This study explored the use of artificial neural networks in the estimation of runners' kinetics from lower body kinematics. Three supervised feed-forward artificial neural networks with one hidden layer each were modelled and assigned individually with the mapping of a single force component. Number of training epochs, batch size and dropout rate were treated as modelling hyper-parameters and their values were optimised with a grid search. A public data set of twenty-eight professional athletes containing running trails of different speeds (2.5 m/sec, 3.5 m/sec and 4.5 m/sec) was employed to train and validate the networks. Movements of the lower limbs were captured with twelve motion capture cameras and an instrumented dual-belt treadmill. The acceleration of the shanks was fed to the artificial neural networks and the estimated forces were compared to the kinetic recordings of the instrumented treadmill. Root mean square error was used to evaluate the performance of the models. Predictions were accompanied with low errors: 0.134 BW for the vertical, 0.041 BW for the anteroposterior and 0.042 BW for the mediolateral component of the force. Vertical and anteroposterior estimates were independent of running speed (p=0.233 and p=.058, respectively), while mediolateral results were significantly more accurate for low running speeds (p=0.010). The maximum force mean error between measured and estimated values was found during the vertical active peak (0.114 ± 0.088 BW). Findings indicate that artificial neural networks in conjunction with accelerometry may be used to compute three-dimensional ground reaction forces in running.INDEX TERMS Accelerometry, artificial neural networks, human biomechanics, motion analysis, kinematics, sports performance.
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 insoles. 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.
Athletic performance, technique assessment, and injury prevention are all important aspects in sports for both professional and amateur athletes. Wearable technology is attracting the research community’s interest because of its capability to provide real-time biofeedback to coaches and athletes when on the field and outside of more restrictive laboratory conditions. In this paper, a novel wearable motion sensor-based system has been designed and developed for athletic performance assessment during running and jumping tasks. The system consists of a number of components involving embedded systems (hardware and software), back-end analytics, information and communications technology (ICT) platforms, and a graphical user interface for data visualization by the coach. The system is able to provide automatic activity recognition, estimation of running and jumping metrics, as well as vertical ground reaction force (GRF) predictions, with sufficient accuracy to provide valuable information as regards training outcomes. The developed system is low-power, sufficiently small for real-world scenarios, easy to use, and achieves the specified communication range. The system’s high sampling rate, levels of accuracy and performance enables it as a performance evaluation tool able to support coaches and athletes in their real-world practice.
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