Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support

Abstract

Nowadays, modern technology is widespread in sports; therefore, finding an excellent approach to extracting knowledge from data is necessary. Machine Learning (ML) algorithms can be beneficial in biomechanical data management because they can handle a large amount of data. A fencing lunge represents an exciting scenario since it necessitates neuromuscular coordination, strength, and proper execution to succeed in a competition. However, to investigate and analyze a sports movement, it is necessary to understand its nature and goal and to identify the factors that affect its performance. The present work aims to define the best model to screen élite and novice fencers to develop further a tool to support athletes’ and trainers’ activity. We conducted a cross-sectional study in a fencing club to collect anthropometric and biomechanical data from élite and novice fencers. Wearable sensors were used to collect biomechanical data, including a wireless inertial system and four surface electromyographic (sEMG) probes. Four different ML algorithms were trained for each dataset, and the most accurate was further trained with hyperparameter tuning. The best Machine Learning algorithm was Multilayer Perceptron (MLP), which had 96.0% accuracy and 90% precision, recall, and F1-score when predicting class novice (0); and 93% precision, recall, and F1-score when predicting class élite (1). Interestingly, the MLP model has a slightly higher capacity to recognize élite fencers than novices; this is important to determine which training planning and execution are the best to achieve good performances.

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

“…EMG records precise information on muscle activity, which is then processed by ML to examine movement patterns, coordination, and biomechanics. The integration makes it possible to identify the best performance tactics and create individualized training plans for each athlete [70,71]. It can also be used in the study of muscle fatigue dynamics [44] and can potentially prevent back pain [72].…”

Special Issue: “Research on Biomedical Signal Processing”

“…Features extracted from the ECG were found to be the most relevant for anxiety classification. Aresta et al [10] combined biomechanical features and ML approaches to identify fencers' levels. In order to determine the best classifier for the novice or élite athlete class, four supervised models (extreme gradient boosting; multilayer perceptron (MLP); random forest; support vector machine) were trained and tested on biomechanical data.…”