A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint
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 expon...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-08-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fbioe.2020.00838/full |
| _version_ | 1818277655344054272 |
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| author | Salil Apte Frederic Meyer Vincent Gremeaux Vincent Gremeaux Farzin Dadashi Kamiar Aminian |
| author_facet | Salil Apte Frederic Meyer Vincent Gremeaux Vincent Gremeaux Farzin Dadashi Kamiar Aminian |
| author_sort | Salil Apte |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T23:04:59Z |
| format | Article |
| id | doaj.art-09025b7fce78474cbf7f0d066775413e |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-12-12T23:04:59Z |
| publishDate | 2020-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-09025b7fce78474cbf7f0d066775413e2022-12-22T00:08:43ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852020-08-01810.3389/fbioe.2020.00838546722A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During SprintSalil Apte0Frederic Meyer1Vincent Gremeaux2Vincent Gremeaux3Farzin Dadashi4Kamiar Aminian5Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, SwitzerlandInstitute of Sport Sciences, University of Lausanne, Lausanne, SwitzerlandInstitute of Sport Sciences, University of Lausanne, Lausanne, SwitzerlandSport Medicine Unit, Division of Physical Medicine and Rehabilitation, Swiss Olympic Medical Center, Lausanne University Hospital, Lausanne, SwitzerlandGait Up S.A., Lausanne, SwitzerlandLaboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, SwitzerlandPower-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.https://www.frontiersin.org/article/10.3389/fbioe.2020.00838/fullsensor fusionsprintingfunctional capacity testwearable GNSS-IMU sensorvalidation studyvelocity profile |
| spellingShingle | Salil Apte Frederic Meyer Vincent Gremeaux Vincent Gremeaux Farzin Dadashi Kamiar Aminian A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint Frontiers in Bioengineering and Biotechnology sensor fusion sprinting functional capacity test wearable GNSS-IMU sensor validation study velocity profile |
| title | A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint |
| title_full | A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint |
| title_fullStr | A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint |
| title_full_unstemmed | A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint |
| title_short | A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint |
| title_sort | sensor fusion approach to the estimation of instantaneous velocity using single wearable sensor during sprint |
| topic | sensor fusion sprinting functional capacity test wearable GNSS-IMU sensor validation study velocity profile |
| url | https://www.frontiersin.org/article/10.3389/fbioe.2020.00838/full |
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