Is machine learning and automatic classification of swimming data what unlocks the power of inertial measurement units in swimming?
| Autor: | Rebecca Pahl, Matthew T. O. Worsey, Hugo G. Espinosa, David V. Thiel, Jonathan B. Shepherd |
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| Rok vydání: | 2021 |
| Předmět: |
Inertial frame of reference
Computer science 030209 endocrinology & metabolism Physical Therapy Sports Therapy and Rehabilitation Control engineering 030229 sport sciences Athletic Performance Healthy Volunteers Power (physics) Machine Learning 03 medical and health sciences Units of measurement Wearable Electronic Devices 0302 clinical medicine Inertial measurement unit Accelerometry Humans Orthopedics and Sports Medicine Swimming |
| Zdroj: | Journal of sports sciences. 39(18) |
| ISSN: | 1466-447X |
| Popis: | Researchers have heralded the power of inertial sensors as a reliable swimmer-centric monitoring technology, however, regular uptake of this technology has not become common practice. Twenty-six elite swimmers participated in this study. An IMU (100Hz/500Hz) sensor was secured in the participant's third lumbar vertebrae. Features were extracted from swimming data using two techniques: a novel intrastroke cycle segmentation technique and conventional sliding window technique. Six supervised machine learning models were assessed on stroke prediction performance. Models trained using both feature extraction methods demonstrated high performance (≥ 0.99 weighted average precision, recall, F1-score, area under ROC curve and accuracy), low computational training times (3 seconds - bar XGB and when hyperparameters were tuned) and low computational prediction times (1 second). Significant differences were observed in weighted average stroke prediction F1-score ( |
| Databáze: | OpenAIRE |
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