Accelerometer-based prediction of running injury in National Collegiate Athletic Association track athletes

Kiernan, Dovin; Hawkins, David; Manoukian, Martin A.C.; McKallip, Madeline; Oelsner, Laura; Caskey, Charles F.; Coolbaugh, Crystal L.

doi:10.1016/j.jbiomech.2018.04.001

Cited by 77 publications

(87 citation statements)

References 71 publications

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“…However, due to the availability and utility of modern portable inertial measurement units (IMUs) and global positioning system (GPS), it is now possible to collect data outside of the laboratory setting [ 10 – 12 ]. Since large quantities of data can be collected using wearable devices, machine learning (ML) techniques are also needed to better understand the complexities of gait biomechanics and how concomitant changes in biomechanical patterns may be related to injury or performance [ 13 , 14 ]. Furthermore, traditional biomechanics research generally investigates potential differences between two groups using group-based analyses.…”

Section: Introductionmentioning

confidence: 99%

Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions

et al. 2018

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Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait biomechanics) and extrinsic (e.g., running surface) risk factors. However, it is unknown how changes in environmental weather conditions affect running gait biomechanical patterns since these data cannot be collected in a laboratory setting. Therefore, the purpose of this study was to develop a classification model based on subject-specific changes in biomechanical running patterns across two different environmental weather conditions using data obtained from wearable sensors in real-world environments. Running gait data were recorded during winter and spring sessions, with recorded average air temperatures of -10° C and +6° C, respectively. Classification was performed based on measurements of pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence obtained from 66,370 strides (~11,000/runner) from a group of recreational runners. A non-linear and ensemble machine learning algorithm, random forest (RF), was used to classify and compute a heuristic for determining the importance of each variable in the prediction model. To validate the developed subject-specific model, two cross-validation methods (one-against-another and partitioning datasets) were used to obtain experimental mean classification accuracies of 87.18% and 95.42%, respectively, indicating an excellent discriminatory ability of the RF-based model. Additionally, the ranked order of variable importance differed across the individual runners. The results from the RF-based machine-learning algorithm demonstrates that processing gait biomechanical signals from a single wearable sensor can successfully detect changes to an individual’s running patterns based on data obtained in real-world environments.

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Section: Introductionmentioning

confidence: 99%

Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions

et al. 2018

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“…Data gathered in-field does not have these specific limitations and can therefore also be used to establish new relationships between running technique, injuries and performance (e.g. (Kiernan et al, 2018)).…”

Section: Which Running Technique Components Should Be Measured and Momentioning

confidence: 99%

Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements

Hooren

Goudsmit

Restrepo

et al. 2019

Journal of Sports Sciences

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Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise realtime feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.

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“…For basic exposure metrics, such as time spent or steps taken while running, an analyst must apply sophisticated step identification and activity recognition algorithms. However, access to raw data can enable the estimation of important biomechanical parameters that may modify the relationship between training load and injury, such as impact shock or ground reaction forces 16–18. Some commercial devices also measure biomechanical parameters, but do so using proprietary algorithms that may not be validated against gold-standard laboratory data.…”

Section: Research-grade Wearable Sensorsmentioning

confidence: 99%

Quantifying exposure to running for meaningful insights into running-related injuries

Davis

Gruber

2019

BMJ Open Sport Exerc Med

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The very term ‘running-related overuse injury’ implies the importance of ‘use’, or exposure, to running. Risk factors for running-related injury can be better understood when exposure to running is quantified using either external or internal training loads. The advent of objective methods for quantifying exposure to running, such as global positioning system watches, smartphones, commercial activity monitors and research-grade wearable sensors, make it possible for researchers, coaches and clinicians to track exposure to running with unprecedented detail. This viewpoint discusses practical issues surrounding the use and analysis of data from such devices, including how wearable devices can be used to assess both internal and external training loads. We advocate for an integrative approach where data from multiple sources are used in combination to directly measure exposure to running in diverse settings.

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Accelerometer-based prediction of running injury in National Collegiate Athletic Association track athletes

Cited by 77 publications

References 71 publications

Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions

Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions

Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements

Quantifying exposure to running for meaningful insights into running-related injuries

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