An Exploration of Machine-Learning Estimation of Ground Reaction Force from Wearable Sensor Data
| Autor: | Amity Campbell, Peter O'Sullivan, Kristoffer K. McKee, Luke Hopper, Catherine Y. Wild, Danica Hendry, Leon Straker, Ryan Leadbetter |
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| Rok vydání: | 2020 |
| Předmět: |
Support Vector Machine
inertial sensor Adolescent Mean squared error Correlation coefficient Wearable computer Kinematics lcsh:Chemical technology Models Biological Biochemistry Article Analytical Chemistry Wearable Electronic Devices Young Adult 03 medical and health sciences 0302 clinical medicine Humans lcsh:TP1-1185 Model development Force platform Dancing Electrical and Electronic Engineering Ground reaction force Exercise Instrumentation Simulation Monitoring Physiologic 030222 orthopedics Body Weight 030229 sport sciences ballet Atomic and Molecular Physics and Optics Biomechanical Phenomena Data set machine learning Female Neural Networks Computer ground reaction force |
| Zdroj: | Sensors, Vol 20, Iss 3, p 740 (2020) Sensors (Basel, Switzerland) Sensors Volume 20 Issue 3 |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s20030740 |
| Popis: | This study aimed to develop a wearable sensor system, using machine-learning models, capable of accurately estimating peak ground reaction force (GRF) during ballet jumps in the field. Female dancers (n = 30) performed a series of bilateral and unilateral ballet jumps. Dancers wore six ActiGraph Link wearable sensors (100 Hz). Data were collected simultaneously from two AMTI force platforms and synchronised with the ActiGraph data. Due to sensor hardware malfunctions and synchronisation issues, a multistage approach to model development, using a reduced data set, was taken. Using data from the 14 dancers with complete multi-sensor synchronised data, the best single sensor was determined. Subsequently, the best single sensor model was refined and validated using all available data for that sensor (23 dancers). Root mean square error (RMSE) in body weight (BW) and correlation coefficients (r) were used to assess the GRF profile, and Bland&ndash Altman plots were used to assess model peak GRF accuracy. The model based on sacrum data was the most accurate single sensor model (unilateral landings: RMSE = 0.24 BW, r = 0.95 bilateral landings: RMSE = 0.21 BW, r = 0.98) with the refined model still showing good accuracy (unilateral: RMSE = 0.42 BW, r = 0.80 bilateral: RMSE = 0.39 BW, r = 0.92). Machine-learning models applied to wearable sensor data can provide a field-based system for GRF estimation during ballet jumps. |
| Databáze: | OpenAIRE |
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