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The Use of Wearable Sensors for the Classification of Electromyographic Signal Patterns based on Changes in the Elbow Joint Angle

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Procedia computer science 2021年 185卷 338-344页

作者： SAM Matiur Rahman Md. Asraf Ali Md. Abdullah Al Mamun Department of Software Engineering Daffodil International University (DIU) Dhaka Bangladesh Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Electronics Division Atomic Energy Centre Dhaka Bangladesh

Upper limb elbow movement in terms of flexion-relaxation is a complex physical phenomenon in human daily life, particularly during synchronized activities, such as exercising, reaching, pointing, and manipulating. Improper elbow movement might cause musculoskeletal injury, fatigue, pain, or disorders in the upper limb muscles. This study aimed to identify subject-specific electromyographic (EMG) signal patterns based on changes in five elbow joint angles (at 0°, 30°, 60°, 90° and 120°) during maximum (100%) voluntary isometric (static) contraction. Surface electromyographic (sEMG) signals were recorded from the upper arm biceps brachii muscle using a three-channel wearable sensor. A non-parametric machine learning algorithm called k -nearest neighbors (k-NN) was used to build a model that can determine the EMG characteristics and thus discriminate between elbow joint angles. Fifteen time domain features were extracted from the recorded EMG signal and those were used for classification purposes. Two cross validation (CV) methods, namely, leave-one-out (LOO) and k-fold, were used to examine and validate the model. The results showed that k-fold CV showed higher mean classification accuracies (89.68%) than the LOO method (82.49%). Our classification-based results from sEMG signals acquired with five elbow joint angles could aid the development of more advanced rehabilitation assistive devices and further improve the neuromuscular activities of the upper arms. Additionally, this result showed that wearable technology has potential application for remotely monitoring and controlling motor rehabilitation exercises.

关键词： EMG machine learning elbow angle classification wearable sensor

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A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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Enhanced Intelligence Using Collective Data Augmentation for CNN Based Cataract Detection 1

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9th International Conference on Frontier Computing, FC 2019

作者： Imran, Azhar Li, Jianqiang Pei, Yan Mokbal, Fawaz Mahiuob Yang, Ji-Jiang Wang, Qing School of Software Engineering Beijing University of Technology Beijing China Computer Science Division University of Aizu Aizuwakamatsu Japan Research Institute of Information Technology Tsinghua University Beijing China

ISBN: (数字)9789811532504

ISBN: (纸本)9789811532498

Cataract is one of the prevailing cause of blindness in the industrial world that accounts for more than 50% of blindness. The early detection of cataract can protect serious threats of visual impairment. Most of the existing work is based on manual extraction of features, but this paper aims at automatic detection of a cataract into its different grades using deep convolutional neural network integrated with data augmentation techniques. The Gaussian-scale space theory and the general data augmentation settings are used to improve the dataset in terms of quality and quantity, which lead to overcome the issues of the unbalanced dataset. The training and testing of the proposed model are performed on both the original dataset and the augmented dataset. The model accuracy of the convolutional neural network with augmented dataset presented in this paper is 0.9691, which shows an optimal performance compared with the original dataset, and other methods. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Joint Theme and Event Based Rating Model for Identifying Relevant Influencers on Twitter: COVID-19 Case Study

SSRN

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SSRN 2022年

作者： Srour, Ali Ould-Slimane, Hakima Mourad, Azzam Harmanani, Haidar Jenainati, Cathia Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Software Engineering IT École de Technologie Supérieure MontrealQC Canada Department of English Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The continuous proliferation of social media platforms and the exponential increase in users’ engagement are impacting social behavior and leading to various challenges, including the detection and identification of key influencers. In fact the opinions of these influencers are at the core of decisionmaking strategies, and are leading trends on the virtual social media landscape. Moreover, influencers might play a crucial role when it comes to misinformation and conspiracy during sensitive, controversial and trending events. However, due to the dynamic and unrestricted nature of social media, and diversity of targeted topics and audiences, identifying and ranking key influencers that are impactful, credible, and knowledgeable about their specialist topic or event remains an evolving and open research paradigm. In this paper, we address the aforementioned problem by proposing a novel influence rating and ranking scheme to identify key and highly influential users for a certain event over Twitter using a mixed theme/event based approach while considering historical data and profile reputation. We further apply our approach to a global pandemic case study, the novel Coronavirus, and conduct performance analysis. The presented experimental results and theoretical analysis explore the relevance of our proposed scheme for identifying and ranking reputable and theme/event related influencers. © 2022, The Authors. All rights reserved.

关键词： Social networking (online)

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Edge AI-Based Dynamic Treatment Strategy Generation Model for Consumer Healthcare Technology

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IEEE Transactions on Consumer Electronics 2024年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Shabaz, Mohammad Ahanger, Tariq Ahamed Keshta, Ismail Saidova, Feruza Soni, Mukesh Karumuri, Srinivasa Rao Inje University Department of AI and Software Gimhae50834 Korea Republic of Al-Maarif University College Department of computer science Al Anbar31001 Iraq Intervention Treatment Institute M.D. Research HoustonTX United States Model Institute of Engineering and Technology J and K Jammu India CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia Tashkent State University of Economics Department of Foreign languages Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India VLSI-Microelectronics Lab Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram Guntur522302 India

Edge AI-based reinforcement learning is less reliant on mathematical models and relies on experience to help with the design and optimization of Consumer Technology models, making it ideal for learning dynamic treatment techniques. However, the current research still has the following problems: 1. The optimality of the learned strategy is considered without taking risks into account, leading to strategies with certain risks. 2. The issue of distribution shift is ignored, resulting in learned strategies that are completely different from those of doctors. 3. The patient's historical observational data and treatments are ignored, making it difficult to accurately assess the patient's state, which in turn leads to the inability to learn the optimal strategy. Based on this, we propose the DOSAC-DTR dynamic treatment strategy model, which integrates Dead-ends and offline monitoring into the Actor-Critic framework. First, we consider the risk of treatment actions recommended by the learned strategy and incorporate the concept of Dead-ends into the Actor-Critic framework. Second, to mitigate the distribution shift problem, we integrate physician monitoring into the Actor-Critic framework. This approach achieves better performance, resulting in lower estimated mortality rates and higher Jaccard index scores. © 1975-2011 IEEE.

关键词： Reinforcement learning

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Corrections to “Fuzzy Logic-Based Substitution-Box for Robust Medical Image Encryption in Telemedicine”

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IEEE Access 2025年 13卷 53717-53717页

作者： Abdul Razaq Louai A. Maghrabi Musheer Ahmad Farrah Aslam Wei Feng Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan Department of Software Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of Information Sciences Division of Science and Technology University of Education Lahore Pakistan School of Mathematics and Computer Science Panzhihua University Panzhihua China

Presents corrections to the paper, Corrections to “Fuzzy Logic-Based Substitution-Box for Robust Medical Image Encryption in Telemedicine”.

关键词： Telemedicine Encryption Biomedical imaging

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Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges

arXiv

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arXiv 2023年

作者： Liu, Qi Yang, Zhiyun Ji, Ru Zhang, Yonghong Bilal, Muhammad Liu, Xiaodong Vimal, S. Xu, Xiaolong The Wuxi University 333 Xishan Avenue Wuxi214105 China The School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China The Division of Computer and Electronic Systems Engineering Hankuk University of Foreign Studies Korea Republic of The School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom The Department of Artificial Intelligence & DS Ramco Institute of Technology Tamilnadu India

Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this paper, recent relevant scientific investigation and practical efforts using Deep Learning (DL) models for weather radar data analysis and pattern recognition have been reviewed;particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization. In addition to recent achievements, the latest advancements and existing challenges are also presented and discussed in this paper, trying to lead to reasonable potentials and trends in this highly-concerned *** Codes I.2, I.4, I.5 © 2023, CC BY-NC-SA.

关键词： Extrapolation

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FENet: A frequency extraction network for obstructive sleep apnea detection

arXiv

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arXiv 2021年

作者： Ye, Guanhua Yin, Hongzhi Chen, Tong Chen, Hongxu Cui, Lizhen Zhang, Xiangliang School of Information Technology & Electric Engineering The University of Queensland Australia School of Computer Science University of Technology Sydney Australia School of Software Shandong University China Division of Computer Electrical and Mathematical Sciences & Engineering King Abdullah University of Science and Technology Saudi Arabia

Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i.e., the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy. In this paper, we advance RR-interval based OSA detection by considering its real-world practicality from energy perspectives. As photoplethysmogram (PPG) pulse sensors are commonly equipped on smart wrist-worn wearable devices (e.g., smart watches and wristbands), the energy efficiency of the detection model is crucial to fully support an overnight observation on patients. This creates challenges as the PPG sensors are unable to keep collecting continuous signals due to the limited battery capacity on smart wrist-worn devices. Therefore, we propose a novel Frequency Extraction Network (FENet), which can extract features from different frequency bands of the input RR-interval signals and generate continuous detection results with downsampled, discontinuous RR-interval signals. With the help of the one-to-multiple structure, FENet requires only one-third of the operation time of the PPG sensor, thus sharply cutting down the energy consumption and enabling overnight diagnosis. Experimental results on real OSA datasets reveal the state-of-the-art performance of FENet. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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Towards Mutual Trust-Based Matching For Federated Learning Client Selection

Towards Mutual Trust-Based Matching For Federated Learning C...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Osama Wehbi Omar Abdel Wahab Azzam Mourad Hadi Otrok Hoda Alkhzaimi Mohsen Guizani Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi UAE Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon Department of Computer and Software Engineering Polytechnique Montréal Montreal Quebec Canada Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Division of Engineering New York University Abu Dhabi UAE

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients’ targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients.

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A Guide to Image and Video based Small Object Detection using Deep Learning: Case Study of Maritime Surveillance

arXiv

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arXiv 2022年

作者： Rekavandi, Aref Miri Xu, Lian Boussaid, Farid Seghouane, Abd-Krim Hoefs, Stephen Bennamoun, Mohammed The Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia The Department of Electrical Electronics and Computer Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia The School of Mathematics and Statistics The University of Melbourne Melbourne Australia submarine optronics undersea combat systems and undersea command and control maritime division Defence Science and Technology Group Australia

Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided. © 2022, CC BY-NC-SA.

关键词： Object recognition

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