A wearable approach for Sarcopenia diagnosis using stimulated muscle contraction signal.

Abstract

Sarcopenia is a rapidly rising health concern in the fast-aging countries, but its demanding diagnostic process is a hurdle for making timely responses and devising active strategies. To address this, our study developed and evaluated a novel sarcopenia diagnosis system using Stimulated Muscle Contraction Signals (SMCS), aiming to facilitate rapid and accessible diagnosis in community settings. We recruited 199 adults from Wonju Severance Christian Hospital between July 2022 and October 2023. SMCS data were collected using surface electromyography sensors with the wearable device exoPill. Their skeletal muscle mass index, handgrip strength, and gait speed were also measured as the reference. Binary classification models were trained to classify each criterion for diagnosing sarcopenia based on the AWGS cutoffs. The binary classification models achieved high discriminative abilities with an AUC score near 0.9 in each criterion. When combining these criteria evaluations, the proposed sarcopenia diagnosis system performance achieved an accuracy of 89.4% in males and 92.4% in females, sensitivities of 81.3% and 87.5%, and specificities of 91.0% and 93.8%, respectively. This system significantly enhances sarcopenia diagnostics by providing a quick, reliable, and non-invasive method, suitable for broad community use. The promising result indicates that SMCS contains extensive information about the neuromuscular system, which could be crucial for understanding and managing muscle health more effectively. The potential of SMCS in remote patient care and personal health management is significant, opening new avenues for non-invasive health monitoring and proactive management of sarcopenia and potentially other neuromuscular disorders.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-025-00461-z.