Louis-Nicolas Veilleux1, Maxime Raison2, Frank Rauch3, Maxime Robert4, Laurent Ballaz5. 1. Centre de Réadaptation Marie Enfant, Research Center, Sainte-Justine University Hospital, Montréal, Québec, Canada; Shriners Hospital for Children and Department of Pediatrics, McGill University, Montréal, Québec, Canada. Electronic address: lnveilleux@shriners.mcgill.ca. 2. Centre de Réadaptation Marie Enfant, Research Center, Sainte-Justine University Hospital, Montréal, Québec, Canada; Rehabilitation Engineering Chair Applied to Pediatrics (RECAP), École Polytechnique de Montréal & Sainte-Justine University Hospital, Montréal, Québec, Canada. 3. Centre de Réadaptation Marie Enfant, Research Center, Sainte-Justine University Hospital, Montréal, Québec, Canada; Shriners Hospital for Children and Department of Pediatrics, McGill University, Montréal, Québec, Canada. 4. Centre de Réadaptation Marie Enfant, Research Center, Sainte-Justine University Hospital, Montréal, Québec, Canada. 5. Centre de Réadaptation Marie Enfant, Research Center, Sainte-Justine University Hospital, Montréal, Québec, Canada; Groupe de Recherche en Activité Physique Adaptée (GRAPA) and Département des sciences de l'activité physique, Université du Québec à Montréal, Montréal, Québec, Canada.
Abstract
INTRODUCTION: A ground reaction force decomposition algorithm based on large force platform measurements has recently been developed to analyze ground reaction forces under each foot during the double support phase of gait. However, its accuracy for the measurement of the spatiotemporal gait parameters remains to be established. OBJECTIVE: The aim of the present study was to establish the agreement between the spatiotemporal gait parameters obtained using (1) a walkway (composed of six large force platforms) and the newly developed algorithm, and (2) an optoelectronic motion capture system. METHODS: Twenty healthy children and adolescents (age range: 6-17 years) and 19 healthy adults (age range: 19-51 years) participated in this study. They were asked to walk at their preferred speed and at a speed that was faster than the preferred one. Each participant performed three blocks of three trials in each of the two walking speed conditions. RESULTS: The spatiotemporal gait parameters measured with the algorithm did not differ by more than 2.5% from those obtained with the motion capture system. The limits of agreement represented between 3% and 8% of the average spatiotemporal gait parameters. Repeatability of the algorithm was slightly higher than that of the motion capture system as the coefficient of variations ranged from 2.5% to 6%, and from 1.5% to 3.5% for the algorithm and the motion capture system, respectively. CONCLUSION: The proposed algorithm provides valid and repeatable spatiotemporal gait parameter measurements and offers a promising tool for clinical gait analysis. Further studies are warranted to test the algorithm in people with impaired gait.
INTRODUCTION: A ground reaction force decomposition algorithm based on large force platform measurements has recently been developed to analyze ground reaction forces under each foot during the double support phase of gait. However, its accuracy for the measurement of the spatiotemporal gait parameters remains to be established. OBJECTIVE: The aim of the present study was to establish the agreement between the spatiotemporal gait parameters obtained using (1) a walkway (composed of six large force platforms) and the newly developed algorithm, and (2) an optoelectronic motion capture system. METHODS: Twenty healthy children and adolescents (age range: 6-17 years) and 19 healthy adults (age range: 19-51 years) participated in this study. They were asked to walk at their preferred speed and at a speed that was faster than the preferred one. Each participant performed three blocks of three trials in each of the two walking speed conditions. RESULTS: The spatiotemporal gait parameters measured with the algorithm did not differ by more than 2.5% from those obtained with the motion capture system. The limits of agreement represented between 3% and 8% of the average spatiotemporal gait parameters. Repeatability of the algorithm was slightly higher than that of the motion capture system as the coefficient of variations ranged from 2.5% to 6%, and from 1.5% to 3.5% for the algorithm and the motion capture system, respectively. CONCLUSION: The proposed algorithm provides valid and repeatable spatiotemporal gait parameter measurements and offers a promising tool for clinical gait analysis. Further studies are warranted to test the algorithm in people with impaired gait.