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

Mathematical Biosciences and Engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

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. © 2024 American Institute of Mathematical Sciences. All rights reserved.

关键词： Empirical mode decomposition

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3D Ball Trajectory Reconstruction of a Ballistic Shot from a Monocular Basketball Video 49

3D Ball Trajectory Reconstruction of a Ballistic Shot from a...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chao, Vanyi Jamsrandorj, Ankhzaya Oo, Yin May Mun, Kyung-Ryoul Kim, Jinwook University of Science and Technology Ai Robotics Daejeon34113 Korea Republic of University of Science and Technology Human Computer Interface and Robotics Daejeon34113 Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Seoul02792 Korea Republic of

ISBN: (纸本)9798350331820

3D ball location in sports can be advantageous and has many applications such as automatic scoring system, ball trajectory highlight and offside detection system for football game. Currently, this task can be solved through the use of costly multi-view approaches or the legacy physics-based methods that require complicated setups and human intervention. In this paper, we propose a method to reconstruct the 3D ball trajectory of the basketball projectile shot by using only a single view. The method relies on accurate camera calibration and 2D ball detection of sequence images. Extra information in the 3D world also plays an important role in improving the 3D reconstruction. The experimental result illustrates the accurately reconstructed 3D trajectory when enough information is given. © 2023 IEEE.

关键词： Ballistics

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A Residual Attention-based EfficientNet Homography Estimation Model for Sports Field Registration 49

A Residual Attention-based EfficientNet Homography Estimatio...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Oo, Yin May Jamsrandorj, Ankhzaya Chao, Vanyi Mun, Kyung-Ryoul Kim, Jinwook Ai Robotics University of Science and Technology Daejeon34113 Korea Republic of Human Computer Interface and Robotics University of Science and Technology Daejeon34113 Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Seoul02792 Korea Republic of

ISBN: (纸本)9798350331820

Accurate sports field registration plays a crucial role in computer vision for effective team sports analysis, as it provides the ability to track and recognize the positions, movements, and interactions of players from image space to real-world field space. In this paper, we propose a two-stage deep learning framework for sports field registration. In the first-stage network, we utilize a Residual EfficientNet-Attention U-Net architecture to estimate the initial homography matrix using predefined keypoints to register sports fields. Subsequently, our second-stage network refines the initial homography for improved accuracy. We evaluate our proposed method on the public sports datasets to show its outperform results against the state-of-the-art methods in some evaluation metrics. © 2023 IEEE.

关键词： Sports

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Ball sition ature bedded Group Activity Recognition del for Team Sport Games 4

Ball sition ature bedded Group Activity Recognition del for ...

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4th International Conference on Machine Learning and Intelligent Systems, MLIS 2022

作者： Jamsrandorj, Ankhzaya Chao, Vanyi Oo, Yin May Mun, Kyung-Ryoul Kim, Jinwook Department of Human Computer Interface & Robotics Engineering University of Science & Technology Korea Republic of Department of Ai Robotics University of Science & Technology Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Korea Republic of

ISBN: (纸本)9781643683560

Most group activity recognition models focus mainly on spatio-temporal features from the players in sports games. Often they do not pay enough attention to the game object, which heavily affects not only individual action but also a group activity. We propose a new group activity recognition model for sports games that incorporates players' motion information and game object positional information. The proposed method uses a transformer encoder for temporal feature extraction and a 'simple' conventional convolutional neural network for extracting spatial features and fusing them with the relative ball position-embedded features. The experimental results show that our model achieved comparable results to state-of-the-art methods on the Volleyball dataset by using only one transformer encoder block and the ball position. © 2022 The authors and IOS Press.

关键词： Sports

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3D Ball Trajectory Reconstruction of a Ballistic Shot from a Monocular Basketball Video

3D Ball Trajectory Reconstruction of a Ballistic Shot from a...

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Annual Conference of Industrial Electronics Society

作者： Vanyi Chao Ankhzaya Jamsrandorj Yin May Oo Kyung-Ryoul Mun Jinwook Kim AI Robotics University of Science and Technology Daejeon Republic of Korea Human Computer Interface and Robotics University of Science and Technology Daejeon Republic of Korea Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Republic of Korea

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A Residual Attention-based EfficientNet Homography Estimation Model for Sports Field Registration

A Residual Attention-based EfficientNet Homography Estimatio...

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Annual Conference of Industrial Electronics Society

作者： Yin May Oo Ankhzaya Jamsrandorj Vanyi Chao Kyung-Ryoul Mun Jinwook Kim AI Robotics University of Science and Technology Daejeon Republic of Korea Human Computer Interface and Robotics University of Science and Technology Daejeon Republic of Korea Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Republic of Korea

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Deep Learning Networks for View-Independent Gait Events Detection

SSRN

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SSRN 2022年

作者： Jamsrandorj, Ankhzaya Jung, Dawoon Kumar, Konki Sravan Arshad, Muhammad Zeeshan Mun, Kyung-Ryoul Kim, Jinwook Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of Department of Human Computer Interface & Robotics Engineering University of Science & Technology Daejeon Korea Republic of

Accurate gait detection is crucial in utilizing the ample health information embedded in it. As an alternative of the laborious and demanding sensor-based detection, vision-based approaches ***, its complicate feature engineering process and heavy reliance on the lateral views serve as challenges. This study aimed to propose a view-independent vision-based gait event detection using deep learning networks that requires no pre-processing. A total of 22 participants performed seven different walking and running-related actions and the sequential video frames acquired from their actions were used as the input of the deep learning networks to produce the probability of gait events as outputs. The Transformer network and ResNet18 trained with sequential video frames achieved an F1-score of 0.956 or higher for walking straight and walking around. The detection performance on the frontal, lateral, and backside views did not differ much. The findings enhance applicability of vision-based approach and contribute to increasing its utility in health monitoring. © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Image-based Gait Spatiotemporal Parameters Estimation using a Single Camera and CNN-Transformer Hybrid Network

Image-based Gait Spatiotemporal Parameters Estimation using ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ankhzaya Jamsrandorj Quynh Hoang Ngan Nguyen Dawoon Jung Min Seok Baek Kyung-Ryoul Mun Jinwook Kim Department of Human Computer Interface & Robotics Engineering KIST School University of Science & Technology Seoul Republic of Korea Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Republic of Korea Department of AI Robotics KIST School University of Science & Technology Seoul Republic of Korea Department of Neurology Wonju Severance Christian Hospital Yonsei University Wonju College of Medicine Wonju Republic of Korea

Vision-based gait analysis can play an important role in the remote and continuous monitoring of the elderly’s health conditions. However, most vision-based approaches compute gait spatiotemporal parameters using human pose information and provide average parameters. This study aimed to propose a straightforward method for stride-by-stride gait spatiotemporal parameters estimation. A total of 160 elderly individuals participated in this study. Data were gathered with a GAITRite system and a mobile camera simultaneously. Three deep learning networks were trained with a few RGB frames as input and a continuous 1D signal containing both spatial and temporal gait parameters as output. The trained networks estimated the stride lengths with correlations of 0.938 and more and detected gait events with F 1 -scores of 0.914 and *** relevance— The proposed method showed excellent agreements with the GAITRite system in analyzing spatiotemporal gait parameters. Our approach can be applied to monitor the elderly’s health conditions based on their gait parameters for early diagnosis of diseases, proper treatment, and timely intervention.

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Walking-in-Place Characteristics-Based Geriatric Assessment Using Deep Convolutional Neural Networks 42

Walking-in-Place Characteristics-Based Geriatric Assessment ...

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42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Jung, Dawoon Nguyen, Mau Dung Park, Mina Kim, Miji Won, Chang Won Kim, Jinwook Mun, Kyung-Ryoul Center for Imaging Media Research Kist Seoul Korea Republic of University of Science and Technology Department of Human Computer Interface Robotics Engineering Daejeon Korea Republic of

ISBN: (纸本)9781728119908

The world population is aging, and this phenomenon is expected to continue for the next decades. This study aimed to propose a simple and reliable method that can be used for daily in-home monitoring of frailty and cognitive dysfunction in the elderly based on their walking-in-place characteristics. Fifty-four community-dwelling elderly people aged 65 years or older participated in this study. The participants were categorized into the robust and the non-robust groups according to the FRAIL scale. The mini-mental state examination was used to classify the cognitive impairment and the non-cognitive impairment groups. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the foot, shank, thigh, and posterior pelvis while each participant was walking in place for 20 seconds. The walking-in-place spectrograms were acquired by applying time-frequency analysis to the lower body movement signals measured in one stride. Four-fold cross-validation was applied to 80% of the total samples and the remaining 20% were used as test data. The deep convolutional neural network-based classifiers trained with the walking-inplace spectrograms enabled to categorize the robust and the non-robust groups with 94.63% accuracy and classify the cognitive impairment and the non-cognitive impairment groups with 97.59% accuracy. This study suggests that the walking-in-place spectrograms, which can be obtained without spacious experimental space, cumbersome equipment, and laborious processes, are effective indicators of frailty and cognitive dysfunction in the elderly. © 2020 IEEE.

关键词： Convolutional neural networks

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Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor

arXiv

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arXiv 2022年

作者： Arshad, Muhammad Zeeshan Jamsrandorj, Ankhzaya Kim, Jinwook Mun, Kyung-Ryoul The Center for Artificial Intelligence KIST Seoul Korea Republic of The Department of Human Computer Interface & Robotics Engineering University of Science & Technology Daejon Korea Republic of KHU-KIST Department of Converging Science and Technology KHU Seoul Korea Republic of

Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to explore a way of improving the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data was gathered from elderly subjects equipped with three IMU sensors while they walked. The input was taken only from the waist sensor was used to train 16 deep-learning models including CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention mechanism. The groundtruth was extracted from foot IMU sensors. Fairly high accuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at the tolerance window of ±6TS (±6ms) and ±1TS (±1ms) respectively. Advancing from the previous studies exploring gait event detection, the model showed a great improvement in terms of its prediction error having an MAE of 6.239ms and 5.24ms for HS and TO events respectively at the tolerance window of ±1TS. The results showed that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor. The study can contribute to reducing the burden of gait detection and increase its applicability in future wearable devices that can be used for remote health monitoring (RHM) or diagnosis based thereon. Copyright © 2022, The Authors. All rights reserved.

关键词： Gait analysis

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