Estimation of horizontal running power using foot-worn inertial measurement units Article Swipe
Salil Apte
,
Mathieu Falbriard
,
Frédéric Meyer
,
Grégoire P. Millet
,
Vincent Grémeaux
,
Kamiar Aminian
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fbioe.2023.1167816
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fbioe.2023.1167816
Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of the gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7% ± 12.5% and 3.2% ± 13.4%, respectively. The eccentric phase was considered relevant for downhill running, wherein the elastic net model provided the lowest error of 1.8% ± 14.1%. Results showed a similar performance across a range of different speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for the estimating horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets the requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot-worn IMUs.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fbioe.2023.1167816
- https://www.frontiersin.org/articles/10.3389/fbioe.2023.1167816/pdf
- OA Status
- gold
- Cited By
- 2
- References
- 79
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381930243
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381930243Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fbioe.2023.1167816Digital Object Identifier
- Title
-
Estimation of horizontal running power using foot-worn inertial measurement unitsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-22Full publication date if available
- Authors
-
Salil Apte, Mathieu Falbriard, Frédéric Meyer, Grégoire P. Millet, Vincent Grémeaux, Kamiar AminianList of authors in order
- Landing page
-
https://doi.org/10.3389/fbioe.2023.1167816Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fbioe.2023.1167816/pdfDirect 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.frontiersin.org/articles/10.3389/fbioe.2023.1167816/pdfDirect OA link when available
- Concepts
-
Accelerometer, Simulation, Computer science, Artificial neural network, Range (aeronautics), Gait, Power (physics), Inertial measurement unit, Gyroscope, Artificial intelligence, Engineering, Operating system, Quantum mechanics, Aerospace engineering, Physiology, Biology, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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79Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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