Predicting Upper body Muscle Activation Patterns in Paralympic Cross-Country Skiing Using Neural Networks and Accelerometer Data

Abstract Paralympic cross-country skiing is a competitive and physically demanding sport developed for individuals with physical disabilities. In addition to good training and endurance, sports equipment is a key factor in achieving success. The design of sports equipment must be customized to accommodate specific impairments. Furthermore, biomechanical and neurophysiological factors need to be considered when designing equipment such as ski sledges. Among other neurophysiological factors, muscle activity, typically measured using electromyography (EMG), plays a crucial role. However, due to the high level of dynamic movement in the sport, EMG measurements are not always feasible. This study explores the possibility of estimating EMG data using neural networks and acceleration data. A feedforward neural network model was created and trained to predict upper body muscle activation from acceleration data. Validation of the model using statistical metrics yielded promising results, suggesting its effective use in predicting muscle activity. This research sets the stage for enhancing understanding and optimizing equipment in Paralympic cross-country skiing, ultimately enhancing the performance of para-athletes.