Evaluation of a long short-term memory (LSTM)-based algorithm for predicting central frequency and synergy activation ratio using markerless motion analysis data.

Accurate, non-invasive prediction of muscle fatigue and coordination is essential for improving exercise performance and rehabilitation strategies. This study proposed a deep learning-based algorithm that integrates surface electromyography (EMG) and markerless motion analysis to estimate muscle fatigue and intermuscular coordination during dynamic upper-limb movement. Five healthy male participants (age: 26 ± 1.73 years) performed one-arm dumbbell curls at 50% of their one-repetition maximum (1RM), during which EMG signals were collected from the biceps brachii and lateral deltoid. Muscle fatigue was evaluated using median frequency (MDF) separately for each muscle, while intermuscular coordination was quantified via the Synergy Activation Ratio (SAR), derived from non-negative matrix factorization (NMF). Markerless motion data were captured using a Kinect V2 sensor, and both EMG and motion data were used to train an LSTM model. The model demonstrated high prediction accuracy (MDF: MSE 0.0081, MAE 0.0664 for biceps; MSE 0.0102, MAE 0.0728 for deltoid; SAR: MSE 0.0366, MAE 0.1230). Results showed a decline in biceps MDF across sets, indicating localized fatigue, while the deltoid exhibited increased MDF, possibly reflecting compensatory or inefficient activation. SAR values decreased over time, suggesting fatigue-induced reorganization of muscle synergy and increased reliance on stabilizer muscles. These findings demonstrate the feasibility of using LSTM models with synchronized EMG and motion data to detect both localized fatigue and coordination changes in real-time. The proposed framework may support future applications in personalized training, fatigue monitoring, and ergonomic assessment.