Human-computer Interaction Technology Research Articles

Literature Review of “Gesture Navigator: AI Based Virtual Mouse”

Abstract: The mouse is one of the wonderful inventions of Human-Computer Interaction (HCI) technology. Currently, wireless mouse or a Bluetooth mouse still uses devices and is not free of devices completely since it uses a battery for power and a dongle to connect it to the PC. In the proposed AI based virtual mouse system, this limitation can be overcome by employing webcam or a built-in camera for capturing of hand gestures and hand tip detection using computer vision. The algorithm used in the system makes use of the machine learning algorithm. Based on the hand gestures, the computer can be controlled virtually and can perform left click, right click, scrolling functions, and computer cursor function without the use of the physical mouse. The algorithm is based on deep learning for detecting the hands. Hence, the suggested system aims to minimize the risk of communicable diseases like COVID-19 transmission by reducing human contact and eliminating the need for additional devices to control the computer system.

Fusion inception and transformer network for continuous estimation of finger kinematics from surface electromyography.

Decoding surface electromyography (sEMG) to recognize human movement intentions enables us to achieve stable, natural and consistent control in the field of human computer interaction (HCI). In this paper, we present a novel deep learning (DL) model, named fusion inception and transformer network (FIT), which effectively models both local and global information on sequence data by fully leveraging the capabilities of Inception and Transformer networks. In the publicly available Ninapro dataset, we selected surface EMG signals from six typical hand grasping maneuvers in 10 subjects for predicting the values of the 10 most important joint angles in the hand. Our model's performance, assessed through Pearson's correlation coefficient (PCC), root mean square error (RMSE), and R-squared (R2) metrics, was compared with temporal convolutional network (TCN), long short-term memory network (LSTM), and bidirectional encoder representation from transformers model (BERT). Additionally, we also calculate the training time and the inference time of the models. The results show that FIT is the most performant, with excellent estimation accuracy and low computational cost. Our model contributes to the development of HCI technology and has significant practical value.

Face and Speech Emotion Recognition System

Abstract: Emotions serve as the cornerstone of human communication, facilitating the expression of one's inner thoughts and feelings to others. Speech Emotion Recognition (SER) represents a pivotal endeavour aimed at deciphering the emotional nuances embedded within a speaker's voice signal. Universal emotions such as neutrality, anger, happiness, and sadness form the basis of this recognition process, allowing for the identification of fundamental emotional states. To achieve this, spectral and prosodic features are leveraged, each offering unique insights into the emotional content of speech. Spectral features, exemplified by the Mel Frequency Cepstral Coefficient (MFCC), provide a detailed analysis of the frequency distribution within speech signals, while prosodic features encompass elements like fundamental frequency, volume, pitch, speech intensity, and glottal parameters, capturing the rhythmic and tonal variations indicative of different emotional states. Through the integration of these features, SER systems can effectively simulate and classify a diverse range of emotional expressions, paving the way for enhanced human-computer interaction and communication technologies

Application of e-learning and new media teaching platform based on human computer interaction technology in civil and commercial law courses

At present, the traditional teaching mode has been difficult to meet the students' needs for independent learning, interactive learning and diversified learning resources. Therefore, it is of great significance to develop an e-learning platform that can provide rich learning resources and promote student participation and individualized learning to improve the teaching effect. The research adopts the e-learning new media teaching platform based on human–computer interaction technology, and combines the civil and commercial law courses. The e-learning platform provides personalized learning recommendations according to the students' learning conditions, interests and hobbies. It effectively stimulates the enthusiasm and initiative of students to learn. Teachers can monitor and evaluate students' learning situation in real time through the platform, so as to give better guidance and feedback. The e-learning new media teaching platform based on human–computer interaction technology can greatly improve students' learning effect and learning participation. With the help of the platform, students are easier to understand and master the course content, and through online discussion and interaction can better deepen the understanding and application of knowledge. Personalized learning recommendation also enables students to learn according to their own learning needs and improves their learning efficiency.

Human Eye Activity Monitoring Using Continuous Wave Doppler Radar: A Feasibility Study.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

IHand: Hand Recognition-Based Text Input Method for Wearable Devices

Text input using hand gestures is an essential component of human–computer interaction technology, providing users with a more natural and enriching interaction experience. Nevertheless, the current gesture input methods have a variety of issues, including a high learning cost for users, poor input performance, and reliance on hardware. To solve these problems and better meet the interaction requirements, a hand recognition-based text input method called iHand is proposed in this paper. In iHand, a two-branch hand recognition algorithm combining a landmark model and a lightweight convolutional neural network is used. The landmark model is used as the backbone network to extract hand landmarks, and then an optimized classification head, which can preserve the space structure of landmarks, is designed to classify gestures. When the landmark model fails to extract hand landmarks, a lightweight convolutional neural network is employed for classification. Regarding the way letters are entered, to reduce the learning cost, the sequence of letters is mapped as a two-dimensional layout, and users can type with seven simple hand gestures. Experimental results on the public datasets show that the proposed hand recognition algorithm achieves high robustness compared to state-of-the-art approaches. Furthermore, we tested the performance of users’ initial use of iHand for text input. The results showed that the iHand’s average input speed was 5.6 words per minute, with the average input error rate was only 1.79%.

Phantom sensation: Threshold and quality indicators of a tactile illusion of motion

Utilizing a randomized, blind, controlled experiment, and the ascending method of limits, we determined the minimum amplitude of motion at which individuals perceive a tactile illusion called moving phantom sensation, the perceived level of clarity and continuity of motion. Implementing tactile illusions in virtual/augmented reality, sensory substitution systems, and other human–computer interaction technologies results in interfaces with improved resolution, using two vibrating actuators only. The actuators are attached to the skin in different locations to render a moving phantom sensation. The intensity of vibrations increases in one actuator while decreases in the other according to the envelope of the voltage supply signals. This intensity variation creates the illusion of a vibrating point moving between the actuators. We gradually increased the amplitude of motion until the participant reported perceiving the illusion, for eight values of duration of the stimulus from 0.1 to 6.0 s. Participants perceived the illusion at a minimum amplitude of motion of 20%; being 100% the motion from one actuator to the other. The median level of clarity of the perceived illusion at the minimum amplitude of motion was 2 (not so clear). Finally, we found a positive correlation between duration and continuity of motion.

Voltage-controlled finger friction behavior for immersive tactile experience in aqueous environment

To mimicking fingers perceiving the physical world, researchers developed tactile devices to establish a correlation between real and virtual world. This study proposed a new finger friction modulation method based on voltage-controlled technique in aqueous environment. Under continuous or alternate voltages applied to TC4 plate, the effects of NaCl, KCl and CaCl2 on finger friction were characterized with different concentrations. The finger friction was reversibly modulated by the voltages with the maximum friction variation of 110%. This modulation is attributed to the adsorption of OH-, bubbles effect and formation of passive film which affected the boundary lubrication performance of finger-TC4 interface. This study provides a new surface haptics concept for the underwater human-computer interaction or immersive virtual reality haptic technology.

Intelligent Vehicle Violation Detection System Under Human–Computer Interaction and Computer Vision

In view of the current problems of low detection accuracy, poor stability and slow detection speed of intelligent vehicle violation detection systems, this article will use human–computer interaction and computer vision technology to solve the existing problems. First, the picture data required for the experiment is collected through the Bit Vehicle model dataset, and computer vision technology is used for preprocessing. Then, use Kalman filtering to track and study the vehicle to help better predict the trajectory of the vehicle in the area that needs to be detected; finally, use human–computer interaction technology to build the interactive interface of the system and improve the operability of the system. The violation detection system based on computer vision technology has an accuracy of more than 96.86% for the detection of the eight types of violations extracted, and the average detection is 98%. Through computer vision technology, the system can accurately detect and identify vehicle violations in real time, effectively improving the efficiency and safety of traffic management. In addition, the system also pays special attention to the design of human–computer interaction, provides an intuitive and easy-to-use user interface, and enables traffic managers to easily monitor and manage traffic conditions. This innovative intelligent vehicle violation detection system is expected to help the development of traffic management technology in the future.

Current research on human-computer interaction based on VR in flight simulation driving

With the rapid development of the aviation industry, the demand for pilots in the world is increasing day by day, and the flight simulator industry has received more and more attention and attention. However, due to the rapid upgrading of flight simulation technology, many problems arise. This review aims to deeply explore the application of human-computer interaction technology including VR technology in flight simulation driving, so as to reduce the driver's work burden during driving and improve the learning efficiency of flight simulation. Starting from virtual reality technology and human-computer interaction technology, this paper reviews the development and application of human-computer interaction technology based on virtual reality (VR) in the field of flight simulation and driving, and discusses the application of VR technology and human-computer interaction technology in the field of flight simulation and driving development and testing. Finally, this review summarizes the current application status and challenges of human-computer interaction technology in the field of flight simulation driving. Although human-computer interaction technologies such as VR have made remarkable progress in the field of flight simulation, there are still problems such as high cost of hardware equipment, operational stability, and fidelity that need to be solved. In the future, it is worth looking forward to the continuous innovation of VR-based human-computer interaction technology in flight simulation driving, bringing more development opportunities for flight training and the aviation industry.

Exploring the synergy of human-computer interaction in financial decision-making

With the rapid development of technology, the combination and complementarity of artificial intelligence (AI) and human expertise in finance is becoming increasingly important. This research focuses on the synergistic effect of human-computer interaction (HCI) in financial decision-making, analyzes the operation mode of financial decision-making, lists examples of the combination of related fields and artificial intelligence technology, and focuses on showing that human-computer interaction technology enhances user experience and benefits in financial activities. Ability to invest in performance. Emerging features such as personalized financial services, intuitive user interfaces, and real-time feedback can dramatically improve user experience, research shows. Integrating artificial intelligence and professional knowledge can improve investment performance, assist risk management and decision support. The study emphasizes the importance of human-computer complementarity and balance, which has important reference value for financial institutions to adjust plans, policy makers to modify laws and regulations, and researchers to improve user experience and enhance investment results.

Hand gesture recognition in natural human-computer interaction

This paper introduces the definition of Gesture recognition. First the article gives a precise definition of gesture recognition and explains the difference between gestures and postures, then it reveals technical difficulties of Gesture recognition. These technical difficulties include four aspects. After analyzing the technology and methods of Gesture recognition and the technical difficulties, it concretely expounds gesture recognition process based on data gloves, which is widely studied before and it also introduces the computer vision based research achievement which is currently becoming a research hotspot in this field. Then it also takes Kinect and HoloLens as examples to introduce specific practical cases of gesture recognition in wearable devices. Also it outlines the application of gesture recognition technology in human-computer interaction which include but not limited to smart terminal, Game control, Robot Control, Clinical and Health, Smart home, Sign Language Recognition, Vehicle system, Interactive entertainment. Finally it reach the conclusion that the biggest challenge researchers meet is to build a powerful framework to overcome the common problems with fewer constraints to provide reliable results. And sometimes researchers need to combine multiple methods for different complex environments.

Human-Computer Interaction Corporate Law Education for Directors: A Machine Learning Approach

With the formation and growth of the company, corporate law is continuously established and enhanced. It significantly contributes to the company’s healthy growth and brings business operations inside the legal framework. As a result, corporate law education is crucial for employees, particularly directors. Fundamentally, corporate law education is a learning process. The metaverse period has increased learners’ needs for learning environments, and human-computer interaction technology will offer all-encompassing support for the smart learning environment. The board of directors needs to adequately monitor each director’s learning level as they study corporate law, which will inevitably be detrimental to the company’s long-term growth. The most efficient method to address this issue is to use eye movement data to mine different eye movement patterns, followed by an analysis of the learning state of corporate law of directors. The scanning path analysis is used to examine the similarities and differences of the directors’ eye movement behaviors during the study of corporate law to enhance the state of corporate law learning. However, the learning status of corporate law cannot be determined only by the eye tracking of directors. We employ the convolutional neural networks (CNN) -based emotion recognition model to provide the directors constructive criticism about their learning state and offer suggestions for the learning mode. The experimental results demonstrate that time series-based eye movement pattern mining can identify directors’ viewing habits, and clustering can reveal different learning strategies that can be used to evaluate directors’ corporate law learning status. Additionally, the CNN-based emotion recognition model experiment also shows that the established model has an accuracy of 97.0035% and an F1 of 0.9412 in the CASIA-FaceV5 dataset, which helps evaluate the emotions of directors when learning company law.

Exploration of the Role of Human-computer Interaction Technology in Collaboration and Communication in Work Scenarios

With the development of technology, human-computer interaction technology has gradually become a key tool for improving work efficiency, promoting collaboration and communication in work scenarios. This article first introduces the concept and application fields of human-computer interaction technology, then analyzes the collaborative and communication needs in the work environment, and then explores the collaborative and communication effects of human-computer interaction technology in the work environment. Finally, this article presents the challenges and corresponding solutions faced by human-computer interaction technology in work scenarios. Through exploration, the aim is to provide useful references for enterprises and policy makers to fully leverage the advantages of human-computer interaction technology in work scenarios.

The Intersection of Art and Technology: Innovative Thinking and Practice in the Perspective of New Media

Abstract The swift advancement of science and technology has emerged as a pivotal catalyst for artistic innovation, with immersive art displays becoming increasingly prevalent as a favored mode of creative expression. This paper conducts a thorough analysis of the flow dynamics within immersive art display spaces, utilizing the eight-channel cardio fluid flow model as a foundation. By integrating the immersive communication effect model, this study further investigates the communicative aspects of these spaces. The implementation of human-computer interaction technologies not only enhances visual presentation but also facilitates the design of immersive art spaces through advanced scene modeling techniques. This design process includes optimizing the viewer’s perspective by incorporating visual perception parameters and the grid saliency MS algorithm. Additionally, this research undertakes a quantitative evaluation of interactive applications at the nexus of art, science, and technology, focusing on audience demand, interactive experiences, and recognition. The findings reveal a significant increase in audience demand for digital media interaction, from 12.64% to 31.29%, and an impressive average tolerance score of 84.32 for immersive art spaces. Comments related to these spaces uniformly express positive recognition, underscoring the successful integration of science and technology in art displays. This integration, as demonstrated, fosters a diverse evolution of artistic expression forms.

Design of Brand Culture Mental Model Based on Human-Computer Interaction in Artistic Visual Expression

Nowadays, people look at a brand not only to see the value of the brand itself, but to understand the cultural value conveyed behind the brand and experience the connotation of the brand culture. Human-computer interaction technology has also gained more application space with the development of the times. Therefore, a psychological model of brand culture based on human-computer interaction was designed in this paper, and a survey of related content was conducted. In terms of user satisfaction survey, it was concluded that the use of the brand culture mental model based on human-computer interaction technology could greatly improve users’ satisfaction with brand culture and make more people love brand culture; in terms of user participation survey, it was concluded that the brand culture mental model designed based on human-computer interaction technology could achieve better profit results at 21:00 on Sunday. Finally, a survey of user stickiness was carried out, and the test results showed that the brand culture mental model based on human-computer interaction technology established the stickiness between users and brands.

Integrating Human-Computer Interactions in Nigerian Energy System: A Skills Requirement Analysis

The energy sector constitutes an important contributor to the growth and development of the Nigerian economy. Despite Nigeria's abundant energy resources, the sector faces multiple challenges, such as infrastructural deficits, policy inconsistencies, regulatory uncertainties, and overreliance on non-renewable energy sources. Industry 4.0 technologies and Human-Computer Interaction (HCI) present opportunities that address the challenges of the energy sector in Nigeria. This research explores the impact of HCI in the adoption of technologies of 4IR (Artificial Intelligence, the Internet of Things, Big Data, and Cloud Computing), in Nigeria's energy sector. The research assesses the current level of implementation of Industry 4.0, requirements for the implementation of Industry 4.0 technology, demand, and skills for HCI in the implementation of 4IR technology in Nigeria's energy generation sub-sector. This study adopted a comprehensive and critical systematic review of existing literature on the subject matter. The findings reveal the level of 4IR implementation in its infancy, with some energy companies and start-ups beginning to integrate digital technologies to enhance their operations and services. The study further revealed the potential of HCI in facilitating the integration and social acceptance of 4IR technologies and identified significant skills demand for HCI, such as digital literacy, data analysis, critical thinking, problem-solving, and system thinking to facilitate the adoption of 4IR. The research concludes that the development of HCI skills in the adoption of 4IR presents a great opportunity to transform the energy sector in Nigeria. The research recommends policy formulation, to embrace Industry 4.0 and multifaceted process development of HCI skills for the application of 4IR in the energy sector.

Bridging Communication Gaps: A Comprehensive Survey on Sign Language Hand Gesture Smart Recognition Technology

Abstract: Efficient communication is crucial in human interaction, yet individuals with language and hearing challenges encounter obstacles. This article centers on Nepali Sign Language (NSL) and introduces the Hand Gesture Smart Recognition initiative, with the goal of translating NSL into English. The research explores hand gesture recognition technology, emphasizing the cultural subtleties inherent in both vision-based and glove-based approaches. Recent advancements, such as the integration of compact models and CNNs, signify advancements in gesture recognition. The article offers valuable insights into the development and application of this technology, fostering optimism regarding the future potential of culturally suitable human-computer interaction technologies.

Neural interface technology for human-computer interaction

Brain-Computer Interfaces (BCI) offer a direct communication channel between the brain and external devices, marking a pivotal convergence of neuroscience and technology. Neural activities, essentially electrical impulses, can be captured either invasively, with methods such as Electrocorticography (ECoG) which require surgical implantation, or non-invasively using techniques like Electroencephalography (EEG) that operate externally. Once acquired, raw neural data undergoes processing; external and physiological noises are filtered out, and meaningful patterns or neural fingerprints are extracted. Modern BCIs then employ machine learning, specifically deep learning, to translate these cleaned neural patterns into discernible commands, with continuous feedback loops enhancing system adaptability. These decoded signals can control varied devices, from medical-grade robotic limbs to cursors on screens. BCIs have transformative applications across sectors: theyre pivotal in neurorehabilitation after brain injuries, providing feedback where traditional methods might fail; theyre integrated with virtual reality for immersive feedback; they revolutionize cognitive training and meditation practices; and theyre finding a foothold in high-risk sectors like deep-sea exploration and military operations. Additionally, research on tactile Event-Related Potential (ERP)-based BCIs emphasizes the importance of congruent Control-Display Mapping for efficient user experience. However, with these advancements come ethical concerns, such as the potential invasion of the privacy of ones thoughts, challenges to human identity and autonomy, societal disparities in access, and health implications.

Residual networks for text-independent speaker identification: Unleashing the power of residual learning

The human voice, a dynamic signal, conveys valuable information for speaker identification, encompassing gender, age, emotions, and language. In the biometrics industry, identifying voices in real-time amidst diverse accents, tones, and noisy backgrounds is a challenging task. Voice biometry, a complex aspect of speaker identification, is gaining importance in various applications, such as user authentication, attendance systems, forensics, and banking operations, as it eliminates the need for traditional credentials like cards or passwords. Recent advancements in Human–Computer Interaction technology have made conversational tasks technically feasible. Deep Neural Learning approaches, especially Convolutional Deep Neural Networks (CDNN), have emerged as a powerful tool in the field of speech processing, surpassing traditional Speaker Identification methods. This paper introduces a novel approach using 1-Dimensional Convolutional Residual Blocks for audio classification and Speaker Identification, specifically focusing on speaker recognition from spoken Hindi language. The proposed Residual architecture significantly enhances speaker identification, even in low Signal Noise Ratio environments, achieving an impressive accuracy rate of 86.02%. This outperforms traditional Gaussian Mixture Model (GMM) and Feed Forward Back-propagation Network (FFBN) model for the same set of speakers. Future research directions may explore the classification of audio and speaker identification using various acoustic features derived from speech signals.