Predicting Musculoskeletal Loading at Common Running Injury Locations Using Machine Learning and Instrumented Insoles.

Journal: Medicine and science in sports and exercise

PMID: 38857523

Abstract

INTRODUCTION: Wearables have the potential to provide accurate estimates of tissue loads at common running injury locations. Here we investigate the accuracy by which commercially available instrumented insoles (ARION; ATO-GEAR, Eindhoven, The Netherlands) can predict musculoskeletal loading at common running injury locations.

Authors

Bas VAN Hooren

NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Department of Nutrition and Movement Sciences, Maastricht, THE NETHERLANDS.
Lars VAN Rengs
Kenneth Meijer

Keywords

Achilles Tendon Adult Athletic Injuries Biomechanical Phenomena Female Foot Orthoses Humans Machine Learning Male Neural Networks, Computer Patellofemoral Joint Running Shoes Tibia Wearable Electronic Devices Weight-Bearing Young Adult

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View on PubMed Access via DOI PubMed (38857523)

Predicting Musculoskeletal Loading at Common Running Injury Locations Using Machine Learning and Instrumented Insoles.

Abstract

Authors

Keywords

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