Estimation of Mechanical Power Output Employing Deep Learning on Inertial Measurement Data in Roller Ski Skating Article Swipe
Md. Zia Uddin
,
Trine M. Seeberg
,
Jan Kocbach
,
Anders E. Liverud
,
Víctor González
,
Øyvind Sandbakk
,
Frédéric Meyer
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21196500
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21196500
The ability to optimize power generation in sports is imperative, both for understanding and balancing training load correctly, and for optimizing competition performance. In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs). Thirteen athletes conducted roller ski skating trials on a treadmill with varying incline and speed. The acceleration and gyroscope data collected with the IMUs were run through statistical feature processing, before being used by the deep learning model to estimate power output. The model was thereafter used for prediction of power from test data using two approaches. First, a user-dependent case was explored, reaching a power estimation within 3.5% error. Second, a user-independent case was developed, reaching an error of 11.6% for the power estimation. Finally, the LSTM model was compared to two other machine learning models and was found to be superior. In conclusion, the user-dependent model allows for precise estimation of roller skiing power output after training the model on data from each athlete. The user-independent model provides less accurate estimation; however, the accuracy may be sufficient for providing valuable information for recreational skiers.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21196500
- https://www.mdpi.com/1424-8220/21/19/6500/pdf?version=1632908132
- OA Status
- gold
- Cited By
- 10
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3204821959
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3204821959Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21196500Digital Object Identifier
- Title
-
Estimation of Mechanical Power Output Employing Deep Learning on Inertial Measurement Data in Roller Ski SkatingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-29Full publication date if available
- Authors
-
Md. Zia Uddin, Trine M. Seeberg, Jan Kocbach, Anders E. Liverud, Víctor González, Øyvind Sandbakk, Frédéric MeyerList of authors in order
- Landing page
-
https://doi.org/10.3390/s21196500Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/21/19/6500/pdf?version=1632908132Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/21/19/6500/pdf?version=1632908132Direct OA link when available
- Concepts
-
Accelerometer, Computer science, Artificial neural network, Gyroscope, Power (physics), Artificial intelligence, Deep learning, Inertial measurement unit, Simulation, Feature (linguistics), Machine learning, Engineering, Physics, Operating system, Philosophy, Quantum mechanics, Aerospace engineering, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 3, 2023: 1, 2022: 4Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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