Predicting Muscle Thickness Deformation from Muscle Activation Patterns: A Dual-Attention Framework

TL;DR

This paper presents a deep-learning dual-attention framework that predicts muscle thickness deformation from sEMG signals, enabling portable, real-time muscle health monitoring without ultrasound.

Contribution

It introduces a novel dual-attention deep-learning model that accurately predicts muscle deformation from sEMG signals, bypassing ultrasound measurement limitations.

Findings

Achieved an average prediction precision of 0.923±0.900mm.

Demonstrated potential for real-time portable muscle health monitoring.

Applicable in clinical diagnostics, sports science, and rehabilitation.

Abstract

Understanding the relationship between muscle activation and thickness deformation is critical for diagnosing muscle-related diseases and monitoring muscle health. Although ultrasound technique can measure muscle thickness change during muscle movement, its application in portable devices is limited by wiring and data collection challenges. Surface electromyography (sEMG), on the other hand, records muscle bioelectrical signals as the muscle activation. This paper introduced a deep-learning approach to leverage sEMG signals for muscle thickness deformation prediction, eliminating the need for ultrasound measurement. Using a dual-attention framework combining self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data. Experimental results with six healthy subjects showed that the approach could accurately predict muscle excursion with an average precision of 0.923$\pm$0.900mm, which shows that this method can facilitate real-time portable muscle health monitoring, showing potential for applications in clinical diagnostics, sports science, and rehabilitation.