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Predicting manual wheelchair initiation movement with EMG activity during over ground propulsion
Soufien Chikh1,2, Samuel Boudet3, Antonio Pinti4
1Université de Sfax, Institut Supérieur du Sport et de l'Education Physique de Sfax. Laboratoire de recherche Education, Motricité, Sport et Santé, EMSS-LR19JS01, Sfax, Tunisie.
The Journal of Spinal Cord Medicine
|July 10, 2020
Summary
This study predicts manual wheelchair (MWC) movement direction and speed using electromyography (EMG) signals before movement begins. This technology could enable smart electrical assistance for MWC users, improving mobility and reducing musculoskeletal disorders.
Area of Science:
- Rehabilitation Engineering
- Biomechanics
- Human-Computer Interaction
Background:
- Manual wheelchair (MWC) users frequently experience musculoskeletal disorders due to the physical demands of propulsion.
- Predictive assistance systems could enhance MWC usability and reduce user strain.
Purpose of the Study:
- To predict the intended direction and speed of manual wheelchair movement using electromyography (EMG) data prior to movement initiation.
- To explore the potential for EMG-based prediction to inform smart electrical assistance systems for MWCs.
Main Methods:
- An experimental study involving eight healthy subjects trained in MWC operation.
- EMG data from 14 bilateral muscles were recorded during voluntary movements in various directions and speeds.
- A hierarchical multi-class classification model using logistic regression and stepwise variable selection was employed.
Main Results:
- The study achieved 95% correct classification for predicting the combination of movement direction and speed.
- Analysis of EMG parameters, including prior amplitude and anticipatory postural adjustments, proved effective for prediction.
Conclusions:
- Predicting MWC user intentions from EMG signals is feasible with high accuracy.
- This predictive capability opens avenues for developing intuitive, intention-based electrical assistance systems for MWCs.
