Use of AI methods as a complement or alternative to biomechanical testing of lower limb peak torque in football

Abstract

Purpose Monitoring and assessing the level of lower limb motor skills using the Biodex System plays an important role in the training of football players and in post-traumatic rehabilitation. The aim of this study is to build and test an artificial intelligence-based model to assess the peak torque of the lower limb extensors and flexors. The model was based on real-world results in three groups: hearing (n = 19) and deaf football players (n = 28) and non-training deaf pupils (n=46). Methods The research used a 4-layer forward CNN neural network with two hidden layers with typical normalization for small data sets and Multilayer Perceptron (MLP) based on MatlabR2023a software with Neural Networks and Deep Learning toolkits and semiautomated learning algorithm selection using ML.NET Results The 70-90% accuracy shown in the article is sufficient here. AI provides a highly accurate, objective and efficient means of assessing neuromuscular performance, which can improve injury prevention and rehabilitation strategies. Conclusions The high accuracy shows that AI-based models can help with this, but their wider practical implementation requires further cross-disciplinary research. AI, and in particular MLP and CNN can support both training methods and various gaming aspects. The contribution of the research is to use an innovative approach to derive computational rules/guidelines from an explicitly given dataset and then identify the relevant physiological torque of the lower limb extensors and flexors in the knee joint. The model complements existing methodologies for describing physiology of peak torque of lower limbs with using fuzzy logic, with a so-called dynamic norm built into the model.