Author:

Keywords:

Science & Technology, Technology, Engineering, Biomedical, Engineering, Ground reaction force, Trunk accelerometry, Multilayer perceptron, Biomechanical load, TRAINING LOAD, KINEMATICS, SENSOR, Acceleration, Biomechanical Phenomena, Female, Humans, Male, Mechanical Phenomena, Neural Networks, Computer, Running, Torso, Young Adult, 02 Physical Sciences, 09 Engineering, 11 Medical and Health Sciences, Biomedical Engineering, 4003 Biomedical engineering, 4207 Sports science and exercise, 5105 Medical and biological physics

Abstract:

Prediction of ground reaction force (GRF) magnitudes during running-based sports has several important applications, including optimal load prescription and injury prevention in athletes. Existing methods typically require information from multiple body-worn sensors, limiting their ecological validity, or aim to estimate discrete force parameters, limiting their ability to assess overall biomechanical load. This paper presents a neural network method to predict GRF time series from a single, commonly used, trunk-mounted accelerometer. The presented method uses a principal component analysis and multilayer perceptron (MLP) to obtain predictions. Time-series r2 coefficients with test data averaged around 0.9 for each impact, comparing favourably with alternative approaches which require additional sensors. For the impact peak, r2 was 0.74 across activities, comparing favourably with correlation analysis approaches. Several modifications, such as subject-specific training of the MLP, may help to improve results further, but the presented method can accurately predict GRF from trunk accelerometry data without requiring additional information. Results demonstrate the scope of machine learning to exploit common wearable technologies to estimate GRF in sport-specific environments.