Journal article 1048 views 119 downloads
Determining jumping performance from a single body-worn accelerometer using machine learning
,
Jonathon Neville,
Huw Summers ,
Paul Rees
PLOS ONE, Volume: 17, Issue: 2, Start page: e0263846
Swansea University Authors:
Mark White, Neil Bezodis , Huw Summers , Paul Rees
-
PDF | Version of Record
© 2022 White et al. This is an open access article distributed under the terms of the Creative Commons Attribution License
Download (2.13MB)
DOI (Published version): 10.1371/journal.pone.0263846
Abstract
External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have no...
| Published in: | PLOS ONE |
|---|---|
| ISSN: | 1932-6203 |
| Published: |
Public Library of Science (PLoS)
2022
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa59326 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing),sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions. |
|---|---|
| College: |
Faculty of Science and Engineering |
| Funders: |
The authors received no specific funding for this work. |
| Issue: |
2 |
| Start Page: |
e0263846 |