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

Konki Sravan Kumar; Daehyun Lee; Ankhzaya Jamsrandoj; Necla Nisa Soylu; Dawoon Jung; Jinwook Kim; Kyung Ryoul Mun; Konki Sravan Kumar; Daehyun Lee; Ankhzaya Jamsrandoj; Necla Nisa Soylu; Dawoon Jung; Jinwook Kim; Kyung Ryoul Mun

doi:10.3934/mbe.2024129

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 2901-2921. doi: 10.3934/mbe.2024129

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

1.
Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul, Korea
2.
KHU-KIST Department of Converging Science and Technology, Graduate School, Kyung Hee University, Seoul, Korea
3.
Department of Human Computer Interface and Robotics Engineering, University of Science and Technology, Daejeon, Korea
4.
Department of Computer Science, Ozyegin University, Istanbul, Turkey

Received: 29 November 2023 Revised: 28 December 2023 Accepted: 03 January 2024 Published: 25 January 2024

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being.
- sEMG,
- empirical mode decomposition,
- feature selection,
- sarcopenia risk,
- machine learning
Citation: Konki Sravan Kumar, Daehyun Lee, Ankhzaya Jamsrandoj, Necla Nisa Soylu, Dawoon Jung, Jinwook Kim, Kyung Ryoul Mun. sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 2901-2921. doi: 10.3934/mbe.2024129

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Abstract

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being.

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