Wearable Sensors for In-Field Running Gait Analysis & Intervention

Author: ORCID icon orcid.org/0000-0002-5702-9184
DeJong, Alexandra, Education - School of Education and Human Development, University of Virginia
Hertel, Jay, CU-Kinesiology, University of Virginia

Running-related injuries are extremely prevalent among recreational and competitive runners alike. Exercise-related lower leg pain (ERLLP) remains among the most prevalent running-related injuries, and while there is information on biomechanical contributors to injury progression in controlled laboratory environments, little is known about injured runners’ biomechanics during outdoor running. Biomechanical features identified in ERLLP runners in natural settings may be used to drive objective gait-training interventions to advance clinical management. Outdoor assessments using wearable sensors and wellness screening can additionally be used to prospectively investigate contributing factors to running-related injuries.
The purpose of manuscript 1 was to utilize a machine learning feature extraction analysis to identify biomechanical features among runners with ERLLP compared to healthy runners during outdoor running using wearable sensors. We identified that runners with ERLLP had increased and more variable contact time, and that contact time differences between groups was dependent upon pace, signifying that subsequent gait-training interventions should be individualized for each patient.
The purpose of manuscript 2 was to was to assess the effects of randomized control trial assessing the effects of a 4-week outdoor gait-training intervention using wearable sensors to reduce contact time in conjunction with a home exercise program (FBHE) compared to home exercises alone (HE) for runners with ERLLP on patient-reported pain, function, and outdoor running biomechanics. We identified that the FBHE intervention was superior to HE alone for improving patients’ pain and function, reducing contact time, and increasing cadence at follow-up timepoints compared to baseline and compared to the HE group.
The purpose of manuscript 3 was to prospectively assess gait biomechanics and wellness among Division-1 cross-country athletes over the course of a single competitive season. We identified that stride length, impact, pace, contact time, mileage, and running a meet the prior day were all significantly associated with athletes’ perceived exertion, and that contact time and braking forces were related to athlete wellness. Stride length, loading, cadence, contact time, and pronation velocity were found to differ among injured athletes in the two recorded days leading up to injury compared to healthy teammates.
Implementing wearable sensors into gait assessments allowed us to quantify biomechanical deficiencies in runners’ natural settings. Using this data, we were able to design an objective, data-driven gait-training program that appeared to be superior to traditional clinical management techniques. We were additionally able to identify several biomechanical factors that were evident among runners that developed running-related injuries over time, that serves as a foundation for future hypothesis-driven assessments to aid in injury assessments and interventions.

PHD (Doctor of Philosophy)
biofeedback, gait-training, gait analysis, running, medial tibial stress syndrome, wearable sensors
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