Experimental Protocol to Assess Neuromuscular Plasticity Induced by an Exoskeleton Training Session

Methods Protoc., 2021 · DOI: https://doi.org/10.3390/mps4030048 · Published: July 13, 2021

Assistive Technology Neuroplasticity Rehabilitation

Simple Explanation

This protocol aims to quantify how a single session of robotic exoskeleton training impacts the nervous system and muscles of people post-stroke, compared to healthy individuals. The study will assess if physiological signals, such as those from the brain (EEG) and muscles (EMG), can predict lower limb movement and potentially control the robot in the future. Participants will undergo a single training session with an active exoskeleton for gait rehabilitation.

Study Duration

Not specified

Participants

N ≥20 Stroke survivors and N ≥10 age and sex-matched controls

Evidence Level

Not specified

Key Findings

1
The key innovation of the proposed protocol is in the co-registration of data from three synchronised systems (high-density EEG, surface EMG and IMU) before, during and after robot-assisted gait-training.
2
The study aims to determine the feasibility of controlling an exoskeleton with physiological (e.g., EEG and/or EMG) signals.
3
The synchronous EMG signals are required to identify antagonistic-agonistic muscular activation during gait and muscles response to exoskeleton.

Research Summary

This protocol outlines a study to investigate neuromuscular plasticity induced by exoskeleton training in post-stroke individuals and controls, using EEG and EMG. The goal is to quantify brain connectivity changes, compare muscle activation patterns, and assess the feasibility of using physiological signals to predict gait intentions. The study aims to establish biomarkers for gait recovery and personalize robotic training based on neuromuscular patterns.

Practical Implications

Personalized Rehabilitation

Findings could lead to fine-tuning robotic device parameters for improved patient recovery and customized rehabilitation training.

Biomarker Identification

Identifying prognostic biomarkers of gait recovery can help track longer-term outcomes of functional restoration.

Improved Human-Machine Interaction

The study aims to provide insights into human-machine interaction, enabling a more personalized and fine-tuned robotic training experience.

Study Limitations

1
Mechanisms underlying the changes induced by a robot for overground gait-training are still unclear.
2
The intrinsic redundancy of neuromotor strategies during walking, which are fundamental to gait adaptability, acts as confounding factor when interpreting EMG data collected before and after a single training session performed with an exoskeleton
3
We cannot take for granted that the same changes will be observed in individuals with neurological disorders.

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