A Systematic Review of Surface Electromyography in Sarcopenia: Muscles Involved, Signal Processing Techniques,...

Area of Science:

• Gerontology
• Biomedical Engineering
• Neuromuscular Physiology

Background:

• Sarcopenia, an age-related decline in muscle mass and function, affects 1-29% of older adults.
• Current diagnostic methods for sarcopenia are costly, require specialized expertise, and lack portability.
• Surface electromyography (sEMG) offers a potential non-invasive alternative for muscle assessment.

Purpose of the Study:

• To systematically review the literature on the application of sEMG for sarcopenia diagnosis and monitoring.
• To identify muscles involved, signal processing techniques, AI models, and statistical analyses used in sEMG-based sarcopenia assessment.
• To evaluate the potential of sEMG as a tool for early sarcopenia detection.

Main Methods:

• Systematic literature review adhering to PRISMA guidelines.
• Searches conducted in PubMed, Scopus, and IEEE databases (2014-2024).
• Inclusion of original studies using sEMG for sarcopenia assessment in older populations.

Main Results:

• 18 studies were included in the final analysis from an initial 145 articles.
• A growing trend in the use of sEMG for sarcopenia assessment was observed.
• Significant methodological heterogeneity exists across studies, limiting direct comparisons.

Conclusions:

• sEMG is a promising technology for the early detection and monitoring of sarcopenia.
• Standardized guidelines for sEMG data collection and interpretation are crucial for clinical application.
• Further research is needed for clinical validation and to ensure reproducibility of results.