Biomechanical load assessments are becoming increasingly important in the sporting community; however, there are still numerous difficulties in monitoring them in a field environment outside of specialized biomechanical monitoring laboratories. Inertial Measurements Units (IMUs) have been showing promising results in the modeling of biomechanical variables. This study explores the application of an artificial neural network (ANN) in the estimation of runners' vertical ground reaction forces (GRFs) based on the accelerometry collected from two wearable motion sensors developed in-house and attached on the shanks. Data collected from fourteen runners running at three different speeds (8, 10, 12 km/h) were used to train and validate the ANN. Predictions were compared against gold-standard measurements from a pair of pressure in-soles. Root mean square error (RMSE) was used to evaluate the performance of the models. Further investigations, e.g., the use of principal components analysis (PCA) and the impact on the estimation of several GRF-related variables, were carried out to provide useful insights regarding the portability of the model to low-power resource-constrained devices. Findings indicate that ANNs in conjunction with accelerometry may be used to compute vertical ground reaction forces (RMSE: 0.148 BW) and related loading metrics in running accurately.
|Title of host publication||IEEE Sensors, SENSORS 2020 - Conference Proceedings|
|Publication status||Published - 9 Dec 2020|
|Event||2020 IEEE Sensors, SENSORS 2020 - Virtual, Rotterdam, Netherlands|
Duration: 25 Oct 2020 → 28 Oct 2020
|Name||Proceedings of IEEE Sensors|
|Conference||2020 IEEE Sensors, SENSORS 2020|
|Period||25/10/20 → 28/10/20|
Bibliographical noteCopyright © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Funding Information: This publication was supported by Enterprise Ireland and Setanta College Ltd under grant agreement no. IP 2017 0606. This project is co-funded by the European Regional Development Fund (ERDF) under Ireland’s European Structural and Investment Funds Programmes 2014-2020. Aspects of this publication have emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 12/RC/2289-P2 INSIGHT-2 which is co-funded under the ERDF.
- Ground Reaction Forces