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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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The future of virtual reality: Prospect and problems 9th

The future of virtual reality: Prospect and problems

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Lie, Andrew Alfonso Buntoro, Owen Tamashi Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Virtual Reality (VR) is a technology that allows users to interact with a simulated environment created by virtual reality capabilities through the intermediary of computers, simulator tools, and others, providing new experiences and can feel experiences that are not possible in the real world. This research aims to identify and analyze what challenges may occur such as technical, social, and ethical barriers in the development and application of VR technology. Using a mixed methods approach, we collected quantitative data from Kaggle's dataset and conducted in-depth interviews with active VR users. Our findings show that although VR has significant potential in various fields, such as education, healthcare, and entertainment, challenges such as motion sickness, high costs, and limited and engaging content hinder the widespread adoption of VR technology. The study also highlights the importance of improving technologies that need to be implemented such as visual capabilities, motion responsiveness, and audio quality to enhance the user experience when using VR. By overcoming these barriers, VR can maximize its positive impact and become a revolutionary tool in various sectors. © 2024 The Authors.

关键词： Virtual environments

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A fine-tuned vision transformer-based on limited dataset for facial expression recognition 9th

A fine-tuned vision transformer-based on limited dataset for...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Febrian, Rio Huang, Ronald Richie Setiono, Nicholas Ramdhan, Dimas Chowanda, Andry Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

With the increase of interest in Facial Expression Recognition (FER) in the past few decades. Several challenges surfaced with the invention of many different FER models which are often based on Convolution Neural Network (CNN) architectures. Recently, an attention-based transformer model has been presented to address FER. One of the major issues with Transformers is the need for a large data quantity for training. Therefore, in this paper, we propose to learn how to fine-tune a vision transformer-based (ViT) model using a limited dataset. We will be using the JAFFE Dataset, which consists of only 213 images containing seven different emotions. The proposed method is evaluated using several fine-tuning methods, such as adding dropout, data augmentation, and layer freezing. We compared the models implemented with 5% dropout regularization, augmented dataset (up to 5000 images), and freezing the initial model's layers, fine-tuning around a fourth of the last layers. The best model was achieved by fine-tuning ViT L-16 with 96.06% accuracy, trained with 5% dropout in the augmented dataset, and freezing the initial 21st layers. We also compared our model to the other previous work model and the results showed that our model reached the state-of-the art for the JAFFE dataset. © 2024 The Authors.

关键词： Convolutional neural networks

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Multimodal Information Extraction:A systematic Review of subtask, modal types and applications based on Deep Learning in Banking sector 5

Multimodal Information Extraction:A systematic Review of sub...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Srinivsan, Samundiswary Krishna, R. Sathya Bama Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India SIES Graduate School of Technology Navi Mumbai India

ISBN: (纸本)9798350361155

Deep Learning has been widely applied in all application domains namely Healthcare, Entertainment, Banking & finance, Agriculture, Aerospace & defence etc. Due to stupendous achievement of deep learning as a data processing technique has fascinated in all research areas. Handling of Deep learning in Banking and Financial sector is pervasive. Advancement of Information extraction especially because of different formats of information such as text, image, sound, video can be extracted simultaneously by the usage of deep learning techniques. This study reviews and analyses multimodal information extraction based on deep learning techniques especially on banking and financial sector. It aims to enhance the efficiency of document-intensive business tasks in the financial sector. Deep review analysis will be carried out to understand subtasks of Information extraction and techniques involved in Banking domain. Finally, how multimodal information extraction using deep learning enhances the efficiency than unimodal and its use cases in current AI based automation world will be highlighted. © 2024 IEEE.

关键词： Finance

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Global-Local Consistency for Cost-Effective Anomaly Localization with Point Annotations 16

Global-Local Consistency for Cost-Effective Anomaly Localiza...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Nakanishi, Keiichi Katafuchi, Ryoya Tokunaga, Terumasa Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Iizuka Japan

ISBN: (纸本)9798350377903

Creating pixel-level ground-truth (GT) masks is quite costly for deep learning-based image segmentation. Specialists in areas such as anomaly detection and medical diagnostics face difficulties in producing many GT masks due to limited resources. To reduce this burden, we propose a cost-effective image segmentation framework with point annotations (CoSPA) that performs image segmentation with only point annotations. Point annotations refer to the partial and sparse labeled coordinates in an image. The key idea is to ensure consistency between the predictions for the same coordinates from the two different networks of CoSPA. This new consistency enables the CoSPA to improve segmentation performance and extend to semi-supervised learning. For the MVTec AD dataset, we verified the cost-effectiveness of CoSPA through an anomaly detection task. We demonstrated that the point annotating costs were reduced by 80% compared to creating GT masks. Subsequently, the CoSPA realized by 87% of the mean Intersection over Union (mIoU) achieved using the fully supervised method, DeepLabV3+. Moreover, the mIoU of CoSPA using only 30% of all point annotations defeated that of the unsupervised method, PaDiM. This study offers a new direction for economic anomaly localization. © 2024 IEEE.

关键词： Image segmentation

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Dynamic Talking-Head Generation with Speech Emotion Recognition and Intensity Detection 13

Dynamic Talking-Head Generation with Speech Emotion Recognit...

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13th International Conference on Electronics, Communications and Networks, CECNet 2023

作者： Fu, Chen Yu, Yang Zhang, Zhiqiang Weng, Wei Zhou, Jinjia Graduate School of Science and Engineering Hosei University Japan School of Computer Science and Technology Southwest University of Science and Technology China Institute of Liberal Arts and Science Kanazawa University Japan

ISBN: (纸本)9781643684802

In recent years, speech emotion recognition (SER) and emotive talking head generation have drawn significant attention. However, existing methods either focus predominantly on emotion recognition without considering its intensity or produce emotionally consistent talking heads, neglecting the rich expressiveness of human interactions. Addressing these limitations, our study aims to bridge these two areas, proposing an innovative talking-head generation approach that further enhances the realism of the generated video. Within the SER domain, we develop a dual-level model that not only improves the accuracy of identifying emotions in speech data but also assesses their intensity. This dual-capacity model significantly enhances the depth and subtlety of emotional recognition, achieving an industry-leading accuracy rate of 73.5%. On the talking head front, this work generates emotionally expressive talking heads that reflect both emotional state and intensity. Beyond this, the model also simulates human facial dynamics like blinking, thus offering a more comprehensive and realistic representation of human speech. These advances effectively address the shortcomings of existing methods and provide a new avenue for the accurate portrayal of emotional intensity in synthesized talking heads. Extensive experimental results demonstrate the effectiveness and superiority of our approach over existing methodologies in both SER and talking head generation. © 2024 The authors and IOS Press.

关键词： Emotion Recognition

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Fast and flexible stack‐based inverse tone mapping

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1444-1454页

作者： Ning Zhang Yuyao Ye Yang Zhao Xufeng Li Ronggang Wang School of Electronic and Computer Engineering Peking University Shenzhen Graduate SchoolShenzhenChina School of Computer and Information Hefei University of TechnologyHefeiChina Department of Computer Science(CS) College of Engineering(EG)City University of Hong KongHong KongChina

Inverse tone mapping technique is widely used to restore the lost textures from a single low dynamic range ***,many stack‐based deep inverse tone mapping networks have achieved impressive results by estimating a set of multi‐exposure images from a single low dynamic range ***,there are still some *** the one hand,these methods usually set a fixed length for the estimated multi‐exposure stack,which may introduce computational redundancy or cause inaccurate *** the other hand,they neglect that the difficulties of estimating each exposure value are different and use the identical model to increase or decrease exposure *** solve these problems,the authors design an exposure decision network to adaptively determine the number of times the exposure of low dynamic range input should be increased or ***,the authors decouple the increasing/decreasing process into two sub‐modules,exposure adjustment and optional detail recovery,based on the characteristics of different variations of exposure *** these improvements,this method can fast and flexibly estimate the multi‐exposure stack from a single low dynamic range *** on several datasets demonstrate the advantages of the proposed method compared to state‐of‐the‐art inverse tone mapping methods.

关键词： 2‐D image enhancement image processing

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The advancement in multi-label and label distribution learning

The advancement in multi-label and label distribution learni...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Liu, Suping Samonte, Mary Jane C. Mapua University School of Graduate Studies and Information Technology Manila1002 Philippines Guangdong University of Science and Technology School of Computer Science Dongguan523083 China

ISBN: (纸本)9798331527624

Multi label learning and label distribution learning as emerging machine learning paradigms have developed rapidly in recent years. This article aims to review the research progress in this field, focusing on the algorithms and applications of multi label learning and label distribution learning. Firstly, the basic concepts and related backgrounds of multi label learning and label distribution learning were introduced, and their differences and connections with traditional single label learning were explained. Secondly, the research progress on multi label learning, multi label classification, multi label feature selection, and label distribution learning were introduced separately. Finally, this article summarizes and looks forward to the future development directions of multi label learning and label distribution learning. © 2024 IEEE.

关键词： Adversarial machine learning

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A Study on DDoS Attacks Detection on IoT Devices Using Machine Learning for Microcontrollers

A Study on DDoS Attacks Detection on IoT Devices Using Machi...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Mwaura, Jedidah Araki, Shunsuke Kakizaki, Ken'Ichi Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering FuKuoka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350324136

In recent years, there has been a proliferation of Internet of Things (IoT) devices, and so has been the attacks on them. In this paper we will propose a methodology to detect Distributed Denial of Service (DDoS) attacks on IoT devices using Machine Learning for Microcontrollers. We will discuss a model which we made for Arduino Nano 33 BLE Sense using Machine Learning for Microcontrollers. Additionally, we will discuss results of our proposal in detecting DDoS attacks on IoT devices. Lastly, we will describe the feasibility of our model on IoT devices. © 2024 IEEE.

关键词： DDoS IoT Machine Learning TinyML

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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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