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检索条件"任意字段=8th IFIP International Conference on Engineering for Human-Computer Interaction"

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Machine Learning Regression Models for Real-Time Touchless interaction Applications 8

Machine Learning Regression Models for Real-Time Touchless I...

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8th IEEE international conference on Software engineering and computer Systems, ICSECS 2023

作者： Gong, Qinyan Hafizh, Hadyan Ateeq, Muhammad Majeed, Anwar Pp Abdul Isaac, Matilda Hu, Bintao School of Internet of Things Xi'An Jiaotong-Liverpool University Suzhou China School of Robotics Xi'An Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9798350310931

Touchless technologies have gained significant popularity, particularly amidst the COVID-19 pandemic, as they addressed concerns related to germ transmission and hygiene during human-device interactions. this study aimed to develop an intuitive and user-friendly touchless system by combining eye gaze and hand gesture methods. Four regression machine learning models, namely Ridge regression, Lasso regression, Linear regression, and Gradient-boosting regressor, were trained and tested using standard metrics such as coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). the results indicated that Ridge regression outperformed the other models, demonstrating a high R2 value of 0.98. Leveraging this model, a simulation was conducted to evaluate the effectiveness of integrating eye gaze and hand gestures in realtime touchless interactions. the simulation demonstrated the successful integration of these modalities in the item selection process, providing users with a seamless and intuitive interaction method. this touchless interaction technology enabled effortless and accurate navigation of screens and facilitated item selections. the promising results highlighted the potential of this technology, presenting exciting opportunities for its integration with actuators. By incorporating actuators, touchless interaction systems could revolutionize various industries, including retail, healthcare, hospitality, and smart home automation. © 2023 IEEE.

关键词： Actuators

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Mental Stress Assessment in Working Environment for an Individual Using Wearable Sensor of EEG and Pulse Signal Measured with Help of Deep Learning Algorithm 8th

Mental Stress Assessment in Working Environment for an Indiv...

引用

8th ifip TC 12 international conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Karthikeyan, M.V. Bhuvaneshwar, S. Nishanth, V. Department of Electronics and Communication Engineering St. Joseph’s Institute of Technology OMR Tamil Nadu Chennai60019 India

ISBN: (纸本)9783031736162

thanks to artificial intelligence (AI), machines will eventually have the same emotional impact as humans. Deep emotions, not just words, are used by this "affective computing" to engage with humans. Bypassing societal masks, sensors like the EEG and GSR are able to sense our genuine feelings. However, creating robots that are actually sensitive to our emotions is challenging. To build genuinely emotionally intelligent machines, we need to improve the methods we use to choose, arrange, and evaluate these signals. this paper propose a method for assessing mental stress in the workplace in real-time using deep learning algorithms in conjunction with wearing EEG and GSR. the major goal is to make computers smarter emotionally so that they can change the way people interact with computers (HCI). Our proposed system can discreetly track a worker’s emotional and psychological well-being as they go about their daily tasks. By combining electroencephalogram (EEG) and pulse signal analysis, our system is able to record not only the mental but also the physiological reactions to mental stress. © ifip international Federation for Information Processing 2025.

关键词： Wearable sensors

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Parkinson's Disease Detection using Machine Learning Algorithm 8

Parkinson's Disease Detection using Machine Learning Algorit...

引用

8th international conference on Communication and Electronics Systems, ICCES 2023

作者： Shabu, S. L. Jany Sivapriya, V. Josephine Kavya, F. Refonaa, J. Dhamodaran, S. Poornima Sathyabama University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396638

Parkinson's disease (PD) is a neurological ailment characterised by the gradual onset of motor symptoms such as tremors and muscle stiffness. It might be challenging to make a correct diagnosis of PD in its early stages since the symptoms may be similar to those of other conditions or to age-related physiological changes. this research uses convolutional neural networks, a type of deep neural network, to aid in the diagnosis of Parkinson's disease. With the help of the patient, the drawing exercises are employed to detect abnormal motor patterns. When trained on pictures of spiral drawings, the proposed convolutional classifier successfully distinguishes PD patients from controls 91.67% of the time. An improved version of the proposed program that can do a specific diagnostic procedure automatically and with minimum human interaction is not far off. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Personalized Assistance through Artificial Intelligence: Smart Voice Appliances 8th

Personalized Assistance Through Artificial Intelligence: Sma...

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8th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Jain, Archana Rathore, Yogesh Singh Maurya, Santosh Kumar Malik, Suraj Bansal, Nidhi Pandey, Mahima Shankar Saraswat, Bal Krishna Computer Science and Engineering Department School of Engineering and Technology IIMT University Uttar Pradesh Meerut India Computer Science and Application Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Computer Science and Engineering Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Galgotia College of Engineering and Technology Uttar Pradesh Greater Noida India Nagpur Campus Pune India

ISBN: (纸本)9789819766772

In today’s world of growing technology, we can do things we never thought we could do before, but to achieve these ideas, there is a need for a platform that can do all our work easily and comfortably. So, we humans have developed applications such as the human Voice Assistant with the ability to interact with the environment in one of the most visible forms of human interaction namely humanVOICE. the most popular Android mobile app "Google Assistant" and "Google Voice Search" were developed by Google. Various applications such as Microsoft Cortana and Amazon Alexa are also used as voice assistants. the Apple iPhone’s voice assistant named "SIRI" helps the users to communicate with the services and applications of iOS by voice and respond to the user’s voice commands. We have built an application where the voice assistant will be available on a specific desktop or laptop. It can change the overall course of interaction between the user and the system. the system designed here is such that all the services provided by the system are accessible through the user’s voice commands. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： engineering education

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Medical-Domain-Expert-Centered AI-Assisted Digitized Histopathology Education Tool 10th

Medical-Domain-Expert-Centered AI-Assisted Digitized Histopa...

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10th ifip WG 13.2 international Working conference on human-Centered Software engineering (HCSE)

作者： Vaczlavova, Erika Laco, Miroslav Slovak Univ Technol Bratislava Fac Informat & Informat Technol Ilkovicova 2 Bratislava 84216 Slovakia

ISBN: (纸本)9783031645754;9783031645761

Education in the medical domain with the help of digitized content using state-of-the-art technology is a current topic, nowadays. In this paper, we are presenting our ongoing research on streamlining the medical domain expert workflow in the field of histopathology by designing a graphical user interface for an artificial-intelligence-powered (AI-powered) assistance educational tool for digitized specimen annotations. We found out that the approach to the presentation of the AI-powered features in the user interface should be built upon the level of expertise of the domain expert. the differences in interactions with the proposed annotation tools were observed in a case study with five participants with different levels of expertise from ongoing research cooperation with medical faculty students and domain experts in the field of histopathology. We discuss the quantitative and qualitative outcomes from the aforementioned case study to serve as a base for maximizing the benefits leveraging from applying AI-powered assistance tools in the field of education in digitized histopathology.

关键词： human-AI interaction User Experience Histopathology

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Gesture-Enhanced Presentation Control for Education 8

Gesture-Enhanced Presentation Control for Education

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8th IEEE international conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Saumya, Y.M. Dcruz, Colin Christon Rodrigues, Shane Priyanka Dsouza, Austin Braggs, Daniel Loy Pereira, Elwin Jason St Joseph Engineering College Dept. of CSE Vamanjoor India

ISBN: (纸本)9798350350593

Presentation skills are vital in many areas of life. Giving presentations is probably a common experience for anyone, whether they are a worker, student, business owner, or employee of an organisation. the requirement to manage and manipulate the slides with a keyboard or other specialised device might make presentations seem tedious at times. Enabling users to control the slideshow with hand gestures is the aim of this work. Gestures have become increasingly common in human-computer interaction in recent years. Several PowerPoint functionalities have been attempted to be controlled by hand movements by the system. this system maps motions using multiple Python modules and uses machine learning to identify motions with minute variances. Creating the perfect presentation is becoming increasingly difficult due to a number of aspects, including the slides, the keys to switching the slides, and the audience's composure. An intelligent presentation system that is based on hand gestures makes it simple to update or modify the slides. Allowing viewers to explore and manipulate the slideshow with hand movements is the technology's main objective. the technique recognises various hand motions for a variety of tasks using machine learning. A means of recognition opens up a line of communication between people and machines. © 2024 IEEE.

关键词： Gesture recognition

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Efficient Gender Recognition with Deep Learning Models on Balanced Facial Image Datasets 8

Efficient Gender Recognition with Deep Learning Models on Ba...

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8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poddar, Kishan Sharma, Bhanu Chitkara University Chitkara University Institute of Engineering & Technology Punjab India

ISBN: (纸本)9798350376425

From security to marketing to human-computer interaction, gender recognition from facial photographs is a basic task in many disciplines. this effort intends to develop an appropriate gender recognition model using a balanced dataset including 1,292 images equally distributed between men and women. Training, validation and testing sets were created from diverse age groups, ethnicities and environments of the dataset to ensure proper evaluation. Transfer learning methods were used to accelerate training and increase generalization using ResNet50, a deep learning model developed on convolutional neural networks (CNN). Designed for the specific difficulty of gender recognition, the pre-trained model was developed on leveraging characteristics obtained from a large dataset to achieve amazing accuracy with minimal data and processing resources. Both suitable for classification issues, the model was optimized using cross-entropy loss and Adam optimizer. Performance evaluation was shown by remarkable accuracy of 97% coupled with outstanding precision, recall and F1-scores including both genders. Low rates of misclassification help to establish even more the dependability of the model by use of the confusion matrix. these results show the viability of the approach and its use in pragmatic gender recognition programs. © 2024 IEEE.

关键词： Convolutional neural networks

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Teaching HCI engineering: Four Case Studies 18th

Teaching HCI Engineering: Four Case Studies

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18th ifip TC 13 international conference on human-computer interaction (INTERACT)

作者： Caffiau, Sybille Campos, Jose C. Martinie, Celia Nigay, Laurence Palanque, Philippe Spano, Lucio Davide Univ Grenoble Alpes CNRS Grenoble INP LIG F-38000 Grenoble France Univ Minho Braga Portugal INESC TEC HASLab Braga Portugal Univ Paul Sabatier Toulouse III IRIT Toulouse France Univ Cagliari Dept Math & Comp Sci Via Osped 72 I-09124 Cagliari Italy

ISBN: (纸本)9783030983888;9783030983871

the paper presents the work carried out at the HCI engineering Education workshop, organised by ifip working groups 2.7/13.4 and 13.1. It describes four case studies of projects and exercises used in human-computer interaction engineering courses. We propose a common framework for presenting the case studies and describe the four case studies in detail. We then draw conclusions on the differences between the presented case studies that highlight the diversity and multidisciplinary aspects to be taught in a human-computer interaction engineering course. As future work, we plan to create a repository of case studies as a resource for teachers.

关键词： Teaching HCI engineering User-Centered Design Software engineering

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the Priority of Information for Crew Members in Various Flight Scenarios: An Eye-Tracking Study 16th

The Priority of Information for Crew Members in Various Flig...

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16th international conference on Cross-Cultural Design (CCD) Held as Part of the 26th international conference on human-computer interaction (HCII)

作者： Hao, Xiaohui Zhang, Haochun Tan, Hao Shanghai Aircraft Design & Res Inst COMAC Shanghai 201210 Peoples R China Hunan Univ Sch Design Changsha 410000 Peoples R China

ISBN: (纸本)9783031609121;9783031609138

the cockpit of modern commercial aircraft has a complex interface layout and the complex human-machine interaction interfaces can impose a cognitive load on the flight crew. A cockpit interface that meets human factors engineering requirements can reduce pilot cognitive load, enhance operational efficiency, and improve flight safety. In order to summarize the interaction mechanisms between flight crew members and cockpit interfaces, this research employs the methodology of eye-tracking to investigate the level of attention and salience that crew members pay to different areas of the cockpit in different flight scenarios. these two aspects are respectively reflected through the pilot's total duration of fixation and number of visits to specific areas. the eye-tracking experiments involved the participation of two experienced professional pilots in a flight simulator that accurately replicates the cockpit of mainstream aircraft in proportion. the flight crew, consisting of the pilot flying (PF) and the pilot monitoring (PM), was tasked with executing flight procedures at eight stages during normal flight operations. these eight stages include cockpit preparing, engine starting, taxiing, taking-off, approaching, landing, after-landing, and shutdown. By dividing the aircraft cockpit into 18 areas of interest, we found that the crew members' focus areas within the cockpit interface dynamically change at different stages of the flight. through eye-tracking analysis, we summarized and prioritized the focus areas for the PF and the PM in eight different flight scenarios. the results can help designers of human-machine interfaces in aircraft cockpits to capture design requirements and provide guidance for future aircraft cockpit human-machine interaction design.

关键词： Aircraft Cockpit Eye-Tracking human-Machine interaction Design

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Camouflaged Object Detection Based on Multi-Scale Fusion Network with Fuzzy Region Enlargement and Shrinking 8

Camouflaged Object Detection Based on Multi-Scale Fusion Net...

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8th international conference on computer Graphics and Virtuality, ICCGV 2025

作者： Xia, Yunshuai Han, Ziyang College of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9781510689213

this paper proposes a novel multi-scale fusion network based on fuzzy region enlargement and shrinking (SVFNet) for camouflaged object detection (COD) tasks. Camouflaged objects are highly similar to their backgrounds in terms of texture, color, and shape, making them difficult to detect. To improve detection performance, SVFNet mimics human visual habits by enlarging and shrinking the original image to extract multi-scale features, thereby enhancing detection accuracy. SVFNet first scales the image down to 0.5x and enlarges it to 1.5x, generating multi-scale images, and then extracts features using a shared triple-feature encoder. the DS-ASPP module is used to fuse multi-scale features, while the MAF module aggregates key features through an attention mechanism, and the HFI module strengthens information interaction between channels. SVFNet also introduces an Uncertainty-Aware Loss (UAL) to enhance detection ability in fuzzy regions. Experiments show that SVFNet outperforms existing methods on the CAMO, COD10K, and NC4K datasets, particularly in low-contrast and complex backgrounds, reducing false positives and false negatives. Ablation experiments validate the effectiveness of each module. Although the model could be improved in terms of middle-layer feature handling and training efficiency, SVFNet provides an innovative and efficient solution for camouflaged object detection. © 2025 SPIE.

关键词： Object recognition

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