Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study.
Accurate assessment of training status in team sports is crucial for optimising performance and reducing injury risk. This pilot study investigates the feasibility of using machine learning (ML) models to estimate oxygen uptake (VO2) with wearable sensors during team sports activities. Six healthy m...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0319760 |
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| _version_ | 1850176186663043072 |
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| author | Dermot Sheridan Arne Jaspers Dinh Viet Cuong Tim Op De Beéck Niall M Moyna Toon T de Beukelaar Mark Roantree |
| author_facet | Dermot Sheridan Arne Jaspers Dinh Viet Cuong Tim Op De Beéck Niall M Moyna Toon T de Beukelaar Mark Roantree |
| author_sort | Dermot Sheridan |
| collection | DOAJ |
| description | Accurate assessment of training status in team sports is crucial for optimising performance and reducing injury risk. This pilot study investigates the feasibility of using machine learning (ML) models to estimate oxygen uptake (VO2) with wearable sensors during team sports activities. Six healthy male team sports athletes participated in the study. Data were collected using inertial measurement units (IMU), heart rate monitors, and breathing rate sensors during incremental fitness tests. The performance of different ML models, including multiple linear regression (MLR), XGBoost, and deep learning models (LSTM, CNN, MLP), was compared using raw and engineered features from IMU data. Results indicate that while LSTM models with raw IMU data provided the most accurate predictions (RMSE: 4.976, MAE: 3.698 [Formula: see text]), MLR models remained competitive, especially with engineered features. Multi-sensor configurations, particularly those including sensors on the torso and limbs, enhanced prediction accuracy. The findings demonstrate the potential of ML models to monitor VO2 noninvasively in real-time, offering valuable insights into the internal physiological demand during team sports activities. |
| format | Article |
| id | doaj-art-8fb0fe74dd1d47f387edf0c9208738c8 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-8fb0fe74dd1d47f387edf0c9208738c82025-08-20T02:19:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01204e031976010.1371/journal.pone.0319760Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study.Dermot SheridanArne JaspersDinh Viet CuongTim Op De BeéckNiall M MoynaToon T de BeukelaarMark RoantreeAccurate assessment of training status in team sports is crucial for optimising performance and reducing injury risk. This pilot study investigates the feasibility of using machine learning (ML) models to estimate oxygen uptake (VO2) with wearable sensors during team sports activities. Six healthy male team sports athletes participated in the study. Data were collected using inertial measurement units (IMU), heart rate monitors, and breathing rate sensors during incremental fitness tests. The performance of different ML models, including multiple linear regression (MLR), XGBoost, and deep learning models (LSTM, CNN, MLP), was compared using raw and engineered features from IMU data. Results indicate that while LSTM models with raw IMU data provided the most accurate predictions (RMSE: 4.976, MAE: 3.698 [Formula: see text]), MLR models remained competitive, especially with engineered features. Multi-sensor configurations, particularly those including sensors on the torso and limbs, enhanced prediction accuracy. The findings demonstrate the potential of ML models to monitor VO2 noninvasively in real-time, offering valuable insights into the internal physiological demand during team sports activities.https://doi.org/10.1371/journal.pone.0319760 |
| spellingShingle | Dermot Sheridan Arne Jaspers Dinh Viet Cuong Tim Op De Beéck Niall M Moyna Toon T de Beukelaar Mark Roantree Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study. PLoS ONE |
| title | Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study. |
| title_full | Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study. |
| title_fullStr | Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study. |
| title_full_unstemmed | Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study. |
| title_short | Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study. |
| title_sort | estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data a pilot study |
| url | https://doi.org/10.1371/journal.pone.0319760 |
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