A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint Article Swipe
Salil Apte
,
Frédéric Meyer
,
Vincent Grémeaux
,
Farzin Dadashi
,
Kamiar Aminian
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fbioe.2020.00838
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fbioe.2020.00838
Power-Force-Velocity profile obtained during a sprint test is crucial for designing personalized training and evaluating injury risks. Estimation of instantaneous velocity is requisite for developing these profiles and the predominant method for this estimation assumes it to have a first order exponential behavior. While this method remains appropriate for maximal sprints, the sprint velocity profile may not always show a first-order exponential behavior. Alternately, velocity profile has been estimated using inertial sensors, with a speed radar, or a smartphone application. Existing methods either relied on the exponential behavior or timing gates for drift removal, or estimated only the mean velocity. Thus, there is a need for a more flexible and appropriate approach, allowing for instantaneous velocity estimation during sprint tests. The proposed method aims to solve this problem using a sensor fusion approach, by combining the signals from wearable Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) sensors. We collected data from nine elite sprinters, equipped with a wearable GNSS-IMU sensor, who ran two trials each of 60 and 30/40 m sprints. We developed an algorithm using a gradient descent-based orientation filter, which simplified our model to a linear one-dimensional model, thus allowing us to use a simple Kalman filter (KF) for velocity estimation. We used two cascaded KFs, to segment the sprint data precisely, and to estimate the velocity and the sprint duration, respectively. We validated the estimated velocity and duration with speed radar and photocell data as reference. The median RMS error for the estimated velocity ranged from 6 to 8%, while that for the estimated sprint duration lied between 0.1 and -6.0%. The Bland-Altman plot showed close agreement between the estimated and the reference values of maximum velocity. Examination of fitting errors indicated a second order exponential behavior for the sprint velocity profile, unlike the first order behavior previously suggested in literature. The proposed sensor-fusion algorithm is valid to compute an accurate velocity profile with respect to the radar; it can compensate for and improve upon the accuracy of the individual IMU and GNSS velocities. This method thus enables the use of wearable sensors in the analysis of sprint test.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fbioe.2020.00838
- https://www.frontiersin.org/articles/10.3389/fbioe.2020.00838/pdf
- OA Status
- gold
- Cited By
- 18
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3041259281
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3041259281Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fbioe.2020.00838Digital Object Identifier
- Title
-
A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During SprintWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-18Full publication date if available
- Authors
-
Salil Apte, Frédéric Meyer, Vincent Grémeaux, Farzin Dadashi, Kamiar AminianList of authors in order
- Landing page
-
https://doi.org/10.3389/fbioe.2020.00838Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fbioe.2020.00838/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fbioe.2020.00838/pdfDirect OA link when available
- Concepts
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Inertial measurement unit, GNSS applications, Control theory (sociology), Kalman filter, Computer science, Sprint, Sensor fusion, Radar, Simulation, Global Positioning System, Artificial intelligence, Telecommunications, Control (management), Software engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 7, 2022: 4, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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