Predicting manual wheelchair initiation movement with EMG activity during over ground propulsion. (4th March 2022)

• Record Type: Journal Article
• Authors: Chikh, Soufien; Boudet, Samuel; Pinti, Antonio; Garnier, Cyril; El Hage, Rawad; Azaiez, Fairouz; Watelain, Eric
• Is Part Of: Journal of spinal cord medicine. Volume 45:Number 2(2022)
• Page Start: 262
• Page End: 269
• Publication Date: 2022-03-04
• Subjects: Prediction -- EMG -- Logistic regression -- Manual wheelchairs -- Initiation; Spinal cord -- Wounds and injuries -- Periodicals; Spinal cord -- Diseases -- Periodicals; 616.8305
• DOI: 10.1080/10790268.2020.1778352
• Languages: English
• ISSNs: 1079-0268

Abstract

Abstract : Context/Objective: This is a preliminary study of movement finalities prediction in manual wheelchairs (MWCs) from electromyography (EMG) data. MWC users suffer from musculoskeletal disorders and need assistance while moving. The purpose of this work is to predict the direction and speed of movement in MWCs from EMG data prior to movement initiation. This prediction could be used by MWC to assist users in their displacement by doing a smart electrical assistance based on displacement prediction. Design: Experimental study. Setting: Trained Subject LAMIH Laboratory. Participants: Eight healthy subjects trained to move in manual wheelchairs. Interventions: Subjects initiated the movement in three directions (front, right and left) and with two speeds (maximum speed and spontaneous speed) from two hand positions (on the thighs or on the handrim). A total of 96 movements was studied. Activation of 14 muscles was recorded bilaterally at the deltoid anterior, deltoid posterior, biceps brachii, pectoralis major, rectus abdominis, obliquus externus and erector spinae. Outcome Measures: Prior amplitude, prior time and anticipatory postural adjustments were measured. A hierarchical multi-class classification using logistic regression was used to create a cascade of prediction models. We performed a stepwise (forward–backward) selection of variables using the Bayesian information criterion. Percentages of well-classified movements have been measured through the means of a cross-validation. Results: Prediction is possible using the EMG parameters and allows to discriminate the direction / speed combination with 95% correct classification on the 6 possible classes (3 directions * 2 speeds). Conclusion: Action planning in the static position showed significant adaptability to the forthcoming parameters displacement. The percentages of prediction presented in this work make it possible to envision an intuitive assistance to the initiation of the MWC displacement adapted to the user's intentions.