Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating Article Swipe
Frédéric Meyer
,
Magne Lund-Hansen
,
Trine M. Seeberg
,
Jan Kocbach
,
Øyvind Sandbakk
,
Andreas Austeng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22239267
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22239267
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22239267
- https://www.mdpi.com/1424-8220/22/23/9267/pdf?version=1669803283
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310191260
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4310191260Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s22239267Digital Object Identifier
- Title
-
Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski SkatingWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-28Full publication date if available
- Authors
-
Frédéric Meyer, Magne Lund-Hansen, Trine M. Seeberg, Jan Kocbach, Øyvind Sandbakk, Andreas AustengList of authors in order
- Landing page
-
https://doi.org/10.3390/s22239267Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/23/9267/pdf?version=1669803283Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/22/23/9267/pdf?version=1669803283Direct OA link when available
- Concepts
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Inertial measurement unit, Ground reaction force, Ranging, Standard deviation, Simulation, Artificial neural network, Engineering, Computer science, Geodesy, Artificial intelligence, Mathematics, Statistics, Physics, Geology, Telecommunications, Classical mechanics, KinematicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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