Deciphering Muscular Dynamics: A Dual-Attention Framework for Predicting Muscle Contraction From Activation Patterns

Abstract

Quantitatively deciphering the relationship between muscle activation and thickness deformation is essential for diagnosing muscle-related diseases and monitoring muscle health (e.g., Facioscapulohumeral Dystrophy). Despite the potential of ultrasound (US) imaging and sensing to measure changes in muscle thickness during movements, it remains challenging to make a fully portable device, considering the wiring and data collection. On the other hand, surface electromyography (sEMG) can record muscle bioelectrical signals and measure muscle activations, offering a unique perspective that correlates with underlying changes in muscle thickness. This paper introduces a deep-learning-based approach that used sEMG signals to infer muscle deformation. Using a hierarchical combination of self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data, eliminating the dependency on applying ultrasound imaging techniques. The experimental results on six healthy subjects indicated that our approach could accurately predict muscle excursion with an average precision of 0.923 ± 0.900 mm, showing benefits in measuring muscle deformation only with a sEMG device. This technique facilitates real-time portable muscle health monitoring by sEMG to provide bioelectrical signals and biomechanical information. It indicates the great potential of using this technique in clinical diagnostics, sports science, and rehabilitation.