoelectric decoder), as suggested in Sarasola-Sanz et al (2018) Whether such changes in the muscle activation patterns of the impaired limb of stroke patients would lead to functional recovery has not been proven yet. With a view toward optimizing such a system in a patient-centered approach to stroke recovery, we queried participants in the experiments about their perceptions of different features of the system
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