Running speed modulates joint kinetics and ground reaction forces during outdoor running: a wearable sensor study

Abstract

Wearable sensors, such as inertial measurement units (IMUs), enable biomechanical analyses of running in real-world conditions. While the effects of running speed on joint kinetics and ground reaction forces (GRFs) have been extensively studied in laboratories, field-based evidence remains scarce. This study used IMU data and machine learning to analyze the influence of different endurance running speeds on lower-limb joint moments and GRFs during outdoor running. Twenty-nine recreational runners performed an incremental speed protocol (8–13 km/h) on a 400 m outdoor track while wearing five lower-body IMUs. From the sensor data, 3D joint moment (ankle, knee, and hip) and GRF time-series were estimated using an extended version of a previously validated convolutional neural network (CNN). Speed differences were analyzed using statistical parametric mapping (SPM) methods. Running speed affected all kinetic parameters during most of the stance phase. While GRFs, ankle and hip moments increased significantly across all speed increments, knee moments were mostly unaffected by speed increases beyond 10 km/h. These findings suggest that reducing running speed may help mitigate ankle- and hip-related loading, which could be relevant for injury prevention. The observed speed-dependent effects on running kinetics were consistent with laboratory-based findings, reinforcing the validity of our approach and supporting the feasibility of IMU-based methods for biomechanical analysis in ecological conditions. This study demonstrated the potential of wearable sensors and machine learning to complement traditional lab-based methods and enhance our understanding of running biomechanics in real-world settings.