Sprint Assessment Using Machine Learning and a Wearable Accelerometer

Gurchiek, Reed D.; Don, Hasthika S. Rupasinghe Arachchige; Watagoda, Lasanthi C. R. Pelawa; McGinnis, Ryan S.; Werkhoven, Herman van; Needle, Alan R.; McBride, Jeffrey M.; Arnholt, Alan T.

doi:10.1123/jab.2018-0107

Cited by 17 publications

(11 citation statements)

References 22 publications

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“…The most dynamic action in this gait group is sprint. An accelerometer positioned on the sprinters' waist was used in [27] for the assessment of sprint based on the regression machine learning method. Mertens et al [28] employed sophisticated validation methods including laser pistols and real-time kinematic GPS systems for the measurement of the sprint velocity using only one IMU with an integrated accelerometer and gyroscope.…”

Section: Resultsmentioning

confidence: 99%

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

Taborri

Keogh

Kos

et al. 2020

Applied Bionics and Biomechanics

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In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes’ performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes’ performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment.

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Section: Resultsmentioning

confidence: 99%

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

Taborri

Keogh

Kos

et al. 2020

Applied Bionics and Biomechanics

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“…Since an HC event produces a local maximum either on the pitch or on the yaw angle of the same foot, only N hurdles elements in HC θL ∪ HC ψL and HC θR ∪ HC ψR sets were considered as true HC events. To select the best HC candidates among all the elements in HC θL ∪ HC ψL and HC θR ∪ HC ψR , we normalized the absolute values of the peaks as in Equations (11) and (12) (only the equations for the left foot are shown): (11) ψ HC ψL = ψ HC ψL − M ψL /I ψL (12) Here, M θL is the median of the left foot pitch angle over the entire trial, M ψL the median of the yaw angle, I θL the interquartile range (IQR) of the pitch angle, and I ψL the IQR of the yaw angle.…”

Section: Orient: Orientation 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%

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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“…feedback or dynamic). Incorporation of domain knowledge in regression has been suggested for other biomechanics applications [103], and as shown in [36], a good understanding of system dynamics can directly inform kernel structure in Gaussian process regression. For these reasons, hybrid methods using both physics-based and machine learning techniques in concert are being proposed in other fields including climate sciences [104], GPS-inertial navigation [105], and general chaotic processes [106].…”

Section: Towards a Hybrid Approachmentioning

confidence: 99%

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Gurchiek¹,

Cheney²,

McGinnis³

2019

Preprint

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Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

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Sprint Assessment Using Machine Learning and a Wearable Accelerometer

Cited by 17 publications

References 22 publications

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

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

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

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