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Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms.

Konki Sravan Kumar¹, Daehyun Lee^1,2, Ankhzaya Jamsrandoj³

¹Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul, Korea.

Mathematical Biosciences and Engineering : MBE

|March 8, 2024

Summary

This study introduces a new method using surface electromyography (sEMG) and machine learning to detect sarcopenia risk early. The technique accurately identifies muscle health risks during physical activities, aiding preventive strategies.

Keywords:

empirical mode decomposition feature selection machine learning sEMG sarcopenia risk

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Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

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Published on: November 8, 2024

Area of Science:

Biomedical Engineering
Kinesiology
Gerontology

Background:

Sarcopenia risk detection at younger ages is vital for preventive strategies and healthy aging.
Current methods may lack precision in early identification during dynamic activities.

Purpose of the Study:

To develop and validate a novel technique for early sarcopenia risk detection.
To combine surface electromyography (sEMG) signals with empirical mode decomposition (EMD) and machine learning (ML).

Main Methods:

sEMG data collected during walking and squatting activities from healthy and at-risk individuals.
EMD applied to extract intrinsic mode functions (IMFs) from normalized sEMG signals.
Minimum redundancy maximum relevance (mRMR) for feature selection, followed by ML classification with leave-one-subject-out cross-validation.

Main Results:

High accuracy rates achieved: 0.88 (normal walking), 0.89 (fast walking), 0.81 (standard squat), 0.80 (wide squat).
The sEMG-EMD-ML system demonstrated reliable identification of sarcopenia risk.
Effective classification of individuals at risk for sarcopenia during various physical tasks.

Conclusions:

The proposed sEMG-EMD-ML system offers a reliable and accurate approach for early sarcopenia risk detection.
This technology has practical applications in muscle function assessment and health monitoring.
Early identification supports interventions for improved muscle quality and well-being throughout life.