Screening of sarcopenia in long-term care facilities using wireless surface electromyography and deep transfer learning: a proof-of-concept study

Résumé fourni par la source

Abstract Background Sarcopenia is prevalent in long-term care facilities (LTCFs) and is associated with falls, functional decline, and mortality. Aims This proof-of-concept study, within the I-COUNT pilot randomized controlled trial, evaluated the feasibility and diagnostic performance of a non-invasive sarcopenia screening approach based on wearable surface electromyography (sEMG) combined with artificial intelligence. Results Eighteen older adults residing in two Italian LTCFs (6 sarcopenic, 12 controls; 33.3% men, mean age 87 ± 5.4 years) underwent sEMG during three standardised motor tasks involving lower-limb muscles. Surface-EMG signals were transformed into time-frequency scalograms (Continuous Wavelet Transform) and classified as sarcopenic versus non-sarcopenic using deep transfer learning. The classification framework achieved 91% accuracy using Leave-One-Out Cross-Validation, with sensitivity of 84%, specificity of 93%, precision of 92%, and an F1-score of 91. Conclusions These findings support wearable sEMG as a scalable tool for screening of sarcopenia in LTCFs and provide preliminary evidence for further validation in larger populations. Trial registration number ClinicalTrials.gov ID NCT06820710.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.