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Wearable Hybrid Strain-Myoelectric Sensing System for Machine-Learning-Assisted Sarcopenia Screening.

Ke Wang^1,2, Guanbo Min^1,2, Tingyu Wang^1,2

¹Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing China.

|April 23, 2026

Summary

This summary is machine-generated.

A new wearable system for sarcopenia screening uses advanced sensors and AI to accurately detect muscle loss in older adults. This low-cost, portable device offers a radiation-free solution for early diagnosis.

Keywords:

convolutional neural network–long short‐term memory interpretability analysis muscle fiber sarcopenia wearable electronics

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Area of Science:

Biomedical Engineering
Gerontology
Neuromuscular Physiology

Background:

Sarcopenia screening is crucial due to global population aging.
Current methods are costly, equipment-dependent, and assess mass/strength separately.

Purpose of the Study:

To develop and validate a Wearable Sarcopenia Assessment System (WSAS) for early sarcopenia detection.
To integrate hybrid sensing with deep learning for synchronous signal capture.

Main Methods:

Developed a WSAS with integrated surface electromyography (sEMG) and piezoelectric strain sensors.
Employed a CNN-LSTM deep learning framework trained on nine features.
Validated the system in 75 elderly participants.

Main Results:

Achieved 99.85% screening accuracy and an AUC of 0.97.
WSAS captured neuromuscular alterations, including fiber transition and junction remodeling.
Demonstrated high signal-to-noise ratio (34.32 dB).

Conclusions:

WSAS shows potential as a portable, low-cost, radiation-free tool for early sarcopenia screening.
The system effectively identifies neuromuscular changes associated with sarcopenia.
Highlights the utility of hybrid sensing and deep learning in geriatric diagnostics.