An exploration of machine‐learning estimation of ground reaction force from wearable sensor data
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 Lin...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/82990 |
| _version_ | 1848764553644474368 |
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| author | Hendry, Danica Leadbetter, Ryan McKee, Kristoffer Hopper, L. Wild, Catherine O’Sullivan, Peter Straker, Leon Campbell, Amity |
| author_facet | Hendry, Danica Leadbetter, Ryan McKee, Kristoffer Hopper, L. Wild, Catherine O’Sullivan, Peter Straker, Leon Campbell, Amity |
| author_sort | Hendry, Danica |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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–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. |
| first_indexed | 2025-11-14T11:21:11Z |
| format | Journal Article |
| id | curtin-20.500.11937-82990 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:21:11Z |
| publishDate | 2020 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-829902021-04-13T00:55:04Z An exploration of machine‐learning estimation of ground reaction force from wearable sensor data Hendry, Danica Leadbetter, Ryan McKee, Kristoffer Hopper, L. Wild, Catherine O’Sullivan, Peter Straker, Leon Campbell, Amity Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering machine learning inertial sensor ballet ground reaction force LANDING BIOMECHANICS DANCERS MICROSENSORS KINEMATICS FATIGUE EVENTS 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–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. 2020 Journal Article http://hdl.handle.net/20.500.11937/82990 10.3390/s20030740 English http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering machine learning inertial sensor ballet ground reaction force LANDING BIOMECHANICS DANCERS MICROSENSORS KINEMATICS FATIGUE EVENTS Hendry, Danica Leadbetter, Ryan McKee, Kristoffer Hopper, L. Wild, Catherine O’Sullivan, Peter Straker, Leon Campbell, Amity An exploration of machine‐learning estimation of ground reaction force from wearable sensor data |
| title | An exploration of machine‐learning estimation of ground reaction force from wearable sensor data |
| title_full | An exploration of machine‐learning estimation of ground reaction force from wearable sensor data |
| title_fullStr | An exploration of machine‐learning estimation of ground reaction force from wearable sensor data |
| title_full_unstemmed | An exploration of machine‐learning estimation of ground reaction force from wearable sensor data |
| title_short | An exploration of machine‐learning estimation of ground reaction force from wearable sensor data |
| title_sort | exploration of machine‐learning estimation of ground reaction force from wearable sensor data |
| topic | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering machine learning inertial sensor ballet ground reaction force LANDING BIOMECHANICS DANCERS MICROSENSORS KINEMATICS FATIGUE EVENTS |
| url | http://hdl.handle.net/20.500.11937/82990 |