An adaptive filtering algorithm to estimate sprint velocity using a single inertial sensor

Gurchiek, Reed D.; McGinnis, Ryan S.; Needle, Alan R.; McBride, Jeffrey M.; Werkhoven, Herman van

doi:10.1007/s12283-018-0285-y

Cited by 9 publications

(10 citation statements)

References 27 publications

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“…N hurdles . Equations (6)- (9) show how the exact times of the HC were obtained based on the selected i k periods of each parameter:…”

Section: Temp: Temporal Event-based Detectionmentioning

confidence: 99%

“…These studies differ in terms of sensor configuration, sensor location, and type of parameter measured [1]. Several groups have used inertial sensors in sprint running to characterize temporal parameters [2][3][4], body-segment orientation [5,6], ground reaction forces [7,8], and speed [9][10][11]. Surprisingly, only a few studies used MIMU to quantify spatiotemporal parameters in hurdle races.…”

Section: Introductionmentioning

confidence: 99%

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Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensors

Falbriard

Mohr

Aminian

2020

Sensors

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This research aimed to determine whether: (1) shoe-worn magnetic and inertial sensors can be used to detect hurdle clearance and identify the leading leg in 400-m hurdles, and (2) to provide an analysis of the hurdlers’ spatiotemporal parameters in the intervals defined by the hurdles’ position. The data set is composed of MIMU recordings of 15 athletes in a competitive environment. The results show that the method based on the duration of the flight phase was able to detect hurdle clearance and identify the leading leg with 100% accuracy. Moreover, by combining the swing phase duration with the orientation of the foot, we achieved, in unipedal configuration, 100% accuracy in hurdle clearance detection, and 99.7% accuracy in the identification of the leading leg. Finally, this study provides statistical evidence that contact time significantly increases, while speed and step frequency significantly decrease with time during 400 m hurdle races.

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“…N hurdles . Equations (6)- (9) show how the exact times of the HC were obtained based on the selected i k periods of each parameter:…”

Section: Temp: Temporal Event-based Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensors

Falbriard

Mohr

Aminian

2020

Sensors

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“…For the v max , the limits of agreement (L.O.A.) for the Bland–Altman plot ranged from −1.20 to 0.89 m/s, this range being smaller than one (−1.25 to 1.64 m/s) presented in Gurchiek et al (2018) . L.O.A for the v 0 parameter varied from −1.01 to 0.67 m/s, which is similar in extent to one (−0.7 to 1.3 m/s) showed in Samozino et al (2016) .…”

Section: Discussionmentioning

confidence: 77%

“…Thus, the velocity estimation algorithm based on GNSS and IMU fusion is robust in terms of accuracy and precision, despite the inaccuracies in the GNSS velocity. None of the previous works on estimation of sprint mechanics ( Samozino et al, 2016 ; Stanton et al, 2016 ; Gurchiek et al, 2018 ; Setuain et al, 2018 ) conducted a validation of the instantaneous velocity or the overall profile with respect to a speed radar. Stanton et al (2016) validated the mean velocity over an entire sprint, while ( Gurchiek et al, 2018 ) validated the mean velocity over 10 m intervals.…”

Section: Discussionmentioning

confidence: 99%

“…None of the previous works on estimation of sprint mechanics ( Samozino et al, 2016 ; Stanton et al, 2016 ; Gurchiek et al, 2018 ; Setuain et al, 2018 ) conducted a validation of the instantaneous velocity or the overall profile with respect to a speed radar. Stanton et al (2016) validated the mean velocity over an entire sprint, while ( Gurchiek et al, 2018 ) validated the mean velocity over 10 m intervals. This method is the first one to provide validated instantaneous analysis of the sprint velocity profile over multiple distances, and thus it is not possible to compare our results with the state-of-the-art.…”

Section: Discussionmentioning

confidence: 99%

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A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

Apte

Meyer

Grémeaux

et al. 2020

Front. Bioeng. Biotechnol.

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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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Wearables for Running Gait Analysis: A Systematic Review

et al. 2022

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Background Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as three-dimensional motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance. Objectives We aimed to systematically review the available literature investigating how wearable technology is being used for running gait analysis in adults. Methods A systematic search of the literature was conducted in the following scientific databases: PubMed, Scopus, Web of Science and SPORTDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis and key findings. Results A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units (n = 62) [i.e. a combination of accelerometers, gyroscopes and magnetometers in a single unit] or solely accelerometers (n = 40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors (n = 93), using a treadmill (n = 62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g. age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards. Conclusions This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one inertial measurement unit sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and the reliability of devices and suitability of gait outcomes. Clinical Trial Registration CRD42021235527.

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An adaptive filtering algorithm to estimate sprint velocity using a single inertial sensor

Cited by 9 publications

References 27 publications

Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensors

Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensors

A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

Wearables for Running Gait Analysis: A Systematic Review

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