Abstract
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.
Highlights
Running is one of the most common recreational activities around the world but despite its popularity, each year approximately 50% of runners experience a running-related musculoskeletal injury [1,2,3]
A handful of studies have investigated the effect of environmental weather conditions on running performance, but none have investigated whether gait biomechanics change as a result of environmental weather
The purpose of this study was to develop a classification model based on subjectspecific changes in biomechanical running patterns across two different environmental weather conditions using data obtained from wearable sensors in out-of-laboratory environments
Summary
Running is one of the most common recreational activities around the world but despite its popularity, each year approximately 50% of runners experience a running-related musculoskeletal injury [1,2,3]. The etiology of overuse running injuries is multifactorial, and can result from the interaction of many extrinsic factors, such as environmental conditions, running surface, footwear, and weekly training mileage, as well as intrinsic risk factors such as age, foot strike pattern, and gait biomechanics [1,2,3,4]. Ely et al, [9] reported a progressive reduction in marathon performance as temperatures increased from 5 to 25 degrees C, for both males and females and across competitive and recreational runners, but performance was more negatively affected for slower runners These studies suggest that weather can affect both physiological and mechanical aspects of running gait. It is possible that different weather conditions may be associated with concomitant changes in gait biomechanical running patterns, to our knowledge no study has directly investigated this hypothesis
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