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BioPoint: Enhancing Human-computer Interaction through Single-Site, Multi-Sensor Gesture Recognition 46

BioPoint: Enhancing Human-Computer Interaction through Singl...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Isabel, Xavier Campbell, Evan Olivier, Loïc Gagné, Gabriel Scheme, Erik Côté-Allard, Ulysse Gagnon-Turcotte, Gabriel Laval University Dept. of Electrical and Computer Engineering QC Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B 5A3 Canada University of Oslo Dept. of Technology Systems Oslo Norway

ISBN: (纸本)9798350371499

Advancements in human-computer interaction (HCI) and machine learning are seen as key avenues to help individuals living with upper limb disabilities in accomplishing their activities of daily living. Multi-channel myoelectric systems are a promising approach for HCI due to their intuitive and accurate capture of user intent through muscle activity. However, such systems are still bulky compared to widely accepted smartwatches-like devices and as such pose a challenge for seamless integration into daily life. Consequently, this work presents the first single-site EMG-IMU-PPG system for multi-gesture recognition from the wrist. Overall, when evaluated on eight able-bodied participant, the system achieves an average accuracy of 65.28%±10.45% on 17 finger and wrist gestures and 90.98%±5.65% on eight hand gestures. This study also introduces "Myo Hero", an online environment inspired by "Guitar Hero."Myo Hero provides a dynamic method for testing multi-gesture recognition systems, improving the evaluation process's appeal. Online evaluation of the multimodal system substantially outperformed random play, signifying EMG-IMU-PPG's potential for human-computer interaction. © 2024 IEEE.

关键词： Electromyography

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An extended reality platform for inclusion of adults on the autism spectrum: A position paper 15

An extended reality platform for inclusion of adults on the ...

引用

15th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Westin, Thomas Rahmani, Rahim Palosaari-Eladhari, Mirjam Romero, Mario Stockholm University Dept. of Computer and Systems Sciences Postbox 7003 Kista164 07 Sweden KTH Royal Institute of Technology Stockholm100 44 Sweden

Extended reality (XR) enables both new opportunities but also introduces new barriers for inclusion in society. Furthermore, XR is less researched than web, desktop and mobile applications. This position paper presents the concept of an XR platform for inclusion, with the purpose to make people on the autism spectrum and with other disabilities, more independent of help from others in everyday life situations. Based on previous research, our position is that, through current and future XR technologies combined with civic and artificial intelligence, it is possible to create individually personalised support for this purpose, grounded in practice to ensure validation. © 2024 Elsevier B.V.. All rights reserved.

关键词： Augmented reality

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SPARROW: Smart Precision Agriculture Robot for Ridding of Weeds 5

SPARROW: Smart Precision Agriculture Robot for Ridding of We...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Arachchige, Gayantha Godakanda Bandara, Anuththara Wellalage, Sasini Pandithage, Dinithi Hansika, Mahaadikara M.D.J.T. de Silva, Rajitha Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka Melbourne Australia Lincoln Agri-Robotics University of Lincoln United Kingdom

ISBN: (纸本)9798350361155

The advancements in precision agriculture are vital to support the increasing demand for global food supply. Precision spot spraying is a major step towards reducing chemical usage for pest and weed control in agriculture. A novel spot spraying algorithm that autonomously detects weeds and performs trajectory planning for the sprayer nozzle has been proposed. Furthermore, this research introduces a vision-based autonomous navigation system that operates through the detected crop row, effectively synchronizing with an autonomous spraying algorithm. This proposed system is characterized by its cost effectiveness that enable the autonomous spraying of herbicides onto detected weeds. © 2024 IEEE.

关键词： computer vision

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EHR based Prediction and Diet Recommender For Diabetes using Blockchain 3

EHR based Prediction and Diet Recommender For Diabetes using...

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3rd International Conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2024

作者： Poonguzhali, N. Amarabalan, N. Jawaharlal Nehru University School of Computer and Systems Sciences New Delhi India Manakula Vinayagar Institute of Technology Dept. of Electrical and Electronics Engineering Puducherry India

ISBN: (纸本)9798350391565

Diabetes is an illness which is triggered due to high blood sugar levels. Ignoring diabetes is undesirable since the condition can have adverse impacts on the heart, kidneys, cholesterol levels, eyes, and almost all other organs. Diabetes will be easier to manage the earlier it is detected. To do this, researchers use a variety of machine learning techniques to more accurately forecast the onset of early diabetes in human bodies or in its patients. By constructing models using datasets that are available, machine learning techniques produce more accurate predictions. In the proposed work, Random Forest Algorithm is used to predict diabetes, as this technique outperforms in terms of accuracy. Individuals with diabetes, maintaining an appropriate diet is crucial. The majority of them aim to assist users in managing their blood sugar levels. A person's weekly meal preparation habits can be attributed to a myriad of intersecting elements, such as taste, healthfulness, financial constraints, personal preferences, and lifestyle. An individual might experience it difficult to choose what to eat. To assist with the management of diabetes, the proposed system advises using a customized meal planning strategy. The diet plan suggested in the system is based on decision tree technique which is diabetic-friendly that is also socially, economically, and culturally acceptable and that can satisfy all nutritional needs while being mindful of calories. Following the prediction of diabetes and the suggestion of a diet plan, the patient's medical information is stored in the EHR for convenient access. © 2024 IEEE.

关键词： Electronic health record

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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2024 International Conference on Advancement in Renewable Energy and Intelligent systems, AREIS 2024

作者： Raji, V. Maheswari, S. Dharinya, S. Sree Chinniah, P. Kavin, K.S. Raja, Kiruba Thangam S.K.P. Engineering College Dept. of Information Technology Tiruvannamalai India Tamilnadu Erode India School of Computer Science Engineering and Information Systems Vit University Dept. of Software and Systems Engineering Vellore India ST. Joseph College of Engineering Dept. of Ece Tamilnadu Sriperumbudur India Government College of Engineering Dept. of Eee Tamil Nadu Tirunelveli India Vit University Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350387230

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Fractional Order Ultra-Local Model-Based Sliding-Mode-Assissted Disturbance Observer and Model-Free Control

Fractional Order Ultra-Local Model-Based Sliding-Mode-Assiss...

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2024 International Conference Automatics and Informatics, ICAI 2024

作者： Zhang, Liuchang Wang, Haoping Tian, Yang Zhuang, Xincheng Azar, Ahmad Taher Ahmed, Saim Nanjing University of Science and Technology Dept. School of Automation Nanjing China Prince Sultan University Dept. Automated Systems & Soft Computing Lab Riyadh Saudi Arabia Prince Sultan University Dept. College of Computer and Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350353907

This paper puts forward a fractional order sliding-mode-assisted disturbance observer-based model-free control (FO-SAMDOMFC) methodology for fractional order system with disturbance. First, the system dynamic model is rewritten as a fractional order ultra-local model (ULM), and a SMADO is designed to estimate the lumped disturbance term. Then, A closed-loop control law is proposed on the basis of the ULM with the objective of guaranteeing the convergence of the tracking error. Next, a proof of the stability of the closed-loop system is given based on the fractional extension of Lyapunov's direct method. Finally, the simulation compared with other controllers based on MATLAB/Simulink is realized, and the simulation results validate the proposed method. © 2024 IEEE.

关键词： Sliding mode control

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Transfer Learning using BERT & Comparative Analysis of ML Algorithms for Opinion Mining 3

Transfer Learning using BERT & Comparative Analysis of ML Al...

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3rd IEEE Global Conference for Advancement in technology, GCAT 2022

作者： Gandhi, Bhuvanjeet Singh Kumar, Shubham Victor, Akila Selvanambi, Ramani Vellore Institute of Technology Dept. of Computer Science Vellore India Vellore Institute of Technology Dept. of Software Systems Vellore India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Sentiment Analysis is an ongoing field of research in text mining that is concerned with the computational treatment of textual views, sentiments, and subjectivity. It's the task of distinguishing between positive and negative viewpoints, emotions, and assessments. Sentiment analysis has been the topic of intensive research since its inception. During COVID-19, there has been a subtle increase in the usage and the time spent on the social networking sites by people as most of the daily operations have moved online. Moreover, in addition to the illness itself, the pandemic has led to dread, anxiety, stress, concern, repugnance, and poignancy in individuals all around the world. Considering these indicators, experts are paying close attention to Twitter data analysis during this pandemic. BERT is used as a transfer learning model and this work analyses the efficacy of fine-tuning it for the task of opinion mining by comparing it to a baseline model that includes a TF-IDF vectorizer and a Naïve Bayes classifier. Its performance is also compared to that of Naïve Bayes, Logistic Regression, K Nearest Neighbor, Decision Tree and XGBoost classifier. To determine the most effective settings for the BERT model, hyper-parameter tweaking is used. After two epochs of training at a learning rate of le-5 and batch size of 16, the maximum accuracy of 87.6% is attained. These results outperform all of the machine learning models examined in this study. This work tackles a comprehensive overview of the last update in this field. It can be beneficial to scholars in this domain because it encapsulates the most well-known Sentiment analysis methodologies and their comparison in single research work. © 2022 IEEE.

关键词： Sentiment analysis

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Cloud Video System Availability Assessment Using Markov and Semi-Markov Models 16

Cloud Video System Availability Assessment Using Markov and ...

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16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer Engineering, TCSET 2022

作者： Ivanchenko, Oleg Kharchenko, Vyacheslav Udovyk, Iryna Ponochovnyi, Yuriy Dnipro University of Technology Dept. of Computer Systems and Software Dnipro Ukraine National Aerospace University Dept. of Computer Systems Networks and Cybersecurity Kharkiv Aviation Institute Kharkiv Ukraine Dept. of Information Systems and Technologies Poltava Ukraine

ISBN: (纸本)9781665468619

Comparative analysis of Markovian and Semi-Markovian modelling for cloud video system availability is performed considering its architecture and dependability characteristics of various components. Initial data for modelling is based on review of research and operation data of cloud systems. The results of modelling provide decision making related to choice of technique of modelling and availability assessment. Besides, they contributes to ensuring the high availability of cloud systems and can be used by developers in order to address the issues into DevOps engineering area. © 2022 IEEE.

关键词： Availability

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Facial Shape-Based Eyeglass Recommendation Using Convolutional Neural Networks

Facial Shape-Based Eyeglass Recommendation Using Convolution...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rifat, Rakib Hossain Siddique, Sunzida Das, Laxmi Rani Haque, Mohd Ariful Brac University Dept. of Computer Science and Engineering Mohakhali Dhaka1212 Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Noakhali Science and Technology University Dept. of Computer Science and Telecommunication Engineering Sonapur Bangladesh Clark Atlanta University Dept. of Cyber-Physical Systems AtlantaGA United States

ISBN: (纸本)9781665430654

Eyeglasses are not only used to protect our vision and prevent dust from getting into our eyes. Additionally, glass that fits properly can give a person an elegant appearance. However, people often find it difficult to choose eyeglasses that fit their face shape;to address this issue, we have proposed a novel architecture in this paper. In order to do this, we created a pipeline that can recommend eyeglasses based on the form of the eyes using multiple transfer learning architecture to predict the face shape from a given image. We utilized InceptionV4 [17], InceptionV3[18], Vit Small [12], DenseNet121 [10], ResNet50 [9], and VGG16 [16] to predict the facial shape from the image and achieve a test accuracy of 75%. We used 5500 photos with five different face shapes (Heart, Oblong, Oval, Round, Square) for this experiment, and two distinct datasets were gathered from Kaggle [2] and GitHub [1]. By simply uploading the photograph to our recommendation system, our proposed solution can assist users in selecting the appropriate eyewear. © 2023 IEEE.

关键词： CNN DenseNet121 Eye Glass InceptionV3 InceptionV4 ResNet50 Trasnfer Learning VGG16 Vit Small

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Design and Evaluation of a Low-profile Haptic Interface Based on Surface Electrical Nerve Stimulation 1

Design and Evaluation of a Low-profile Haptic Interface Base...

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1st IEEE Conference on Telepresence, Telepresence 2024

作者： Jakes, Rachel S. Mesias, Luis Santos, Veronica J. Fu, Michael J. Tyler, Dustin J. Cwru Dept. of Biomedical Engineering United States Dept. of Mechanical and Aerospace Engineering United States United States Cwru Dept. of Electrical Computer and Systems Engineering United States The MetroHealth System Dept. of Physical Medicine & Rehabilitation United States Advanced Platform Technology Center Dept. of Va Northeast Ohio Healthcare System United States

ISBN: (纸本)9798331528249

Haptic feedback in telepresence applications is vital for remote task performance and object manipulation. To maximize telepresence capabilities, haptic systems need to be integrated with immersive telepresence interfaces such as virtual reality, which offer benefits such as improved spatial awareness and operator mobility. Compatibility with the mobility of these systems and their built-in optical hand tracking require low-profile wearable interfaces. Further, for full functional benefits, haptic systems must be able to elicit spatially congruent sensation without impeding operator movement and physical object interactions. In this paper, we describe the design of a lightweight, compact, and adaptable surface electrical nerve stimulation-based haptic interface. We demonstrate its capacity to consistently generate spatially congruent sensation on the all five fingertips without occlusion, as well as its compatibility with headset-based optical hand tracking. © 2024 IEEE.

关键词： Haptic interfaces

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