Human-computer Interface Technology Research Articles

Application of Virtual Reality Technology in English Teaching: Research on the Relationship between Learning Motivation and Learning Effectiveness

Virtual reality technology is an important direction of simulation technology, is a collection of simulation technology and computer graphics, human-computer interface technology, multimedia technology, sensing technology, network technology, and other technologies. It is a challenging cross-technology frontier discipline and research field. Virtual Reality (VR) technology mainly includes simulated environment, perception, natural skills and sensing devices. The simulated environment is a computer-generated, real-time dynamic three-dimensional stereo realistic image. This paper explores the transformative potential of virtual reality (VR) technology in English language teaching, focusing on the dynamic interplay between learning motivation and effectiveness. Through immersive VR environments, educators can cultivate increased engagement and foster language acquisition. A comprehensive literature review demonstrates the widespread adoption of VR technology in various fields and highlights its positive impact on learning outcomes. The proposed methodology advocates the seamless integration of VR technology with innovative pedagogical approaches to optimize English language instruction. By analyzing the relationship between learning motivation and effectiveness, this study sheds light on the nuanced dynamics of VR-enhanced learning in English language education.

The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action.

This study explores the important role of assessing force levels in accurately controlling upper limb movements in human-computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human-computer interface technologies.

A Study on the Development of Interactive Technology for the Convenience of the Visually Impaired

In this paper, as human-computer interface (HCI) technology advances, opportunities are analyzed to provide a better service environment for the visually impaired. The system and user interaction for the visually impaired will become possible through the convergence of technologies that include location-based services, wireless body sensor networks, and smart devices, which can have direct and positive impacts. The system connects to the user's mobility assistance device through a smartphone app and helps the user walk to identify and guide through obstacles. This interactive technology is expected to provide a convenient environment for visually impaired users.

Enhanced Defensive Model Using CNN against Adversarial Attacks for Medical Education through Human Computer Interaction

The design of human-machine interfaces is more precise and demanding in the medical and healthcare industries. Medical monitoring equipment demands more consistent and effective interpretation, as well as fast and straightforward operation due to its monitoring and reference functions. Consequently, it is crucial to consider how people interact with computers when designing the interface for medical monitoring devices. Nowadays people are giving more importance to health than anything in the world. Therefore, as it is related to peoples’ safety, the architecture of human-computer communication must be carefully considered in the studies and development of high-end medical equipment. The price of training physicians and other medical professionals is rising dramatically. Most of the countries have stepped forward from the traditional medical teaching system to a more human computer interactive teaching and learning environment with innovative technologies. This article focuses on the related researches, existing HCI applications for healthcare and the application of deep neural network for disease classification. The proposed work is to develop a healthcare learning platform to offer healthcare education to both medical practitioners and also for common people. This can be implemented as Mobile apps using human-computer interface technology and also as a website with Artificial Intelligence and Machine Learning Techniques. This proposal’s primary goal is to provide anytime, everywhere access to healthcare education for physiological and medical teaching courses, consequently advancing national health care.

A secured architecture for IoT-based healthcare system

Healthcare has gradually moved away from the model centered on traditional health centers due to the emergence of highly accurate sensors and Internet of Things (IoT) enabled medical equipment. Ambient intelligence takes whatever actions are required in response to a recognized event in order to enable continuous learning about patient data. The capabilities of IoT-assisted healthcare services might be improved by incorporating autonomous control and human-computer interface (HCI) technologies into ambient intelligence. Major unsolved issues include the privacy and security of information collected by medical IoT devices, both during transmission to and during cloud storage. This research explores different techniques, IoT factors, and features, with an emphasis on the data security concerns connected to data flow in medical IoT. In order to guarantee data security and privacy at all data levels, this study suggests a safe design for the IoT healthcare system.

Implementation of The Space Writer using Motion Detection

The software that recognizes writing in space without any physical touch with the device is known as the Space Writer. It's a method of generating digital artwork with the help of modern human-computer interface technology. The goal of this project is to propose a solution that tracks the user's hand gestures and draw the motion on the screen. Writing in space helps to develop automated processes and can improve the interface between man and machine in a variety of applications.

A Virtual EMG Signal Control and Analysis for Optimal Hardware Design

Background: A muscle-computer interface is one of the new applications of the human-computer interface technologies and specifically the brain-computer interface. Brain-muscle-computer interface based on the Electromyography (EMG) signal. EMG signal is an electrical activity from a muscle that is used as an input for effecting several tasks.Objective: This work presented an interfacing process between the Graphical User Interface (GUI) and hardware system. Using the implemented system, the researcher shall deals with the raw EMG data easily by analyzing the signal from the muscle sensor detection.Material and Methods: A novel virtual EMG signal control and analysis system design is proposed in this work. The system consists mainly of two parts, hardware and software toolbox. Hardware design is mainly dependent on using a muscle movement sensor as well as the feedback from the virtual toolbox. The virtual software design offers a relatively simple design of a friendly graphical user interface. It consists mainly of the input EMG signal and output signal after using different processing methods. Feedback response from the final EMG signal results after the processing may help the designer to present the optimal hardware design.Results: The output results show the output performance of the proposed virtual EMG data controlling and analysis with the implemented hardware design of the muscle sensor movement detection. The results show promise that these interfaces may provide a new option to benefit the designer in choosing the optimal prosthesis design of severely disabled persons.

Transfer learning based effective emotional face recognition using DCNN via cropping techniques

Facial Expression Recognition (FER) has grown in popularity as a result of the recent advancement and use of humancomputer interface technologies. Because the images can vary in brightness, backdrop, position, etc. it is challenging for current machine learning and deep learning models to identify facial expression. If the database is small, it doesn't operate well. Feature extraction is crucial for FER, and if the derived characteristics can be separated, even a straightforward approach can help tremendously. Deep learning techniques and automated feature extraction, allow some irrelevant features to conflict with important features. In this paper, we deal with limited data and simply extract useful features from images. To make data more numerous and allow for the extraction of just important facial features, we suggest innovative face cropping, rotation, and simplification procedures and advocate using the Transfer Learning technique to construct DCNN for building a very accurate FER system. By replacing the dense top layer(s) with FER, a pretrained DCNN model is adopted, and the model is then modified with facial expression data. The training of the dense layer(s) is followed by adjusting each of the pre-trained DCNN blocks in turn. This new pipeline technique has gradually increased the accuracy of FER to a higher degree. On the CK+ and JAFFE datasets, experiments were run to assess the suggested methodology. For 7-class studies on the CK+ and JAFFE databases, high average accuracy in recognition of 99.49% and 98.58% were acquired.

Emotion Detection Analysis using EEG and Physiological Signals for Hybrid Systems

Emotions are an inevitable and integral part of human existence. They form the basis of decisions taken by individuals and the way they perceive their surroundings. Method of articulation of emotions have changed with the increment in dependency between people and innovation. Now the need to recognize emotions has increased with the increasing role of human-Computer Interface (HCI) technology. There are many ways to record and identify human’s emotion using different neurophysiological measurements/ technologies like GSR(Galvanic Skin Response), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper, the focus is on emotion detection using EEG signals and other physiological signals and further analyzing them. There exist various machine learning techniques that have been used to pre-process and classify EEG data, have been reviewed in the paper. The analysis involves major aspects of the emotion recognition process like feature extraction, classification and comparison of the approaches. Different supervised machine learning algorithms have been applied to classify the EEG data. This paper focuses on comprehensive analysis of existing systems and based on the result propose the techniques which when applied will reap high-quality results.

TYPING EYES: A HUMAN COMPUTER INTERFACE TECHNOLOGY

The human’s beings always make use of their sensory modalities to effectively communicate or carry out their jobs. The humans use the sensory organs such as the ears, eyes mouth, including the hands to deliver a good verbal conversation. The human computer interface technology could also be made classier by involving the human voice, hands and eyes to access an application or control a device etc.; they paper puts forth the development of the human computer interface based on the eyes movement for typing the information without using the hand for typing, this could be very much useful for the patients with disabilities in the arms or affected by paralytic attacks for their regular works associated with the computers and can also be used by the normal people in security intensive applications, the evaluation results obtained for the proposed method using the MATLAB shows that the mean typing speed to be 30 characters per minute.

Speech Emotion Recognition Using Convolutional- Recurrent Neural Networks with Attention Model

Speech Emotion Recognition (SER) plays an important role in human-computer interface and assistant technologies. In this paper, a new method is proposed using distributed Convolution Neural Networks (CNN) to automatically learn affect-salient features from raw spectral information, and then applying Bidirectional Recurrent Neural Network (BRNN) to obtain the temporal information from the output of CNN. In the end, an Attention Mechanism is implemented on the output sequence of the BRNN to focus on target emotion-pertinent parts of an utterance. This attention mechanism not only improves the classification accuracy, but also provides model’s interpretability. Experimental results show that this approach can gain 64.08% weighted accuracy and 56.41% unweighted accuracy for four-emotion classification in IEMOCAP dataset, which outperform previous results reported for this dataset.

Eye state recognition based on deep integrated neural network and transfer learning

Eye state recognition is widely used in many fields, such as driver drowsiness recognition, facial expression classification, and human–computer interface technology. This study proposes a novel framework based on the deep learning method to classify eye states in still facial images. The proposed method combines a deep neural network and a deep convolutional neural network to construct a deep integrated neural network for characterizing useful information in the eye region by use of the joint optimization method. A transfer learning strategy is applied to extract effective abstract eye features and improve the classification capability of the proposed model on small sample datasets. Experimental results on the Closed Eyes in the Wild (CEW) and Zhejiang University Eyeblink datasets show that the proposed approach outperforms other state-of-the-art methods. In addition, the effects of transfer learning methods with different pretraining datasets on classification accuracy are investigated with the CEW dataset. A driver drowsiness recognition dataset is constructed and used in an experiment to evaluate the effectiveness of the proposed method in driving environments. Experimental results demonstrate that the proposed method performs more stably and robustly than do other methods.

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Attractive Agents Are More Persuasive

Advances in more ubiquitous human–computer interface technologies have resulted in the increased use of virtual agents, thus highlighting the need to understand the impact these artificial entities have on the human user in terms of how the user both perceives and behaves toward these characters. We report the results of an experiment based on the Desert Survival task, where the effect of agent attractiveness on persuasion was tested. The results suggest that the attractive agent was significantly more persuasive in changing the participant's opinion than was the unattractive agent. Furthermore, greater behavioral change was observed when the participant interacted with the attractive agent, in terms of persuasiveness, including the attribution of more positive personality traits toward this agent (the attractiveness stereotype). These findings provide further evidence of the powerful influence of the agent's physical appearance on the human user in an interaction-based context. We conclude by pointing toward future research and potential within the area.

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