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Usability—the extent to which a system can be operated to achieve specified goals with effectiveness, efficiency, and satisfaction—is a hallmark of many successful systems. Identifying usability concerns with existing measures, however, can be problematic. We use biased competition theory (BCT) to explain how interfaces with low usability create attentional interference, which can be measured through mouse movement efficiency (MME). We tested our hypotheses in four studies. Study 1 manipulates attentional interference and examines its influence on MME using eye tracking to validate BCT as an appropriate theoretical lens for our research. Study 2 manipulates usability and shows similar markers of attentional interference in MME on a website with two different populations. Study 3 is a field test that compares the reliability of MME in ranking the usability of different components of a commercial web application to rankings produced by a common perceptual measure of usability: perceived ease of use. Collaborating with a Fortune 100 software vendor, Study 4 further demonstrates the efficacy of MME in ranking the usability of various system components compared to a standard industry measure of usability across three different products. The results consistently show that MME can help identify usability differences among parts of systems. The results have implications for facilitating more efficient and temporally precise usability research and mass-deployable usability testing. [ABSTRACT FROM AUTHOR]

Customer care is important for its role in relationship building. This role has traditionally been performed by human customer agents; however, the emergence of interactive generative AI (GenAI) shows potential for using AI for customer care in emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this article develops an AI-enabled customer care journey, from accurate emotion recognition to empathetic response, emotional management support, and, finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth interviews with top managers and a survey of chief marketing officers. By juxtaposing these requirements against the current feeling capabilities of GenAI, the authors highlight the technological challenges engineers must tackle. The article concludes with a set of marketing tenets for implementing and researching the caring machine. These include verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to enhance GenAI's emotion understanding, employing "response engineering" to personalize emotion management recommendations, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value. [ABSTRACT FROM AUTHOR]

Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products. This research compares high-adaptivity algorithms, which can learn and adapt, versus low-adaptivity algorithms, which are entirely pre-programmed, and explore their impact on consumers' product preferences. Six empirical studies show that, in general, consumers prefer products with high-adaptivity algorithms. However, this preference depends on the desired level of product outcome range—the number of solutions a product is expected to provide within a task or across tasks. The findings also demonstrate that perceived algorithm creativity and predictability drive the observed effects. This research highlights the distinctive role of algorithm types in the perception of consumer goods and reveals the consequences of unveiling the mind of the machine to consumers. [ABSTRACT FROM AUTHOR]

Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers' work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition's relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations. [ABSTRACT FROM AUTHOR]

Due to their customizability, touchscreens continue to advance as a device of choice when designing aircraft cockpits. Previous studies investigated the effect of turbulence on human performance when using touchscreens, but few have evaluated its performance for realistic aviation-specific tasks. In this study, we compared four touchscreen widgets and three mechanical devices during realistic data input on a primary flight display (PFD). Twenty participants took part in the experiment at a constant level of vibration, while simultaneously completing a secondary tracking task. Results indicated that virtual keypads lead to faster completion time for medium to large changes while keeping error rates low. Rotary knobs were fastest for small changes. Virtual keypads also had lower workload and discomfort compared to rotary knobs and drag-based widgets. We found the completion time to be the most important factor in tracking task performance, which translated in higher precision for keypads. These findings suggest that virtual keypads represent an efficient and secure option for numerical data input at low-to-medium vibration. [ABSTRACT FROM AUTHOR]

AbstractIn the era of digitalization, it is critical to improve older adults’ digital literacy and inclusion by enhancing their technology adoption to create a more accessible and inclusive social environment. However, older adults always exhibit lower levels of technology usage, and there is insufficient support available to meet their specific needs. Gamification, with its potential to motivate people, presents a great opportunity to address the issue of technology adoption among older adults. Despite gamification being predominantly applied to younger generations, this study offers a unique contribution by examining the impact of gamification on supporting older adults’ technology adoption. A case study of tailor-made gamification for Google Maps was constructed based on the game-based learning model and game mechanics. The effectiveness of the proposed gamification strategy has been assessed through interviews and observation. The results revealed a significant positive effect of gamification on enhancing older adults’ technology knowledge and adoption intentions. [ABSTRACT FROM AUTHOR]

Gesture recognition technology has become a transformative solution to enhance accessibility for people with vision impairments. This innovation enables the interpretation of body and hand movements, transforming them into meaningful information or commands by applying advanced computer vision sensors and algorithms. This technology serves as an intuitive interface for the visually impaired, enabling them to access information seamlessly, navigate digital devices, and interact with their surroundings, fostering more independence and inclusivity in day-to-day activities. Gesture recognition solutions using deep learning (DL) leverage neural networks (NN) to understand intricate patterns in human gestures. DL algorithms can identify and classify different hand and body movements accurately by training on extensive datasets. Therefore, this study develops an automated gesture recognition using artificial rabbits optimization with deep learning (AGR-ARODL) technique for assisting visually challenged persons. The AGR-ARODL technique mainly intends to assist visually challenged people in the recognition of various kinds of hand gestures. In accomplishing this, the AGR-ARODL technique primarily pre-processes the input images using a median filtering (MF) approach. Next, the AGR-ARODL technique involves the SE-ResNet-50 model to derive feature patterns and its hyperparameter selection process is carried out by the use of the artificial rabbit optimization (ARO) algorithm. The AGR-ARODL technique applies a deep belief network (DBN) model for the detection of various hand gestures. The simulation results of the AGR-ARODL method are tested under the benchmark gesture recognition dataset. Widespread experimental analysis underscored the betterment of the AGR-ARODL technique compared to recent DL models. [ABSTRACT FROM AUTHOR]

AbstractFacial recognition technology (FRT) has become widespread in society, and its adoption in organizational settings is viewed as a natural continuation of established surveillance practices. However, concerns and resistance to FRT often arise due to controversial applications and improper use. This study contributes to the understanding of FRT in organizational contexts by conducting semi-structured interviews in a university that illustrates the larger themes of organizational justice and the ethical complexities surrounding the deployment of this technology. Drawing on organizational justice theory, we analyze users’ attitudes towards FRT deployment, their perceptions of organizational justice, and if and how the deployment adhered to or violated organizational justice principles. Our study revealed that this implementation case violated all aspects of organizational justice. Users expressed concerns regarding face capture, potential discrimination, authoritarian practices, and harm to community norms. We also provide recommendations to address organizational justice issues associated with implementing new, controversial technologies, considering technology acceptability perspectives. [ABSTRACT FROM AUTHOR]

Elderly and vision-impaired indoor activity monitoring uses sensor technology to monitor interaction and movement in the living area. This system can identify deviations from regular patterns, provide alerts, and ensure safety in case of emergencies or potential risks. These solutions enhance quality of life by promoting independent living while offering peace of mind to loved ones and caregivers. Visual impairments have become the chief part of these problems over the past few years. Usually, visually impaired people seek help from others to keep doing their daily tasks. An automated human activity recognition (HAR) technique could enhance a vision-impaired person’s transaction activity and safe movement. HAR using deep learning (DL) includes training neural networks to automatically detect and classify various activities carried out by humans based on sensor information. By leveraging DL approaches, the HAR system could accurately identify and classify activities, including sitting, standing, walking, or running, from the information captured by sensors or wearable devices. This system is integral to smart home automation, healthcare, and sports analytics. This study develops an Automated Fractal Quantum Salp Swarm Algorithm with Machine Learning based HAR (QSSA-MLHAR) for assisting elderly and visually impaired people. The QSSA-MLHAR technique uses sensory input data to identify human activities in diverse classes. In the QSSA-MLHAR technique, the min–max scalar is primarily used to scale the input data. Besides, the QSSA-MLHAR technique applies a mixed extreme learning machine (M-ELM) model to classify various human actions. The QSSA-based hyperparameter tuning process is used to improve the detection results of the M-ELM system. A wide range of simulations was performed to ensure the better performance of the QSSA-MLHAR method. The comprehensive outcomes assured that the QSSA-MLHAR technique performs well compared to other models. [ABSTRACT FROM AUTHOR]

Objective: Web-based platforms for delivering physical activity (PA) to people with developmental disabilities have a great potential to improve the lives of many. However, their design, including the content design, lacks sufficient investigation. This study aims to evaluate three online platforms for delivering PA to people with developmental disabilities in terms of content quality and identify relevant barriers and facilitators of PA delivery. Methods: The study used a methodological triangulation approach which involved quantitative evaluations by experts using an evaluation rubric, on-site observations and in-depth interviews with recruited participants. The participants consisted of 15 pairs of individuals, each consisting of a person with developmental disabilities and their primary caregiver. They were instructed to watch and follow five PA video content from each of the three platforms. The on-site observations and interviews were conducted in a large computer-equipped meeting room setting. Results: The quantitative and qualitative analysis identified a set of barriers and facilitators of PA delivery related to content quality. Key barriers identified include a lack of content diversity, insufficient understanding of developmental disabilities among content creators, inappropriate language usage, and resistance to engaging in physical activities in home settings. Significant facilitators were pinpointed, such as incorporating engaging elements for individuals with developmental disabilities, utilising easy-read language, and ensuring sufficient repetition for effective learning. The results from the triangulation showed that the multiple methods were complementary and converged on the same outcome. Discussion: The study findings could contribute to the development of adequately adapted PA content to distribute knowledge to populations with developmental disabilities. IMPLICATIONS FOR REHABILITATION: Content delivered via online platforms has the potential to convey knowledge about physical activity to a significant number of individuals with developmental disabilities without the limitations of time and space. The absence of prescribed content guidelines to effectively impart physical activity to individuals with developmental disabilities impedes the process of online knowledge translation. There is a requirement for varied physical activity content encompassing various individuals with developmental disabilities, considering their diverse learning contexts. It is essential that content development is evaluated with input from experts in developmental disabilities in order to provide quality physical activity information for people with developmental disabilities. [ABSTRACT FROM AUTHOR]

Background: Knowledge-to-action gap exists in delivering physical activity (PA) to people with developmental disabilities via online platforms. Although web-based platforms have great potential in facilitating the delivery of PA for this target group, the lack of knowledge regarding web accessibility poses a challenge in accessing PA-related information online. Objective: This study evaluates the delivery of PA in terms of web accessibility. It also aims to identify barriers and facilitators in delivering PA knowledge to people with developmental disabilities online to improve web accessibility for the target user group. Methods: The study employs a concurrent nested design incorporating both quantitative (web usability questionnaire) and qualitative data (in-depth interviews). Fifteen pairs of individuals consisting of a person with developmental disabilities and a primary caregiver participated in the study, and three web-based platforms were selected for web accessibility tests and in-depth interviews. Results: The nested analysis provides a quantitative comparison of web accessibility and identifies barriers and facilitators of delivering PA for the target user group from the web accessibility perspective. Conclusion: The study findings could inform the development of accessible online platforms that distribute health-related knowledge to populations with developmental disabilities. Additionally, they could help enhance the design of other platforms intended for these populations. IMPLICATIONS FOR REHABILITATION: Online platforms have significant potential to improve the delivery of physical activity information to individuals with developmental disabilities. Barriers to accessing online platforms due to poor web usability impede the process of online knowledge translation to end-users. Web usability can be enhanced by implementing appropriate interventions, such as restructuring navigation and redesigning the user interface. Improving web usability will enhance the accessibility of physical activity knowledge for individuals with developmental disabilities, which consequently will positively impact their health. [ABSTRACT FROM AUTHOR]

The use of haptic simulation for emergency procedures in nursing training presents a viable, versatile and affordable alternative to traditional mannequin environments. In this paper, an evaluation is performed in a virtual environment with a head-mounted display and haptic devices, and also with a mannequin. We focus on a chest decompression, a life-saving invasive procedure used for trauma-associated cardiopulmonary resuscitation (and other causes) that every emergency physician and/or nurse needs to master. Participants' heart rate and blood pressure were monitored to measure their stress level. In addition, the NASA Task Load Index questionnaire was used. The results show the approved usability of the VR environment and that it provides a higher level of immersion compared to the mannequin, with no statistically significant difference in terms of cognitive load, although the use of VR is perceived as a more difficult task. We can conclude that the use of haptic-enabled virtual reality simulators has the potential to provide an experience as stressful as the real one while training in a safe and controlled environment. [ABSTRACT FROM AUTHOR]

Object manipulation is a fundamental interaction in virtual reality (VR). Efficient and accurate manipulation is important for many VR applications, especially collaborative VR applications. We introduce a collaborative method based on the manipulation guidance field (MGF) to improve manipulation accuracy and efficiency. MGF aims to guide users of different manipulation types to different manipulation viewpoints to efficiently and collaboratively manipulate objects. First, we introduce the concept of MGF and its construction method. Two strategies are offered to accelerate the MGF updating process. A collaborative manipulation method to manipulate objects using the guidance of MGF is then proposed. Finally, a user study (n = 36 participants) was conducted to evaluate the efficiency and accuracy of our MGF-based collaborative object manipulation method in three scenes: (1) Livingroom scene; (2) WaveHouse scene; (3) Pipe scene. Compared to a control method without MGF, the results show that our MGF-based method has significantly reduced task completion time, position error, rotation error, and task load. [ABSTRACT FROM AUTHOR]

With the increasing popularity of the Metaverse concept, human beings have stepped to a new height in the intelligent technology progress. This work presents a literature review of the current research on Human-Computer Interaction in the Metaverse. Keywords like "Metaverse," "Human-Computer Interaction (HCI)," "virtual space," "virtual technology," "three-dimensional (3D) reconstruction," "parallel universe," "independent identity," "access to interests," and "blockchain" are used to search for relevant articles in the literature in databases like Scopus, Web of Science, and Google Academic. Nearly 100 advanced studies on the Metaverse are selected from over 20,000 documents covering 2018-2023. Finally, the current application state of the Metaverse's underlying technologies is explored and described using the PRISMA principles; these technologies include the fifth-generation (5 G) communication, HCI, virtual technology, blockchain, and 3D reconstruction. Moreover, the predictions for HCI's future growth in the Metaverse are made. The review concludes that the rapid advancement of the 5 G connection has made the concept of the Metaverse possible. The blockchain ensures the safety of currency transactions in the Metaverse virtual space. There will be a shift toward "invisibility" in the way that humans and computers interact in virtual worlds. In other words, HCI will be transparent to the user in the digital realm, and people will get along with the computer in a natural and equitable way. In the interaction, wearable devices allow for an immersive experience in the interaction, but they restrict the participants' freedom of movement and perception. The more humanized somatosensory connection will gain traction in the future, bringing people one step closer to the Metaverse. [ABSTRACT FROM AUTHOR]

This research paper investigates the intersection of the maker movement and educational innovation, using the China-U.S. Young Maker Competition as a foundational example. It examines how maker education, fueled by hands-on learning and a curiosity-driven approach, can evolve and influence. The study explores the roles and impacts of Human-Computer Interaction (HCI), Design-Driven Innovation (DDI), and Cross-Cultural Perspectives (CCP) within maker education. It highlights HCI's connection of technology with learning, DDI's focus on user-centered solutions, and the significance of CCP in enhancing cultural collaboration, vital for fostering an innovative and creative future. This paper offers a detailed perspective on the current state and future potential of maker education. It proposes a roadmap for the coming decade, emphasizing collaborative learning and creative endeavors, all set within the engaging environment of the competition. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to dramatic changes in people's lives. The Human-Computer Interaction (HCI) community widely investigated technology use during crises. However, commercial video games received minor attention. In this article, we describe how video game play impacted the life transformations engendered by the pandemic. We administered a qualitative online survey to 330 video game players who were living in Italy during the lockdown measures. We found that the COVID-19 pandemic altered the participants' sense of time and space, reshaped both their intimate and wider social interactions, and elicited a wide spectrum of disturbing emotions. Players escaped from this unsatisfying reality into video game worlds, searching for a new normality that could compensate for the unpredictability and dangerousness of the pandemic life, as well as seeking uncertainty in the game environments to balance the flatness of the lockdown everydayness. In doing so, they "appropriated" the gaming technologies, which also led to several unexpected outcomes. Starting from these findings, we propose a model of escapism that points out four ways to escape from reality into video game worlds. Moreover, we outline some design implications that might inspire future strands of research in the field of crisis technologies. [ABSTRACT FROM AUTHOR]

Featured Application: The Trail Making Test in Virtual Reality (TMT-VR) provides a highly adaptable and immersive tool for cognitive and neuropsychological assessments. Its application extends beyond traditional clinical settings, offering potential use in remote assessments, telemedicine, and longitudinal studies that require repeated measures. The fully automated, randomized, and naturalistic environment of the TMT-VR ensures precise and consistent evaluation across diverse populations, making it particularly useful for assessing cognitive functions in older adults, individuals with neurological conditions, and populations with limited technological exposure. Additionally, the TMT-VR could serve as a valuable tool in cognitive rehabilitation programs, where immersive and engaging tasks are crucial for patient motivation and adherence. However, validation studies are required for scrutinizing its utility in older adults, clinical populations, and settings. Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18–35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes as follows: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. The TMT-VR demonstrates high usability, acceptability, and user experience among participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations. [ABSTRACT FROM AUTHOR]

This paper addresses the notable gap in evaluating eXplainable Artificial Intelligence (XAI) methods for text classification. While existing frameworks focus on assessing XAI in areas such as recommender systems and visual analytics, a comprehensive evaluation is missing. Our study surveys and categorises recent post hoc XAI methods according to their scope of explanation and output format. We then conduct a systematic evaluation, assessing the effectiveness of these methods across varying scopes and levels of output granularity using a combination of objective metrics and user studies. Key findings reveal that feature-based explanations exhibit higher fidelity than rule-based ones. While global explanations are perceived as more satisfying and trustworthy, they are less practical than local explanations. These insights enhance understanding of XAI in text classification and offer valuable guidance for developing effective XAI systems, enabling users to evaluate each explainer's pros and cons and select the most suitable one for their needs. [ABSTRACT FROM AUTHOR]

Mechanical keyboards are one of the most used keyboards in daily work, and as the core component of a mechanical keyboard, keyswitches' characteristics have been widely researched during the last several decades. Meanwhile, keyboards' backlight design has become an emerging market trend, even though its actual influence on user experience remains unknown. Our study investigated the influence of keyboard switch characteristics and backlight states on typing performance and subjective typing experience. Thirty-three participants were involved in an English-language typing task on keyboards with four typical switches (red, black, brown, and blue types). Software was used to automatically record typing speed and accuracy, and a 7-point Likert subjective scale was used to evaluate typing experience. Results showed that tactile characteristics performed better than linear switches in typing speed, accuracy, and subjective refresh feeling in pair comparison of blue and red switches, and turning on the backlight effect improved typing speed, refreshing feeling, and pleasure. Among six physical characteristics of switches measured in the experiment, pre travel, reset travel, make force, and bottom force contributed more to better typing performance and subjective experience. Our findings provide keyboard manufacturers and researchers with a reference to optimize users' typing experience. [ABSTRACT FROM AUTHOR]

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable. [ABSTRACT FROM AUTHOR]

Upper limb rehabilitation robots have important practical significance in helping patients recover their motor function, but traditional training methods often lack real-time and accurate evaluation of patient movement status and intention and have low interactive participation. Therefore, the study proposes a training system for upper limb rehabilitation robots based on multi-sensor trajectory data and human-computer interaction. The system is designed from three aspects: inertial sensor trajectory tracking, Kinect sensor image restoration, and interaction system design. A polynomial joint zero value constraint algorithm is introduced to correct errors, and variable parameter pixel filtering combined with a weighted average moving algorithm is used to improve image quality. The training results showed that the robot trajectory tracking accuracy significantly improved, and the improvement in motion trajectory error was greater than 20%. Compared with traditional training methods, interactive training had a recognition accuracy of over 85% in rehabilitation actions, and the system had better stability. This rehabilitation robot can effectively meet the feasibility and effectiveness of improving the interaction system, providing new technological means for intelligent medical services. [ABSTRACT FROM AUTHOR]

Purpose: An AI concierge is a technologically advanced, intelligent and personalized assistant that is designated to an individual customer, proactively taking care of that customer's needs throughout the service journey. This article envisions the idea of AI concierges and discusses how to leverage AI concierges in the customer journey. Design/methodology/approach: This article takes a conceptual approach and draws insights from literature in service management, marketing, psychology, human-computer interaction and ethics. Findings: This article delineates the fundamental forms of AI concierges: dialog interface (no embodiment), virtual avatar (embodiment in the virtual world), holographic projection (projection in the physical world) and tangible service robot (embodiment in the physical world). Key attributes of AI concierges are the ability to exhibit semantic understanding of auditory and visual inputs, maintain an emotional connection with the customer, demonstrate proactivity in refining the customer's experience and ensure omnipresence through continuous availability in various forms to attend to service throughout the customer journey. Furthermore, the article explores the multifaceted roles that AI concierges can play across the pre-encounter, encounter and post-encounter stages of the customer journey and explores the opportunities and challenges associated with AI concierges. Practical implications: This paper provides insights for professionals in hospitality, retail, travel, and healthcare on leveraging AI concierges to enhance the customer experience. By broadening AI concierge services, organizations can deliver personalized assistance and refined services across the entire customer journey. Originality/value: This article is the first to introduce the concept of the AI concierge. It offers a novel perspective by defining AI concierges' fundamental forms, key attributes and exploring their diverse roles in the customer journey. Additionally, it lays out a research agenda aimed at further advancing this domain. [ABSTRACT FROM AUTHOR]

Neurotechnology and Artificial Intelligence (AI) have developed rapidly in recent years with an increasing number of applications and AI-enabled devices that are about to enter the market. While promising to substantially improve quality of life across various severe medical conditions, there are also concerns that the convergence of these technologies, e.g., in the form of intelligent neuroprostheses, may have undesirable consequences and compromise cognitive liberty, mental integrity, or mental privacy. Therefore, various international organizations, such as the Organization for Economic Cooperation and Development (OECD) or United Nations Educational, Scientific and Cultural Organization (UNESCO), have formed initiatives to tackle such questions and develop recommendations that mitigate risks while fostering innovation. In this context, a first international conference on the ethics and regulation of intelligent neuroprostheses was held in Berlin, Germany, in autumn 2023. The conference gathered leading experts in neuroscience, engineering, ethics, law, philosophy as well as representatives of industry, policy making and the media. Here, we summarize the highlights of the conference, underline the areas in which a broad consensus was found among participants, and provide an outlook on future challenges in development, deployment, and regulation of intelligent neuroprostheses. [ABSTRACT FROM AUTHOR]

This article provides an overview of recent progress in the achievement of non-contact intraoperative image control through the use of vision and sensor technologies in operating room (OR) environments. A discussion of approaches to improving and optimizing associated technologies is also provided, together with a survey of important challenges and directions for future development aimed at improving the use of non-contact intraoperative image access systems. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is significantly influenced by the cultural background (CB) of observers and the masking conditions of the target face. This study aimed to clarify these factors' impact on FER, particularly in machine-learning datasets, increasingly used in human-computer interaction and automated systems. We conducted an FER experiment with East Asian participants and compared the results with the FERPlus dataset, evaluated by Western raters. Our novel analysis approach focused on variability between images and participants within a "majority" category and the eye-opening rate of target faces, providing a deeper understanding of FER processes. Notable findings were differences in "fear" perception between East Asians and Westerners, with East Asians more likely to interpret "fear" as "surprise." Masking conditions significantly affected emotion categorization, with "fear" perceived by East Asians for non-masked faces interpreted as "surprise" for masked faces. Then, the emotion labels were perceived as different emotions across categories in the masking condition, rather than simply lower recognition rates or confusion as in existing studies. Additionally, "sadness" perceived by Westerners was often interpreted as "disgust" by East Asians. These results suggest that one-to-one network learning models, commonly trained using majority labels, might overlook important minority response information, potentially leading to biases in automated FER systems. In conclusion, FER dataset characteristics differ depending on the target face's masking condition and the diversity among evaluation groups. This study highlights the need to consider these factors in machine-learning-based FER that relies on human-judged labels, to contribute to the development of more nuanced and fair automated FER systems. Our findings emphasize the novelty of our approach compared to existing studies and the importance of incorporating a broader range of human variability in FER research, setting the stage for future evaluations of machine learning classifiers on similar data. [ABSTRACT FROM AUTHOR]

As artificial intelligence technology continues to evolve, numerous advancements enable smoother communication and collaboration between humans and computers. Natural language processing (NLP), as a key direction in the field of computer and artificial intelligence, has been attracting much attention. It focuses on the language used by people in daily life, devoting itself to improving the ability of language generation and understanding, thus making communication between people and computers more fluent and natural, effectively breaking through the problem of human-computer interaction. With the advancement of deep learning technology, the migration of NLP models onto deep learning platforms has emerged as a key trend. Model transfer, a crucial aspect of deep learning, holds significant value in enhancing the performance and efficiency of NLP models. This paper begins by outlining the fundamentals of deep learning platforms and NLP model transfer, followed by a comprehensive examination of current research progress and challenges in this field. Subsequently, we introduce a novel NLP model transfer strategy tailored for deep learning platforms and validate its effectiveness through rigorous experiments. In conclusion, the paper highlights our noteworthy advancements, pointing toward promising future developments. [ABSTRACT FROM AUTHOR]

Introduction: EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging. Methods: In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects. Results: Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% ± 1.25% (SEED), 98.93% ± 1.45% (DEAP-arousal), and 98.57% ± 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology. Discussion: The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition. [ABSTRACT FROM AUTHOR]

The advent of Generative Artificial Intelligence (GenAI) models, such as Stable Diffusion, Open AI's DALL-E2, and MidJourney, has opened the door to a huge range of new possibilities in architectural research and practice. Before architects and researchers can fully leverage these models, it is crucial to assess their proficiency in generating images that accurately depict real spaces and ambiances. Here, we assessed the proficiency of DALL-E2 in depicting intended spaces and intended ambiances, using real photographs from Google Images as a comparative benchmark. Images of eighteen distinct home spaces, each intended to evoke three different ambiances, were generated and presented to human observers. The images were evaluated in terms of their perceived realism, their accuracy in depicting the space and ambiance intended. Findings highlight GenAI's capability in depicting spaces and point to its potential in convincingly conveying ambiances. Beyond the substantive findings, this research highlights the importance of evaluating the rapidly emerging tools enabled by recent developments in AI. [ABSTRACT FROM AUTHOR]

The integrated "perception‐memory" system is receiving increasing attention due to its crucial applications in humanoid robots, as well as in the simulation of the human retina and brain. Here, a Field Programmable Gate Array (FPGA) platform‐boosted system that enables the sensing, recognition, and memory for human‐computer interaction is reported by the combination of ultra‐thin Ag/Al/Paster‐based electronic tattoos (AAP) and Tantalum Oxide/Indium Gallium Zinc Oxide (Ta2O5/IGZO)‐based memristors. Notably, the AAP demonstrates exceptional capabilities in accommodating the strain caused by skin deformation, thanks to its unique structural design, which ensures a secure fit to the skin and enables the prolonged monitoring of physiological signals. By utilizing Ta2O5/IGZO as the functional layer, a high switching ratio is conferred to the memristor, and an integrated system for sensing, distinguishing, storing, and controlling the machine hand of multiple human physiological signals is constructed together with the AAP. Further, the proposed system implements emergency calls and smart homes using facial electromyogram signals and utilizing logical entailment to realize the control of the music interface. This innovative "perception‐memory" integrated system not only serves the disabled, enhancing human‐computer interaction but also provides an alternative avenue to enhance the quality of life and autonomy of individuals with disabilities. [ABSTRACT FROM AUTHOR]

AbstractCollaborative Augmented Reality (CAR) involves multiple users working together in an augmented reality setting to accomplish tasks. In CAR, transparent techniques enable users to see occluded objects in complex environments, yet they often struggle to discern which part of a complex object they have selected. Workspace awareness refers to understanding how others interact within a shared workspace and influence the choice of visualization techniques. This study investigated workspace awareness regarding different object selection visual feedback in co-located CAR. It explored four technique variations: Hi (highlight), Hi-Ray (ray emitting), Hi-View (view switching), and Hi-RV (ray emitting and view switching). Thirty-two participants were divided into 16 pairs, with an instructor demonstrating how to assemble an engine component and a student replicating the process using the four visualization techniques. Results showed the visualization techniques did not affect task completion time significantly. The Hi and Hi-Ray technique had the highest error rate, while the Hi-RV technique had the lowest. However, users preferred the Hi-View technique, which scored highest in interaction, collaboration quality, workspace awareness, and required less workload. To enhance awareness, participants pointed out the weakness in the combination of visual feedback, particularly with the ray emitting technique, which causes confusion when multiple objects are selected due to visual clutter. They suggested that integrating audio feedback could help alert users to their choices, and using color-based indicators could distinguish different actions, such as indicating correct or incorrect placement of virtual objects. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence (AI) technology has been widely employed in brand customer service. However, the inherent limitations of computer-generated natural language content occasionally lead to failures in human-computer interactions, potentially damaging a company's brand image. Therefore, it is crucial to explore how to maintain consumer trust after AI chatbots fail to provide successful service. This study constructs a model to examine the impact of social interaction cues and anthropomorphic factors on users' sustained trust by integrating the Computers As Social Actors (CASA) theory with attribution theory. An empirical analysis of 462 survey responses reveals that CASA factors (perceived anthropomorphic characteristics, perceived empathic abilities, and perceived interaction quality) can effectively enhance user trust in AI customer service following interaction failures. This process of sustaining trust is mediated through different attributions of failure. Furthermore, AI anxiety, as a cognitive characteristic of users, not only negatively impacts sustained trust but also significantly moderates the effect of internal attributions on sustained trust. These findings expand the research domain of human-computer interaction and provide insights for the practical development of AI chatbots in communication and customer service fields. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG)-based Brain-Computer Interface (BCI) systems for emotion recognition have the potential to assist the enrichment of human–computer interaction with implicit information since they can enable understanding of the cognitive and emotional activities of humans. Therefore, these systems have become an important research topic today. This study aims to present trends and gaps on this topic by performing a systematic literature review based on the 216 published scientific literature gathered from various databases including ACM, IEEE Xplore, PubMed, Science Direct, and Web of Science from 2016 to 2020. This review gives an overview of all the components of EEG based BCI system from the signal stimulus module which includes the employed device, signal stimuli, and data processing modality, to the signal processing module which includes signal acquisition, pre-processing, feature extraction, feature selection, classification algorithms, and performance evaluation. Thus, this study provides an overview of all components of an EEG-based BCI system for emotion recognition and examines the available evidence in a clear, concise, and systematic way. In addition, the findings are aimed to inform researchers about the issues on what are research trends and the gaps in this field and guide them in their research directions. [ABSTRACT FROM AUTHOR]

With the development of laser scanners and machine learning, point cloud semantic segmentation plays a significant role in autonomous driving, scene reconstruction, human-computer interaction, and other fields. In recent years, point cloud semantic segmentation based on deep learning has become one of the key research directions in point cloud processing. Due to the limited ability to exploit geometric details and contextual information in point clouds, most methods that adopt encoder-decoder architecture lose local structural information easily, especially detailed features, and extract features insufficiently. To address this issue, the edge-preserving inception DenseGCN U-Net (entitled as EIDU-Net) is proposed. EIDU-Net makes full use of the complementation between geometric details in the original point cloud and high-level features. The edge-preserved graph pooling (EGP) layer, the key module of the EIDU-Net, is designed to retain additional edge feature information from the original point cloud during pooling operations. Accordingly, the edge-preserved graph unpooling (EGU) layer can restore the feature graph more efficiently based on the additionally retained edge features. Extensive experiments demonstrate that our proposed EIDU-Net has remarkable improvements on semantic segmentation tasks under whatever S3DIS or Terracotta Warrior fragments. Our code is publicly available at https://github.com/caoxin918/EIDU-Net. [ABSTRACT FROM AUTHOR]

AbstractThe iteration of AI technology has enhanced the industry competitiveness of sports apps and brought a new emotional experience to users. However, the community function of AI-enabled sports apps has not yet received sufficient attention. This study adopts the technology acceptance model framework through the human-computer interaction and social psychology perspectives, incorporating perceived interactivity (human-human interaction, human-information interaction, human-system interaction), user experience (functional experience, content experience, and emotional experience), attachment theory (emotional attachment, emotional loyalty), social identity, social trust, and satisfaction variables to construct a theoretical model. It aims to solve the problem of how AI can enhance users’ emotional interaction, community stickiness, and ultimately social interaction satisfaction in sports apps. Through literature review, theoretical analysis, and case study, the Chinese “KEEP” APP was identified as the research object, and the questionnaire was constructed with an improved test scale. The study used a cross-sectional sampling method to collect data from Chinese online platforms for three months (N = 520) and tested the hypotheses in the theoretical model through structural equation modeling and artificial neural networks. The results of the study show that (1) emotional attachment, emotional loyalty, social identity, and social trust positively and significantly affect user satisfaction; (2) emotional attachment and social identity positively and significantly affect human-human interaction; (3) perceived usefulness, social identity, and social trust significantly construct an influence loop on the internal path. Compared with existing studies, this study proposes a multidimensional innovative framework that reveals the deeper influence mechanism of AI technology in sports APP communities and makes up for the shortcomings of existing studies in terms of users’ emotional interactions and community stickiness. Through this study, sports APP developers and operators, marketers, AI technology developers, as well as sports enthusiasts, and general users can obtain specific practical suggestions to further optimize the design and functionality of sports apps, and enhance the overall user experience and satisfaction. [ABSTRACT FROM AUTHOR]

Problematic smartphone use (PSU) is a growing concern that hurts users' daily lives and activities. Previous studies have emphasised the importance of developing mindfulness and self-efficacy concerning smartphone use rather than solely focussing on reducing usage. However, there has been little research developing and evaluating digital interventions specifically targeting absent-minded smartphone use. There is also little knowledge on how best mindfulness practice using digital technologies can be integrated with persuasive designs which have been widely studied and implemented for behaviour change. In this paper, we developed a live wallpaper application for Android lock and home screens as a mindfulness-based intervention for absent-minded smartphone use. The application was evaluated over two weeks with 121 participants. This was followed by a semi-structured interview with 15 participants. The results of our analysis show that the intervention reduced absent-minded smartphone use overall. While participants found the various features (especially the customisation features) of the intervention to be persuasive, we found no correlation between perceived persuasiveness and behaviour change. This work provides valuable insights for advancing human-computer interaction (HCI) research on PSU. Additionally, the results raise important questions for future research, such as the relationship between perceived persuasiveness and behaviour change. Finally, we contribute to the discussion on the applications of mindfulness in persuasive technology, challenges and future research areas. [ABSTRACT FROM AUTHOR]

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from "0" to "9". These results show the potential of the capacitive sensor array for advanced human–computer interaction applications. [ABSTRACT FROM AUTHOR]

AbstractHead-up displays (HUDs) enable drivers to receive additional information while maintaining a forward view of the road. However, due to human cognitive resource limitations, improper HUD interface design can pose safety risks, especially when multiple pieces of information are presented simultaneously. This study involved 45 participants and conducted two driving simulation experiments. Study 1 explored the impact of different HUD interface positions on driver cognitive performance in single cognitive task conditions. Study 2 validated the findings of Study 1 under multiple cognitive task conditions and further compared drivers’ cognitive performance, workload, driving operation, and eye movement between grouped and disordered information layouts on the HUD. The results indicated that the central area of the HUD interface provided the best cognitive performance, followed by the left side, with the right side performing the poorest. Grouped information layouts on the HUD proved superior to disordered layouts in terms of cognitive performance, workload, driving operation, and eye movement. An XGBoost regression model was proposed to predict the drivers’ comprehensive cognitive responses. This study could provide empirical evidence and practical recommendations for HUD interface design. [ABSTRACT FROM AUTHOR]

At a time when diverse forms of automated visual feedback systems on speech are introduced to the Human–Computer Interaction (HCI) community, our research investigates the possible design spaces and design implications to consider when creating them. We utilised a Wizard of Oz prototype that recognises fillers during speech and sends out real-time feedback in the form of a flickering screen and a post-report with graphic charts. We let 27 participants use this prototype and interviewed them about their general experience of using such systems. Based on thematic analysis, we propose eight design implications to consider when designing an automated visual feedback system on speech. Additionally, we discuss the possible future for automated visual feedback systems on speech where they collaborate with fields such as personal informatics (quantified self), self-determination theory (SDT) and motivation, and other socio-ethnic areas [ABSTRACT FROM AUTHOR]

With the rapid increase of high-speed train driving automation, the human-computer interaction of high-speed train driving is changing. It is a major dilemma to maintain a well-interactive process between the driver and the driving system for the intelligent train operation. Nevertheless, the enhancement of the driver's situation awareness (SA) is an effective way to promote the process of driver-driving system interaction. To achieve this purpose, we proposed an enhanced SA model based on the perceptual cycle model (PCM). The model helps to identify the high-speed train DMI interface elements that affect the different SA level of drivers and provides theoretical support for describing the SA change pattern of driving tasks. We proposed a new display mode (ESA mode) for enhancing driver SA according to this model, and conducted driving simulator experiments to explore the differences of SA change patterns between the ESA mode and the normal mode (NSA mode) for 16 subjects. The experimental results showed that the ESA mode successfully overcame the subjects' SA recession in the NSA mode. The interaction with enhanced SA can help the driver remain vigilant and further improve the general driving performance during monotonous process of automated high-speed train driving. [ABSTRACT FROM AUTHOR]

With increasing interest in computer‐assisted educa‐ tion, AI‐integrated systems become highly applicable with their ability to adapt based on user interactions. In this context, this paper focuses on understanding and analysing first‐year undergraduate student responses to an intelligent educational system that applies multi‐agent reinforcement learning as an AI tutor. With human–computer interaction at the centre, we discuss principles of interface design and educational gamification in the context of multiple years of student observations, student feedback surveys and focus group interviews. We show positive feedback from the design methodology we discuss as well as the overall process of providing automated tutoring in a gamified virtual environment. We also discuss students' thinking in the context of gamified educational systems, as well as unexpected issues that may arise when implementing such systems. Ultimately, our design iterations and analysis both offer new insights for practical implementation of computer‐assisted educational systems, focusing on how AI can augment, rather than replace, human intelligence in the classroom. Practitioner notes What is already known about this topic AI‐integrated systems show promise for personalizing learning and improving student education. Existing research has shown the value of personalized learner feedback. Engaged students learn more effectively. What this paper adds Student opinions of and responses to an HCI‐based personalized educational system. New insights for practical implementation of AI‐integrated educational systems informed by years of student observations and system improvements. Qualitative insights into system design to improve human–computer interaction in educational systems. Implications for practice and/or policy Actionable design principles for computer‐assisted tutoring systems derived from first‐hand student feedback and observations. Encourage new directions for human–computer interaction in educational systems. What is already known about this topic AI‐integrated systems show promise for personalizing learning and improving student education. Existing research has shown the value of personalized learner feedback. Engaged students learn more effectively. What this paper adds Student opinions of and responses to an HCI‐based personalized educational system. New insights for practical implementation of AI‐integrated educational systems informed by years of student observations and system improvements. Qualitative insights into system design to improve human–computer interaction in educational systems. Implications for practice and/or policy Actionable design principles for computer‐assisted tutoring systems derived from first‐hand student feedback and observations. Encourage new directions for human–computer interaction in educational systems. [ABSTRACT FROM AUTHOR]