Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating
Objective: The aim of this study was to provide a new machine learning method to determine temporal events and inner-cycle parameters (e.g., cycle, pole and ski contact and swing time) in cross-country roller-ski skating on the field, using a single inertial measurement unit (IMU). Methods: The deve...
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/23/9267 |
| _version_ | 1827642507955011584 |
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| author | Frédéric Meyer Magne Lund-Hansen Trine M. Seeberg Jan Kocbach Øyvind Sandbakk Andreas Austeng |
| author_facet | Frédéric Meyer Magne Lund-Hansen Trine M. Seeberg Jan Kocbach Øyvind Sandbakk Andreas Austeng |
| author_sort | Frédéric Meyer |
| collection | DOAJ |
| description | Objective: The aim of this study was to provide a new machine learning method to determine temporal events and inner-cycle parameters (e.g., cycle, pole and ski contact and swing time) in cross-country roller-ski skating on the field, using a single inertial measurement unit (IMU). Methods: The developed method is based on long short-term memory neural networks to detect the initial and final contact of the poles and skis with the ground during the cyclic movements. Eleven athletes skied four laps of 2.5 km at a low and high intensity using skis with two different rolling coefficients. They were equipped with IMUs attached to the upper back, lower back and to the sternum. Data from force insoles and force poles were used as the reference system. Results: The IMU placed on the upper back provided the best results, as the LSTM network was able to determine the temporal events with a mean error ranging from −1 to 11 ms and had a standard deviation (SD) of the error between 64 and 70 ms. The corresponding inner-cycle parameters were calculated with a mean error ranging from −11 to 12 ms and an SD between 66 and 74 ms. The method detected 95% of the events for the poles and 87% of the events for the skis. Conclusion: The proposed LSTM method provides a promising tool for assessing temporal events and inner-cycle phases in roller-ski skating, showing the potential of using a single IMU to estimate different spatiotemporal parameters of human locomotion. |
| first_indexed | 2024-03-09T17:32:16Z |
| format | Article |
| id | doaj.art-cee5f193f702422fac7995119007e14e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:32:16Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cee5f193f702422fac7995119007e14e2023-11-24T12:11:21ZengMDPI AGSensors1424-82202022-11-012223926710.3390/s22239267Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski SkatingFrédéric Meyer0Magne Lund-Hansen1Trine M. Seeberg2Jan Kocbach3Øyvind Sandbakk4Andreas Austeng5Department of Informatics, University of Oslo, 0373 Oslo, NorwayDepartment of Physical Performance, Norwegian School of Sport Science, 0806 Oslo, NorwaySINTEF Digital, Forskningsveien 1, 0373 Oslo, NorwayCentre for Elite Sports Research, Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, NorwayCentre for Elite Sports Research, Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, NorwayDepartment of Informatics, University of Oslo, 0373 Oslo, NorwayObjective: The aim of this study was to provide a new machine learning method to determine temporal events and inner-cycle parameters (e.g., cycle, pole and ski contact and swing time) in cross-country roller-ski skating on the field, using a single inertial measurement unit (IMU). Methods: The developed method is based on long short-term memory neural networks to detect the initial and final contact of the poles and skis with the ground during the cyclic movements. Eleven athletes skied four laps of 2.5 km at a low and high intensity using skis with two different rolling coefficients. They were equipped with IMUs attached to the upper back, lower back and to the sternum. Data from force insoles and force poles were used as the reference system. Results: The IMU placed on the upper back provided the best results, as the LSTM network was able to determine the temporal events with a mean error ranging from −1 to 11 ms and had a standard deviation (SD) of the error between 64 and 70 ms. The corresponding inner-cycle parameters were calculated with a mean error ranging from −11 to 12 ms and an SD between 66 and 74 ms. The method detected 95% of the events for the poles and 87% of the events for the skis. Conclusion: The proposed LSTM method provides a promising tool for assessing temporal events and inner-cycle phases in roller-ski skating, showing the potential of using a single IMU to estimate different spatiotemporal parameters of human locomotion.https://www.mdpi.com/1424-8220/22/23/9267cross-country skiingIMUwearable sensorsLSTMneural network |
| spellingShingle | Frédéric Meyer Magne Lund-Hansen Trine M. Seeberg Jan Kocbach Øyvind Sandbakk Andreas Austeng Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating Sensors cross-country skiing IMU wearable sensors LSTM neural network |
| title | Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating |
| title_full | Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating |
| title_fullStr | Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating |
| title_full_unstemmed | Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating |
| title_short | Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating |
| title_sort | inner cycle phases can be estimated from a single inertial sensor by long short term memory neural network in roller ski skating |
| topic | cross-country skiing IMU wearable sensors LSTM neural network |
| url | https://www.mdpi.com/1424-8220/22/23/9267 |
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