Objective: Monitoring athlete internal workload exposure, including prevention of catastrophic non-contact knee injuries, relies on the existence of a custom early-warning detection system. This system must be able to estimate accurate, reliable, and valid musculoskeletal joint loads, for sporting maneuvers in near real-time and during match play. However, current methods are constrained to laboratory instrumentation, are labor and cost intensive, and require highly trained specialist knowledge, thereby limiting their ecological validity and wider deployment. An informative next step towards this goal would be a new method to obtain ground kinetics in the field. Methods: Here we show that kinematic data obtained from wearable sensor accelerometers, in lieu of embedded force platforms, can leverage recent supervised learning techniques to predict in-game near real-time multidimensional ground reaction forces and moments (GRF/M). Competing convolutional neural network (CNN) deep learning models were trained using laboratory-derived stance phase GRF/M data and simulated sensor accelerations for running and sidestepping maneuvers derived from nearly half a million legacy motion trials. Then, predictions were made from each model driven by five sensor accelerations recorded during independent inter-laboratory data capture sessions. Results: The proposed deep learning workbench achieved correlations to ground truth, by maximum discrete GRF component, of vertical Fz 0.97, anterior Fy 0.96 (both running), and lateral Fx 0.87 (sidestepping), with the strongest mean recorded across GRF components 0.89, and for GRM 0.65 (both sidestepping). Conclusion: These best-case correlations indicate the plausibility of the approach although the range of results was disappointing. The goal to accurately estimate near real-time on-field GRF/M will be improved by the lessons learned in this study. Significance: Coaching, medical, and allied health staff could ultimately use this technology to monitor a range of joint loading indicators during game play, with the aim to minimize the occurrence of non-contact injuries in elite and community-level sports.

The benefits of differentiating between the physiological and biomechanical load-response pathways in football and other (team) sports have become increasingly recognised. In contrast to physiological loads however, the biomechanical demands of training and competition are still not well understood, primarily due to the difficulty of quantifying biomechanical loads in a field environment. Although musculoskeletal adaptation and injury are known to occur at a tissue level, several biomechanical load metrics are available that quantify loads experienced by the body as a whole, its different structures and the individual tissues that are part of these structures. This paper discusses the distinct aspects and challenges that are associated with measuring biomechanical loads at these different levels in laboratory and/or field contexts. Our hope is that through this paper, sport scientists and practitioners will be able to critically consider the value and limitations of biomechanical load metrics and will keep pursuing new methods to measure these loads within and outside the lab, as a detailed load quantification is essential to better understand the biomechanical load-response pathways that occur in the field.

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.

Running impact forces expose the body to biomechanical loads leading to beneficial adaptations, but also risk of injury. High-intensity running tasks, especially, are deemed highly demanding for the musculoskeletal system, but loads experienced during these actions are not well understood. To eventually predict GRF and understand the biomechanical loads experienced during such activities in greater detail, this study aimed to (1) examine the feasibility of using a simple two mass-spring-damper model, based on eight model parameters, to reproduce ground reaction forces (GRFs) for high-intensity running tasks and (2) verify whether the required model parameters were physically meaningful. This model was used to reproduce GRFs for rapid accelerations and decelerations, constant speed running and maximal sprints. GRF profiles and impulses could be reproduced with low to very low errors across tasks, but subtler loading characteristics (impact peaks, loading rate) were modelled less accurately. Moreover, required model parameters varied strongly between trials and had minimal physical meaning. These results show that although a two mass-spring-damper model can be used to reproduce overall GRFs for high-intensity running tasks, the application of this simple model for predicting GRFs in the field and/or understanding the biomechanical demands of training in greater detail is likely limited.

Hill-type muscle models are highly preferred as phenomenological models for musculoskeletal simulation studies despite their introduction almost a century ago. The use of simple Hill-type models in simulations, instead of more recent cross-bridge models, is well justified since computationally ‘light-weight’—although less accurate—Hill-type models have great value for large-scale simulations. However, this article aims to invite discussion on numerical instability issues of Hill-type muscle models in simulation studies, which can lead to computational failures and, therefore, cannot be simply dismissed as an inevitable but acceptable consequence of simplification. We will first revisit the basic premises and assumptions on the force–length and force–velocity relationships that Hill-type models are based upon, and their often overlooked but major theoretical limitations. We will then use several simple conceptual simulation studies to discuss how these numerical instability issues can manifest as practical computational problems. Lastly, we will review how such numerical instability issues are dealt with, mostly in an ad hoc fashion, in two main areas of application: musculoskeletal biomechanics and computer animation.