Soufien Chikh1,2, Samuel Boudet3, Antonio Pinti4, Cyril Garnier5, Rawad El Hage6, Fairouz Azaiez7, Eric Watelain2. 1. Université 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. 2. Université de Toulon, Laboratoire IAPS, UR n°201723207F, Toulon, France. 3. Univ Nord de France, F-59000 Lille, France. Unité de Traitement de Signaux Biomédicaux, Faculté de Médecine et Maïeutique de l'Université Catholique de Lille, France. 4. Univ Lille Nord de France, F-59000 Lille, France. EA 4708, I3MTO, CHRO - 1 rue Porte Madeleine, 45032 Orléans, France. 5. Univ Lille Nord de France, F-59000 Lille, France. UVHC, LAMIH-Dptm SHV, F-59313 Valenciennes, France. CNRS, UMR 8201, F-59313 Valenciennes, France. 6. Department of Physical Education, Faculty of Arts and Social Sciences, University of Balamand, El-Koura, Lebanon. 7. Université de Sfax, Institut Supérieur du Sport et de l'Education Physique de Sfax, Tunisie.
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.
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.
Authors: K Kulig; C J Newsam; S J Mulroy; S Rao; J K Gronley; E L Bontrager; J Perry Journal: Clin Biomech (Bristol, Avon) Date: 2001-11 Impact factor: 2.063
Authors: Y Kentar; R Zastrow; H Bradley; M Brunner; W Pepke; T Bruckner; P Raiss; A Hug; H Almansour; M Akbar Journal: Spinal Cord Date: 2018-01-24 Impact factor: 2.772