Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks
Analyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes. The current state-of-the-art methodologies for rowing performance analysis involve the installation of dedicated instrumented equipment,...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/279 |
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| author | Lorenzo Pitto Frédéric R. Simon Geoffrey N. Ertel Gérome C. Gauchard Guillaume Mornieux |
| author_facet | Lorenzo Pitto Frédéric R. Simon Geoffrey N. Ertel Gérome C. Gauchard Guillaume Mornieux |
| author_sort | Lorenzo Pitto |
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| description | Analyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes. The current state-of-the-art methodologies for rowing performance analysis involve the installation of dedicated instrumented equipment, with the most commonly employed systems being PowerLine and BioRow. This procedure can be both expensive and time-consuming, thus limiting trainers’ ability to monitor athletes. In this study, we developed an easier-to-install and cheaper method for estimating rowers’ forces and powers based only on cable position sensors for ergometer rowing and inertial measurement units (IMUs) and GPS for scull rowing. We used data from 12 and 11 rowers on ergometer and on boat, respectively, to train a long short-term memory (LSTM) network. The LSTM was able to reconstruct the forces and power at the gate with an overall mean absolute error of less than 5%. The reconstructed forces and power were able to reveal inter-subject differences in technique, with an accuracy of 93%. Performing leave-one-out validation showed a significant increase in error, confirming that more subjects are needed in order to develop a tool that could be generalizable to external athletes. |
| format | Article |
| id | doaj-art-3c314597dafe451987a202f033cbcdc8 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3c314597dafe451987a202f033cbcdc82025-01-10T13:21:27ZengMDPI AGSensors1424-82202025-01-0125127910.3390/s25010279Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural NetworksLorenzo Pitto0Frédéric R. Simon1Geoffrey N. Ertel2Gérome C. Gauchard3Guillaume Mornieux4Development Adaptation Handicap (DevAH) Research Unit, Université de Lorraine, 54000 Nancy, FranceDevelopment Adaptation Handicap (DevAH) Research Unit, Université de Lorraine, 54000 Nancy, FranceDevelopment Adaptation Handicap (DevAH) Research Unit, Université de Lorraine, 54000 Nancy, FranceDevelopment Adaptation Handicap (DevAH) Research Unit, Université de Lorraine, 54000 Nancy, FranceDevelopment Adaptation Handicap (DevAH) Research Unit, Université de Lorraine, 54000 Nancy, FranceAnalyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes. The current state-of-the-art methodologies for rowing performance analysis involve the installation of dedicated instrumented equipment, with the most commonly employed systems being PowerLine and BioRow. This procedure can be both expensive and time-consuming, thus limiting trainers’ ability to monitor athletes. In this study, we developed an easier-to-install and cheaper method for estimating rowers’ forces and powers based only on cable position sensors for ergometer rowing and inertial measurement units (IMUs) and GPS for scull rowing. We used data from 12 and 11 rowers on ergometer and on boat, respectively, to train a long short-term memory (LSTM) network. The LSTM was able to reconstruct the forces and power at the gate with an overall mean absolute error of less than 5%. The reconstructed forces and power were able to reveal inter-subject differences in technique, with an accuracy of 93%. Performing leave-one-out validation showed a significant increase in error, confirming that more subjects are needed in order to develop a tool that could be generalizable to external athletes.https://www.mdpi.com/1424-8220/25/1/279machine learningrowingIMUkineticsLSTM |
| spellingShingle | Lorenzo Pitto Frédéric R. Simon Geoffrey N. Ertel Gérome C. Gauchard Guillaume Mornieux Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks Sensors machine learning rowing IMU kinetics LSTM |
| title | Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks |
| title_full | Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks |
| title_fullStr | Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks |
| title_full_unstemmed | Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks |
| title_short | Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks |
| title_sort | estimation of forces and powers in ergometer and scull rowing based on long short term memory neural networks |
| topic | machine learning rowing IMU kinetics LSTM |
| url | https://www.mdpi.com/1424-8220/25/1/279 |
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