Abstract
Sarcopenia, affecting between 1-29% of the older population, is characterized by an age-related loss of skeletal muscle mass and function. Reduced muscle strength, either in terms of quantity or quality, and poor physical performance are among the criteria used to diagnose it. The current gold standard methods to evaluate sarcopenia are limited in terms of their cost, required expertise, and portability. A possible alternative for sarcopenia detection and monitoring is surface electromyography, which offers comprehensive information on muscle function, but a systematic synthesis of the existing literature is lacking. This systematic review aims to evaluate the application of sEMG in diagnosing and monitoring sarcopenia, focusing on the muscles involved, signal processing techniques, artificial intelligence models, and statistical analysis methods used for data interpretation. Following PRISMA guidelines, a search was performed in PubMed, Scopus, and IEEE databases from 2014 up to December 2024. Original studies using sEMG for sarcopenia diagnosis or assessment in older populations were included. After removing duplicates, 145 articles were identified, of which 18 were included in the final analysis. The findings indicate a growing interest in the adoption of sEMG in sarcopenia assessment. However, methodological heterogeneity among studies limits comparability. sEMG represents a promising option for the early detection of sarcopenia, but standardized guidelines for data collection and interpretation are needed. Future studies should focus on clinical validation and results reproducibility.