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

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.

New Considerations for Collecting Biomechanical Data Using Wearable Sensors: The Effect of Different Running Environments

“…Despite the use of 10fold cross-validation of the training dataset to attempt to improve generalizability of classification, the model slightly overfit to the training dataset as there was lower classification accuracy for the independent testing dataset compared to the 10-fold crossvalidation of the training dataset. Regarding real-world usability, previous studies that have classified IMU-generated running and walking patterns have consistently reported classification accuracy greater than 80% (Kobsar et al, 2014(Kobsar et al, , 2015Phinyomark et al, 2014;Ahamed et al, 2018Ahamed et al, , 2019Benson et al, 2018b;Clermont et al, 2018). Thus, the reported 93.17% accuracy for the training dataset and 83.81% accuracy for the independent testing dataset in the current study suggests that this classification mechanism has practical use.…”

“…Yet, test participant 16 had perfect classification accuracy in the sidewalk condition as their anterior-posterior variability in the sidewalk condition was even greater than their treadmill value. Therefore, the misclassifications observed in this study highlight the potential strength of subject-specific models of running biomechanics to monitor changes in an individual's running biomechanics (Ahamed et al, 2018(Ahamed et al, , 2019Benson et al, 2019) and should be further investigated in future studies.…”

“…The observed changes in running patterns in different running environments are likely due to the consistency of the surfaces and/or speed in each environment. For example, a treadmill offers a smooth and consistent running surface and a constant speed for every step, whereas outdoor running presents more variable conditions with opportunities for changes in speed, surface, inclination, turns in the running path, other pedestrians/runners, and/or changes in weather or temperature (Ahamed et al, 2017(Ahamed et al, , 2018Benson et al, 2019). This lack of consistency likely contributed to the decrease in regularity in the vertical and medial-lateral dimensions, and changes in the ratios of the magnitude of the acceleration.…”

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

“…Usually, gait-related studies are accomplished by normal sensors or IMUs or video and rarely, commercialized motion capture systems were explored. Apart from walking gait, running gait patterns were studied using wearable sensors and a Random Forest-based model [41].…”

Design of a Low-Cost, Wearable Device for Kinematic Analysis in Physical Therapy Settings

“…Wearable devices are becoming increasingly common in sports applications to provide biomechanical analysis and measure sports performance. [1][2][3][4] We have developed a low-cost IMU-based wearable device to be used by physical therapy (PT) patients during exercise. The device provides exercise kinematic data that can be used to aid patients in improving exercise technique.…”