Inertial Sensor Location for Ground Reaction Force and Gait Event Detection Using Reservoir Computing in Gait

Sara Havashinezhadian (), Laurent Chiasson-Poirier, Julien Sylvestre and Katia Turcot
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Sara Havashinezhadian: Interdisciplinary Center for Research in Rehabilitation and Social Integration (CIRRIS), Department of Kinesiology, Faculty of Medicine, Université Laval, Quebec, QC G1V 0A6, Canada
Laurent Chiasson-Poirier: Department of Mechanical Engineering, Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Julien Sylvestre: Department of Mechanical Engineering, Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Katia Turcot: Interdisciplinary Center for Research in Rehabilitation and Social Integration (CIRRIS), Department of Kinesiology, Faculty of Medicine, Université Laval, Quebec, QC G1V 0A6, Canada

IJERPH, 2023, vol. 20, issue 4, 1-25

Abstract: Inertial measurement units (IMUs) have shown promising outcomes for estimating gait event detection (GED) and ground reaction force (GRF). This study aims to determine the best sensor location for GED and GRF prediction in gait using data from IMUs for healthy and medial knee osteoarthritis (MKOA) individuals. In this study, 27 healthy and 18 MKOA individuals participated. Participants walked at different speeds on an instrumented treadmill. Five synchronized IMUs (Physilog ® , 200 Hz) were placed on the lower limb (top of the shoe, heel, above medial malleolus, middle and front of tibia, and on medial of shank close to knee joint). To predict GRF and GED, an artificial neural network known as reservoir computing was trained using combinations of acceleration signals retrieved from each IMU. For GRF prediction, the best sensor location was top of the shoe for 72.2% and 41.7% of individuals in the healthy and MKOA populations, respectively, based on the minimum value of the mean absolute error (MAE). For GED, the minimum MAE value for both groups was for middle and front of tibia, then top of the shoe. This study demonstrates that top of the shoe is the best sensor location for GED and GRF prediction.

Keywords: ground reaction force; gait event detection; inertial measurement unit; reservoir computing; sensor location (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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