HCI International 2025 Posters

As cybersecurity threats become increasingly complex, frequent, and targeted, educating users (who are often viewed as “the weakest link” in the system) to increase their awareness of security and privacy is becoming more critical than ever. However, the effectiveness of the “one-size-fits-all”approach in existing awareness programs to educate people about security and privacy may be limited, as it often overlooks individuals’ unique needs, prior knowledge of specific topics, real-life security behaviors, and preferred learning and delivery methods. Recent advancements in AI tools present opportunities to create personalized content tailored to users’ unique needs and preferences. To explore this potential, we developed a web application that leverages AI tools to offer personalized cybersecurity education. The system first assesses users’ security and privacy knowledge and behaviors, classifies them into categories (e.g., beginner, intermediate, advanced), and then presents AI-generated content in their preferred format (e.g., text, video). While the project is a proof-of-concept, the methods/approaches used in this work can help both academia and industry develop more effective, scalable, and adaptive cybersecurity trainings by integrating AI tools.

The research explores the influence of Human-Centered Artificial Intelligence on the usage intention of AI agents. Agentic AI refers to autonomous systems that are capable of pursuing complex objectives and delivering outcomes with minimal human intervention. The researchers focused particularly on the impact of security, privacy, and ethical risks on the intention to use AI agents, specifically in the context of high-involvement AI-driven products. The study adopts a 2 (high automation vs. low automation) × 2 (high personal control vs. low personal control) between-subjects factorial design, with a central focus on the application of AI agents in a Threat Intelligence System.Preliminary results indicate that personal control serves as a mediating factor in the relationship between automation and usage intent. Notably, the findings suggest that a combination of high (versus low) automation and high (versus low) personal control enhances the intention to adopt AI agents in high-involvement AI products, such as Threat Intelligence Systems. These insights underscore the critical balance between automation and user agency, highlighting the importance of designing AI systems that empower users while addressing concerns related to security, privacy, and ethical considerations.

Privacy nutrition labels have emerged as a compelling alternative to lengthy, complex privacy policies for effectively communicating privacy information. In recent years, research on privacy nutrition labels has expanded significantly. However, a literature review of privacy nutrition labels is still lacking. To address this gap, we created and analyzed a dataset of privacy nutrition label papers from 2009 to 2024. We qualitatively coded the papers published in the past 15 years, and revealed characteristics of existing privacy labels research. Our findings highlight areas that have received more attention and those that remain under-served. Our analysis also shed light on common methodologies in existing studies and the different communities of stakeholders. We conclude by reflecting on the gaps in existing research and discussing where future work can focus on.

The protection of trade secrets by data trustees is critical in a time of rapid digitalization and evolving data-sharing practices. Data trustees act as intermediaries managing sensitive information on behalf of data providers, requiring robust technical and legal safeguards to ensure compliance with the German Act on the Protection of Trade Secrets (GeschGehG) and the General Data Protection Regulation (GDPR). This paper examines the legal and technical requirements for trade secret protection and presents our ongoing research, which includes requirement analysis and some first steps regarding the implementation. The GeschGehG, which is aligned with the EU Trade Secrets Directive, defines trade secrets and prescribes appropriate confidentiality measures to ensure their protection. Under Sect. 2(1)(b) of the GeschGehG, failure to implement such measures may result in losing trade-secret protection. Similarly, GDPR introduces stringent requirements for handling personal data, creating complexities for data trustees navigating both regulations. The legal precedence emphasizes a risk-based approach, which requires comprehensive evaluations of potential vulnerabilities and tailored protective measures. From a technical perspective, effective trade secret protection involves clear identification of data users, adherence to the need-to-know principle, automated filtering to exclude sensitive information from shared datasets, and robust security measures to prevent unauthorized access. Legal safeguards, such as contractual obligations and explicit warning systems, further mitigate risks. Our research focuses on implementing these requirements within the framework of an access-type data trustee, where data sharing occurs only under explicit user instructions. By integrating insights from GDPR and GeschGehG, we aim to develop a technically viable and legally compliant system that ensures trade secrets are effectively safeguarded, fostering trust and integrity in data-sharing practices as the digital landscape evolves.

A modeling language is used to analyze the complex and changing system of a major corporation targeted by an advanced persistent threat. The analysis reveals ways that the response to the attack could have been better addressed. This work serves both as an example for improving responses to sophisticated cyberattacks and a demonstration of how the use of a specific modeling language can aid in understanding large, complex, time-varying systems. The modeling language is designed to use the same language to discuss all aspects of such a system, whether technological, social, informational, organizational, policy, or something else. Further, the language intrinsically considers the time-varying nature of these aspects.

In the digital data era, empowering users to manage their personal data has become a cornerstone of fostering trust in media organizations. This experimental study investigates the multidimensional construct of trust in a Flemish public media organization, its online streaming service, and the embedded privacy dashboard of the streaming service. Privacy dashboards represent a user-centric innovation designed to enhance transparency and control, addressing growing concerns about the opaque handling of personal data by digital platforms. Our findings reveal high trust levels in both the media organization and its online streaming service, while the privacy dashboard, though positively received, exhibits lower trust due to limited familiarity. Statistical analyses show a transfer of trust from the established platforms to the dashboard, indicating its potential as a trust-building tool. Additionally, individual factors like digital literacy and privacy concerns significantly shape users’ trust and their attitudes. These results underscore the critical role of human-data interactions in the adoption of privacy-enhancing technologies. Privacy dashboards can empower users by enhancing transparency around data practices, building trust in online media services, and giving individuals greater control over their personal information. For privacy tools to succeed, media organizations must integrate them seamlessly into user experiences while promoting digital literacy and addressing privacy concerns.

Financial inclusion is vital for socio-economic development, yet the marginalized struggle to access secure banking services. Despite advances like digital payments, many still rely on insecure methods, such as cash withdrawal at cyber cafes, increasing vulnerability to fraud. To address this, we developed an ATM training application using Augmented Reality (AR) to provide realistic, hands-on experience with virtual ATMs, promoting self-reliance. Logistic regression analysis on 22 participants for AR-based ATMs revealed that 68% were likely to use real-world ATMs, with 80% median confidence and 82% analysis accuracy for the entire dataset. 85% of the participants were 55–65, while 20% were 70–80, contributing to confidence variation with a standard deviation of 34.8%. For another 10 participants, a Repeated Measures ANOVA for the time taken and ATM confidence pre- and post-training yielded validity of significant influence of training with 99.05% (p-value of 0.95) significance for transaction time and a high percentage (zero p-value) for ATM confidence. The TAM results revealed perceived usefulness scores ranging from 5.5 to 6.13, showcasing the system as applicable with moderate variability, and perceived ease of use scores ranging from 5.625 to 6.125, with a relatively positive response towards ease of use, with less variability. The results show that AR-based training applications can address technical barriers to self-service infrastructure, enhancing user confidence in practical skills for real-world interactions.

Most traditional cryptosystems assume that secret information, such as decryption and signing keys, is kept secure from attackers and that the machine executing the algorithm is trustworthy. In reality, however, security is not always guaranteed, as machines may be infected with malware that leaks secret information or executes computations differently from the intended algorithms. To address this, Ogata et al. introduced the concept of a “server-aided in-brain signature,” a cryptographic protocol to sign a message securely even if the user’s device and servers are not completely trusted, and its concrete scheme that combines a one-time pad and multi-party computation (MPC). In this study, we first design the user interface for the Ogata et al.’s in-brain signature scheme using two terminals: a laptop and a smartphone. In this scheme, the signer must process characters in their brain using a one-time pad, raising concerns about its feasibility. To facilitate this process, we introduce an addition table to significantly reduce cognitive processing in the brain. We then implement the interface and evaluated it through user experiments. As a result, in the objective evaluation, all experimental participants successfully performed one-time pad operations in their brains. In the subjective evaluation, although the in-brain signature imposed a high cognitive load, its potential acceptability is confirmed for transactions requiring a high level of security.

As global data privacy regulations evolve, privacy policies play a critical role in informing users about data collection and processing practices. However, their excessive length, complexity, and technical jargon often hinder user comprehension and informed consent. Existing research on privacy policies has largely focused on English-language documents, leaving non-English-speaking regions underexplored. This study addresses this gap by analyzing over 2,400 privacy policies on websites in China, Japan, and South Korea, evaluating their compliance with national regulations–China’s PIPL, Japan’s APPI, and South Korea’s PIPA. Using language detection, text mining, and compliance analysis, we examined adherence to legal standards and identified disparities across languages. Our findings highlight the need for improved clarity, compliance, and multilingual accessibility in privacy policy design. By integrating insights from HCI and AI-driven text analysis, this research advances understanding of global privacy practices and informs the development of user-centered approaches to enhance transparency and trust in digital environments.

Human-centric cyber security incidents remain a significant concern for organizations of all sizes, especially in the post-pandemic shift to remote and hybrid work. Existing strategies for addressing these issues, such as communication-based approaches, often fail to engage end users effectively or assess their knowledge adequately. This paper introduces CyberTrack, a flexible tool designed to enhance end-user engagement and compliance by integrating gamification, rewards, and monitoring mechanisms. Grounded in foundational security knowledge domains, CyberTrack enables organizations to assess security maturity, monitor user engagement, and address individual training needs. By fostering a culture of security consciousness, this approach promotes resilience against cyber threats, equipping organizations to tackle human-centric challenges comprehensively and proactively. The discussion presents the merits of a multi-faceted approach to promote, engage, and to monitor compliance where organizations can holistically embed training and education as part of their security strategy.

Recent, rapid information technology advancements have led to numerous AI models being applied in society, with innumerable AI applications across various fields. However, as AI models become central to societal functions, they inevitably attract the attention of malicious actors, resulting in the proliferation of counterfeit AI models. Therefore, identity verification, like user recognition, is essential for AI models as well. However, due to probabilistic variability in outputs, especially in large language models (LLMs), and continuous capability enhancement through autonomous learning, the typical user recognition method where the same information is enrolled and presented cannot be directly applied. To address this, in this study, we proposed and investigated an AI model identification method capable of handling the inherent variability in AI outputs while accurately verifying the authenticity of AI models whose capabilities continuously evolve. Specifically, we defined the requirements for AI model identification by “anthropomorphizing AI models” and developed a method based on “assessing the abilities of AI models.” The AI model identification method, designed based on these two concepts, is termed the Completely Automated Public Test to Tell Ability of Artificial Intelligence (CAPT-AI).

In previous studies, we proposed a person verification method that used the brainwaves evoked by imperceptible visible, auditory, or tactile stimulation. In this study, we confirmed that the verification performance was improved at almost all brain-wave electrodes when the imperceptible auditory and tactile stimuli were presented simultaneously.

Fingerprints are widely used for personal identification due to the high level of uniqueness exhibited by the characteristics of the ridge. Prominent applications include access control for devices, services, and buildings (e.g., smartphones, ATMs, and customs control). Most fingerprint acquisitions today rely on touch sensors because fine-grained fingerprint details, such as minutiae, are challenging to capture from moving images using a camera. Real-time video streams further complicate this process due to variations in brightness and background, making it difficult to focus on the finger’s surface and capture fingerprint details effectively. In addition, scale differences between the sampled fingerprint and the template can affect the accuracy of identity verification. In this work in progress, we propose a pipeline that integrates a fine-tuned version of YOLOv11s, to capture fingerprints from video in real-time. The proposed algorithm segments the distal phalanges of visible fingers, applies enhancement methods, extracts fingerprint features, and computes a feature-existence score for fingerprint matching and further HCI interaction. This score determines whether the fingerprint is sufficient for identity verification. We tested our proposal using validation images captured from HD videos with a 50 MP mobile phone camera, successfully segmenting distal phalanges located up to 120 cm. Also, our approach was validated on the public ISPFDv2 dataset comprising 16, 800 fingerphotos, achieving failure-to-acquire (FTA) rates below $$3.42\%$$ 3.42 % on the first attempt and $$0.08\%$$ 0.08 % on the fifth, highlighting the method’s potential for reliable fingerphoto segmentation.

The increasing demand for sustainable IT practices and resilient cybersecurity strategies has highlighted the need for multidisciplinary education to develop skills at the intersection of virtualization, cybersecurity, and green IT sustainability. This study presents the design and piloting of a nano-credential tailored for learners from non-IT disciplines, equipping them with essential competencies in these fields. The curriculum employs a blended pedagogical approach, integrating interactive, problem-based, and exploratory learning to enhance engagement and practical skill development. A structured evaluation process was conducted, involving expert validation and learner piloting. Experts confirmed the curriculum’s relevance, structure, and pedagogical soundness, while learners reported an increase in their understanding of sustainability, virtualization, and cybersecurity. The study identifies key challenges in multidisciplinary education, particularly in bridging technical and non-technical domains. Additionally, it highlights the value of modular and flexible learning pathways, demonstrating how multidisciplinary nano-credentials can bridge skill gaps across disciplines. This work serves as a practical use case for educators, illustrating how to integrate interrelated sustainability and technical-focused topics, aligning learning outcomes with global sustainability goals. By fostering both a sustainability-oriented mindset and technical proficiency, this approach empowers individuals to contribute effectively to cybersecurity and environmental sustainability efforts at organizational and global levels.

This study investigated the application of lag sequential analysis in characterizing the interactions between elderly caregivers and their use of space and furniture in modern family settings. This research focused on the caregiving behaviors of elderly individuals in dual-earner families in China, who are increasingly responsible for caring for grandchildren. It sought to inform the design of more livable and human-centered home environments. Societal changes have led to changes in family structures and caregiving responsibilities. Understanding how home design and furniture placement affect older people’s daily lives and caregiving activities is imperative. A 14-day non-participatory observation was conducted in two households using Noldus the Observer XT. Data were collected on feeding, playing, resting, and other caregiving activities, and lag sequential analysis was used to identify significant sequences of behaviors and their interactions with space and furniture. Results showed that home design and furniture placement significantly influenced grandparents’ caregiving behaviors. Inadequate layouts and inappropriately sized furniture exacerbated caregiving difficulties and hindered daily tasks. The frequent use of furniture, particularly during critical care moments, underscored its importance. The study emphasizes the urgent need for human-centered home designs that address the specific needs of older caregivers. By optimizing layouts, adapting furniture, and incorporating assistive devices, we can create more comfortable, convenient, and safe caregiving environments, thereby significantly improving the quality of life for older people. The practical implications of this research are significant. The findings can be used to design home environments that better support elderly caregivers in modern families. In addition, the research methods can be adapted to identify and address the unique needs of older persons in different caregiving contexts, thereby contributing to developing more livable and supportive environments.

This study applies the Technology Acceptance Model (TAM) and Hofstede’s Cultural Dimensions Theory to explore cultural differences in Smart TV adoption among the elderly in Singapore and Taiwan. A structured questionnaire survey was conducted with 68 participants (34 from Taiwan and 34 from Singapore), revealing that cultural background significantly influences Smart TV usage intentions. Taiwanese elderly scored higher in Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), favoring family-oriented and social interaction features, which align with Taiwan’s collectivist culture. In contrast, Singaporean elderly preferred personalized and utilitarian functions, reflecting a more individualistic cultural orientation. Moreover, Singaporean respondents perceived Smart TVs as less user-friendly, highlighting the need for improved digital literacy and usability support. At the policy level, Taiwan’s community training programs and Singapore’s Silver Infocomm Initiative have successfully increased Smart TV adoption among seniors. However, Taiwanese elderly demonstrated greater acceptance of government-led digital education initiatives. This study underscores the critical role of cultural factors in technology acceptance and suggests that future Smart TV designs should incorporate cultural adaptability to enhance user experience and digital inclusion across different demographic groups. The findings provide valuable insights for policymakers and technology developers, emphasizing the importance of tailored digital literacy programs and culturally responsive Smart TV features to bridge the digital divide among aging populations.

By 2030, China is projected to become a “super-aged” society, with individuals aged 65 and above comprising over 20% of the population. This demographic shift presents significant societal challenges, particularly in the context of digital inclusion. Meanwhile, the rapid development of e-commerce—characterized by its convenience and ability to transcend spatial and temporal constraints—has led to the emergence of the “silver economy,” driven largely by elderly consumers. In this evolving landscape, digital literacy in online shopping has become a crucial factor in ensuring older adults’ effective participation in the digital marketplace. This study adopts a survey-based approach, drawing on UNESCO’s Global Framework on Digital Literacy and utilizing a structured questionnaire to assess the online shopping capabilities of elderly individuals. It examines four core dimensions of digital literacy: Digital Operations & Information Acquisition, Interactive Communication & Content Expression, Security Awareness & Risk Prevention, and Problem-Solving & Adaptability. The findings indicate that while older adults generally exhibit competency in online shopping, notable deficiencies persist in certain areas. Moreover, educational attainment emerges as a key determinant of digital shopping skills, with higher levels of education correlating with significantly greater digital proficiency. These insights contribute to a deeper understanding of the factors shaping elderly individuals’ engagement in e-commerce and offer valuable implications for bridging the digital divide in an aging society.

The rapid advancement of digital technologies has ushered in the era of deepfakes, posing new challenges for media literacy and information integrity. Using a diary study, this research captures insights into the verification processes of two age groups, Generation Z (Gen Zs) and seniors, in identifying deepfake videos. We found that Gen Zs heavily relied on technological aids, utilizing search engines and social media platforms for deepfake identification. In contrast, seniors prioritize content consistency and source credibility, by leveraging on their life experiences and conventional media literacy skills. This generational divide highlights the need for tailored educational programs that enhance critical analysis capabilities in younger users while encouraging digital fluency among older adults.

Background: The successful implementation of innovative communication technologies can contribute to the social integration and general well-being of older adults. As part of the CO-HUMANICS (Co-Presence of Humans and Interactive Companions for Seniors) project, a telepresence robot designed to help older adults to stay socially connected will be developed.Aim: Following a human-centered approach, this study aims to explore older adults’ preferences for the use of human-robot gestures and voice commands to enhance robot-mediated communication via a telepresence robot.Methods: Two participatory design workshops were conducted in August 2024 with a total of N = 13 older adults (68–76 years old, Mage = 72.46, SDage = 2.73, 54% women). Participants were active senior citizens without cognitive or mobility impairments, all of them living in Germany.Results: Older adults discussed simple human-robot gestures and short voice commands that could enhance robot-mediated communication. Their application was considered especially useful during the following stages of human-robot interaction: (1) raising the attention of the robot, (2) moving closer to the robot, (3) initiating communication with a remotely located partner, (4) showing/pointing objects, (5) optimizing the location of the robot/direction of camera. Participants proposed gestures such as hand waving (wake-up signal for the robot) and pointing at objects with a finger and a raised arm and suggested simple voice commands such as “move forward”, “move backward”, and “stop”. Additionally, older adults showed a preference for using gestures and voice commands separately and simultaneously: “(I would say) ‘Robbie please show the pictures that are hanging on the wall’ and would accompany that with a gesture so that the robot knows where to look” (female participant, 72 years old).Conclusions: The implementation of human-robot gestures and voice commands could have a positive impact on older adults’ experience with robot-mediated communication. Recommendations for the future design of telepresence robots for older adults are made.

Based on the premise of the global aging background and the deficiency of the aging smart products in optimizing and designing experience for the elderly group, this study proposes a user experience design and product design paradigm based on VIGT, which is committed to establishing a good experience interaction and hands-on experience with the elderly group. To provide an efficient, customizable and highly identifiable complementary intervention for the elderly population and extend the use time. In the validation and experiment part, the effects of improved user experience design and product design based on VIGT model were evaluated through subjective interview questionnaires and communication experiments. The results show that the user experience design and product design based on VIGT lead to increased patient satisfaction, effective use efficiency, and stronger emotional connection. In addition, user experience design and product design interventions based on visual effects led to greater patient concentration and reduced negative emotions.

Deepfake technology has profoundly impacted society. While it has positive uses, it has also been exploited for malicious purposes, such as disseminating fake news and defaming individuals. However, relatively few studies have focused specifically on the unique harms associated with deepfake technology. Moreover, perceptions of harm are often influenced by the third-person effect (TPE) in which individuals believe that others are more susceptible to risks than themselves. This study addresses these gaps by investigating TPE on seniors’ perceptions of deepfake harms from a nuanced approach where deepfakes could lead to physical, financial, emotional, societal and relational harms. An online survey with 153 participants aged 55 years and above revealed that seniors perceived that they were most susceptible to societal harm. Further, the TPE was present in that seniors felt that others were much more likely to encounter the five types of harms than themselves.

During a pandemic, older adults in retirement homes face significant challenges due to their reliance on caregivers for instrumented activities of daily living (IADL). Restrictions on travel and social interactions further limit opportunities for physical activity, cognitive stimulation, and social engagement, emphasizing the need for innovative support solutions.This study explored the experiences of older adults in isolation, focusing on both the challenges they encountered and the strategies they used to maintain well-being. A focus group was conducted with 10 residents (average age: 82.7 ± 7.81 years; nine females, one male) of a senior retirement facility. Participants responded to ten open-ended questions regarding changes in their daily lives, difficulties faced, self-care practices, and methods for preserving physical health, mental well-being, and social connections.Content analysis of the 4,698-word transcript revealed two overarching themes: challenges of isolation and loss and coping strategies and adaptation. The theme ‘challenges of isolation’ included four sub-themes: reduced social interaction, isolation, missing family, and lifestyle changes. In contrast, the theme ‘coping strategies and adaptation’ consisted of seven sub-themes: maintaining a routine, engaging in activities, attending exercise classes, utilizing technology, staying connected with family, participating in community interactions, and relying on faith. Participant quotes highlighted both struggles and resilience.Findings emphasize the need for structured interventions that support older adults’ independence by promoting routine, digital engagement, and virtual or community-based exercise. Future research should include a larger, more diverse sample and explore long-term interventions to enhance physical, mental, and social well-being in aging populations.

The interactive mobile hospital guidance system has become increasingly pivotal in people's lives, significantly easing remote consultations for patients and reducingtime consumption. The visual interface design serves as the cornerstone of patient interaction. However, most current interactive mobile hospital navigation systems cater to the general population and overlook the exploration of elderly users' interaction experiences. This study delves into the impact of information presentationmode and map view presentation type on elderly users' user experience in interactivemobile hospital navigation systems. The study's variables encompassed information presentation types (i.e., icon + text and plain text) and map view presentation types (i.e., 2D and 3D). Using a 2 × 2 between-subjects design, a total of 40 participants were purposefully sampled and asked to complete questionnaires based on a 7-point Likert scale for subjective evaluation and semi-structured interviews. The results revealed the following: (1) When using the mobile hospital positioning interface, elderly users prefer the presentation of 2D maps with icons and have access to clearer information about landmarks; (2) Overall, the advantage of icons in the type of information presentation is not obvious in the mobile hospital positioning interface; (3) When the map is presented in a three-dimensional way, the icons increase the visual perceptual load of the elderly users in the mobile hospital positioning interface, affecting the the quickness of visual processing for elderly users, while the opposite is true when presented in a two-dimensional manner. This study provides a design reference for the future development of mobile hospital positioning interfaces for the elderly.

Canada is prioritizing digital healthcare solutions to help address staffing shortages, access inequities, and the needs of an aging population where remote healthcare can be critical for sustaining specialized and home care services. We conducted semi-structured interviews with 21 Canadians between the ages of 65 and 87 years old with chronic health conditions to explore their perspectives on remote healthcare technology. We found that participants were interested in adopting remote healthcare technology with AI features but concerned about their safety and privacy. While participants perceived the integration of AI in remote healthcare technology as inevitable, they expressed feelings of powerlessness in avoiding solutions that use AI if desired.

As the degree of population aging continues to deepen, the proportion of the elderly population is significantly increasing. This trend has placed higher demands on the design of age-friendly public facilities, including the need to meet the physiological, psychological, and social interaction requirements of the elderly. Focusing on the cognitive characteristics and processes of the elderly population, this study explores strategies and practices for designing age-friendly public facilities from the perspective of situational cognition. Through literature analysis and case study methods, the development issues of age-friendly public facilities are summarized, and the characteristics of age-friendly public facilities under the situational cognition perspective are discussed. Based on the three-level situational awareness model, a three-tiered progressive design strategy for age-friendly public facilities is proposed. The design strategies derived from the perception, comprehension, and prediction levels are as follows: stimulating multiple senses to promote in-depth perception, integrating cultural connotations to achieve multidimensional understanding, and providing diverse interactions to realize personalized experiences. Taking the public facilities in Yacheng Bay Science and Technology City in Sanya, China, as an example, design practices are carried out. The results show that incorporating situational cognition theory into the design of age-friendly public facilities can stimulate the in-depth perception of the elderly population, promote comprehensive understanding, and provide personalized experiences. This effectively enhances user satisfaction and positive emotional experiences among the elderly.

Purpose: This study aims to construct a human-computer interaction design model ‘SAFER’ for the problem of Alzheimer's disease patients’ cognitive decline that leads to wandering, to optimise the interaction experience between the product and the patient, the caregiver and the emergency service personnel, and to provide an innovative solution for the caregiving product. Methods: The cognitive characteristics and caregiving needs of patients were analyzed through literature review and questionnaires (n = 312, including family members of patients, caregivers and healthcare providers). User needs were classified using the KANO model, and functional weights were determined using the Analytic Hierarchy Process (AHP). The research process strictly followed ethical principles and protected participants’ privacy. Results: The study established the ‘SAFER’ HCI design model with five core dimensions: Simplicity, Adaptability, Feedback enhancement, Emergency handling, and Reliability, with quantified weights for each dimension. The interface scheme based on this model adopts a 3 × 3 grid layout, 64 × 64px minimum touch areas, and multimodal feedback mechanisms. User testing (n = 45) showed a task completion rate of 92.4%, a satisfaction score of 78.5, and a reduction in emergency response time to 15 s. Significance: This study provides a systematic theoretical framework and practical approach for designing caregiving products for people with Alzheimer's disease. It establishes a design mapping from cognitive characteristics to interface implementation and supports multi-party collaboration needs. The model offers a methodological reference for designing assistive products for people with cognitive disabilities, improving both caregiving efficiency and patients’ quality of life.

We are developing a game-based preventive care system, which promotes physical exercise by wiping a virtual window displayed on a screen. This system has a feature that continuous participation can be expected due to the enjoyment of the e-sports game since communication with surrounding peers increases through the game play. However, considering that elderly participants may not be familiar with gaming, this e-sports game was designed as a simple repetitive task of wiping windows alone. This simple task has caused some participants to lose interest. To address this issue, we leveraged the feature of enhancing excitement through communication with peer supporters. Based on the hypothesis that introducing a competitive or cooperative mode among multiple elderly participants would increase engagement, feel a sense of achievement, and promote sustained participation, we developed an e-sports game that can be utilized by groups of elderly individuals. The developed system was provided to groups of elderly individuals aged 63 and older, and experiments were conducted with over 100 participants. As a result, it was found that the system developed in this study significantly increased the desire to repeat its use compared to previously developed single-player systems.

This research proposal introduces an innovative approach to wearable technology by integrating aesthetic elements, such as jewellery, to improve adoption rates among people with dementia and promote inclusive design for the elderly. Although current research on wearables in dementia care has largely focused on functional and technical feasibility, there remains a significant gap in understanding how the physical design of these devices influences user experience, acceptance, and, ultimately, health outcomes. By critically reviewing studies that examine user attitudes and acceptance barriers, this research identifies the need for wearables that offer emotional and aesthetic appeal, helping to cultivate a dignified experience for the wearer and establishing emotional attachments that improve the accuracy of health monitoring. This proposal aims to address the key barriers to acceptance of technology, such as stigma and discomfort, by offering a wearable that transcends the typical medical device, becoming a valued accessory with a rich sensory experience. The presentation introduces an initial design concept and framework, exploring how thoughtful aesthetic choices can drive emotional engagement and improve user compliance. By aligning functional purpose with personal identity, the goal is to create a wearable that not only serves its health-monitoring role but also becomes a meaningful part of daily life, fostering ease of use and promoting a sense of dignity for elderly users. This work aims to inspire further design innovation in dementia care and beyond, focusing on how aesthetic considerations can play a crucial role in improving technology adoption and overall user well-being.

As urban transportation evolves, the subway stands out as a key choice for sustainable and cost-effective travel. Subway information screens commonly display real-time station updates. However, screen size and visibility constraints significantly impact how elderly passengers perceive this information. However, there is a notable lack of research in this specific area. Thus, this study explores how the placement of metro station landmark information (above, below, or alternating positions) affects the visual experience of elderly users on subway screens. Using a one-way ANOVA between-subjects design, we recruited 60 elderly participants through purposeful sampling. Data was collected via a 7-point Likert scale for subjective evaluations and semi-structured interviews. Key findings include: (1) Information placement influences older adults’ visual perception; (2) Compared to alternating positions, older users exhibit a higher preference for information displayed above the platform; (3) Compared to the below position, an alternating display offers an advantage in perceived visual search speed; (4) Likewise, compared to the below position, an alternating display reduces visual search load for older users. This study offers valuable insights for future interface designs in metro transportation tailored to elderly users.

In today’s society, mobile shopping is increasingly permeating people’s daily lives. Concurrently, the elderly user group utilizing mobile shopping is expanding and has become an indispensable part of the mobile shopping community. This study investigates the color contrast and information density in 360-degree panoramic mobile shopping interfaces and explores their impact on the user experience of elderly users. A 2 × 2 between subject design was employed to examine how different color contrast types (strong or weak) and information density types (high or low) affect the elderly’s subjective evaluations. A total of 40 participants were selected via purposive sampling and data were collected using the Technology Acceptance Model (TAM), and semi-structured interviews. Results showed that color contrast and information density significantly affect the elderly’s user experience. Interfaces with low color contrast were found to be more time-efficient and preferred by participants than those with high color contrast. In high information density situations, the elderly users felt more confident with low color contrast interfaces. Conversely, in low information density settings, high color contrast interfaces were more trusted by elderly users. This study offers insights for enhancing the 360-degree panoramic mobile shopping interface experience for elderly users.

In Linköping, Sweden, robots are roaming the streets. Since 2020, autonomous buses are traveling parts of Campus Valla and Vallastaden, in a research project called Ride the Future. In this living lab, the buses are running in non-traffic separated environments, something that has been observed as a challenge needing interaction design solutions. One of the biggest challenges in this non-traffic separated environment is the interaction with bicyclists; a typical reason for emergency brake activation is that bicyclists overtaking the bus reenter the right lane too early and trigger the emergency brake on the bus. This leads to unpleasant user experience for the passengers of the bus and reduces the efficiency of the transit system.In a multi-case study exploring eHMI solutions for solving this challenge, the authors have identified that projection-based eHMI systems show promise for being suitable for this type of traffic environment. This has been tested via a IDEO-inspired “quick and dirty” prototyping in situ, and a VR simulator study using a bicycle simulator and an environment built in Blender and Unreal Engine.The authors also propose two dimensions for eHMI communication; intent (corroborating prior research) and worldview. These two dimensions can be used for designing future eHMIs for autonomous vehicles.

Mature drivers are the driver group most at risk to be involved in fatal crashes when accounting for distance travelled. While advanced driver assistance systems (ADAS) promise to enhance older driver safety, trust in these systems is crucial for their effective use. However, the effects of factors such as trust in automation and attentional capacity on situation awareness (SA), a key factor in driver safety, remain underexplored . This study investigates the impact of trait and state facets of trust in automation and attentional capacity on SA. Participants aged 55+ completed two sessions comprising four experimental drives in a simulator equipped with an advanced cruise control system, while wearing a 14-channel EEG headset running an auditory oddball protocol. In session 1, the automated system functioned as intended (initial trust condition). In session 2, participants encountered system failures during the first two drives (low trust condition) and no failures during the last two drives (trust building condition). After each drive, participants were queried about their SA and trust in automation. State attentional capacity was indexed using differences in P300 amplitudes. Preliminary results reveal a drop in trust and SA at the end of the low trust condition, followed by a rebound effect. Lower P300 amplitudes in the low trust condition suggest an effect of trust on state attentional capacity. Results of this project will provide valuable insights into the interplay between trait and context-dependent factors and their impact on older driver safety, informing the design and development of future ADAS.

An interface of an in-vehicle infotainment (IVI) system was developed to validate such a proposal in a case study of design outcome. Participants of specific digital divide levels joined in participatory or non-participatory processes to investigate whether the design outcomes would mitigate or manifest the digital divide. Participants were first categorized by preliminary questionnaire into five levels of digital divide: innovators, early adopters, early majority, late majority, and laggards. Then they were assigned to design workshops with a balanced composition of participants of high and low digital divide levels. The interfaces generated from the workshops were experimentally validated to analyze how participatory design affected the contributions and design outcomes of the participants. The results indicated that the participation of users with a high digital divide in a participatory design process produced new but impractical prototypes, probably attributable to their limitation of technological proficiency being magnified in the process. In contrast, users with low digital divide generated more sophisticated designs with increased structural complexity in workshops. Future research is required to validate these findings and refine the proposed methods.

SAE Level 4 automated vehicle systems may allow drivers to nap while driving. The aim of this study was to investigate the natural take-over time after a nap without external time pressure. Nine subjects completed a total of 33 test drives in the Fraunhofer IOSB driving simulator, with take-over time measured as the time between the alarm sounding and the manual deactivation of autonomous mode. The average take-over time was 1.5 min after a nap, with a max of about 3.5 min. The results highlight the importance of sufficient time for a stress-free take-over process after sleep, which is far beyond the Level 3 standard of 10 s.

Advances in vehicle technology have raised concerns about drivers’ mental models (i.e., expectations and knowledge of system functionality) when interacting with vehicle human-machine interfaces (HMIs) used to operate comfort/convenience and advanced driver assistance system (ADAS) features. Misalignment between driver expectations and actual system functionality can lead to poor performance. This research, supported by the EcoCAR Electric Vehicle Challenge, examines drivers’ initial expectations for operating features in the 2023 Cadillac LYRIQ. Five participants unfamiliar with the vehicle completed 24 usability tasks involving comfort/convenience (e.g., seat, climate, radio) and ADAS (e.g., adaptive cruise control, semi-autonomous driving, automatic parking) features in the context of a rental car scenario. Behavioral observations, surveys, and interviews measured task success, completion time, perceived self-confidence, and perceived ease. This was supplemented by a cognitive walkthrough with a user experience subject matter expert. Analyses identified four key issues: inconsistent design, violation of user expectations, hidden controls and symbology, and complex menu navigation. Participants’ uncertainty and confusion suggest they developed incomplete or inaccurate mental models of the vehicle’s features. The results of this study highlight the importance of designing automotive systems that support accurate mental model formation. These findings may generalize across vehicles and can inform HMI design improvements industry-wide. Original equipment manufacturers can use this knowledge to enhance both the usability and the user experience of vehicle cockpits.

Automated driving eliminates the need for human drivers as interaction partners. External human-machine interfaces (eHMI) can serve as a new communication interface between automated and manual vehicles. This study investigates how factors relating to prosocial behavior can be integrated into the design of an eHMI. Based on previous research findings, a visual eHMI according to DIN 9421–125 was designed and evaluated in interviews (n = 8). The results show that positive emotions were promoted by ‘thank-you’ gestures of an avatar. However, the colour design based on the traffic light scheme led to confusion. The study indicates that eHMI can promote prosocial behavior in road traffic and provides an initial starting point for further research.

This study explored how display types for non-driving-related tasks (NDRTs) influence driving performance, gaze behavior, and situation awareness (SA) after takeover requests (TOR) during conditionally automated driving (CAD). Current legal regulations, such as those from JAMA and NHTSA, restrict the display of non-driving-related information while driving. As advancements in autonomous driving occur, there is an increasing need to research optimal display types for NDRTs to ensure safety. In this pilot study, three participants completed eight trials under CAD, with two repetitions in four different display types: Baseline, HUD, cellphone, and center console. The dependent variables measured were TOR time, gaze entropy, and SA accuracy. Participants were instructed to properly respond to TORs triggered by road obstacles in a two-lane urban driving environment. The results indicated that the HUD resulted in faster TOR reaction times and the highest gaze entropy, both of which corresponded to a high level of SA. Under HUD, drivers were able to keep their eyes consistently focused on the road, leading to the quickest reaction times and enhanced SA. Overall, this study offers valuable insights into designing in-vehicle displays in autonomous vehicles, particularly in optimizing configurations for NDRTs.

The advancement of autonomous driving technology presents both opportunities and challenges for drivers. These technologies allow drivers to temporarily engage in non-driving related tasks (NDRT) like watching videos or playing games but still play a supervisory role, making takeover issues critical. To enhance the development of situation awareness (SA) in takeover scenarios with varying context accessibility, this study proposed an advanced takeover requests (TOR) design that integrates the concept of monitoring requests (MR). Drivers can initiate control during the monitoring phase whenever deemed necessary. This study provided two types of TOR designs: with and without situational information. Two takeover scenarios: explicit and implicit scenarios. This study constructed a driving simulator cabin and used the Wizard of Oz method to simulate autonomous driving, collecting data on takeover reactions and vehicle dynamics from 32 participants. The statistical analyses indicated that the interaction between TOR designs and takeover scenarios highlights minimum time-to-collision (TTC) and lane change time, respectively. Providing SA information enables the driver to complete lane changes more planned in explicit-context scenarios, benefiting overall SA quality and increasing minimum TTC, enhancing safety margins. Consequently, TOR design should focus on delivering current vehicle dynamic information to improve the overall quality of SA. Although lane changes are executed quickly in implicit-context scenarios, the remaining minimum TTC is lower, suggesting that drivers may perform hasty lane changes. Therefore, TOR design should prioritize providing key hazard clues.

In the era of artificial intelligence-driven urbanization, where cities are constructed upon vast data infrastructures, urban landscapes increasingly resemble navigable databases as vehicles traverse commuting routes. This paper presents a vehicular interaction system grounded in hierarchical autonomous driving technologies, which transcends human sensory limitations through integration of real-time environmental monitoring with historical data retrieval. Employing the conceptual metaphor of “animal perspective”, this framework enhances driving safety while expanding users’ visual cognition. Technically, the system synthesizes vehicle-captured external data with urban-scale big data, transforming the cabin into an active data-sensing entity. Augmented reality overlays reveal high-dimensional information imperceptible to human vision. As autonomous driving levels increase, window-displayed information progressively evolves from safety alerts to immersive experiences. Quantitative and qualitative experiments involving 10 participants demonstrated enhanced user satisfaction with in-vehicle experiences. This research transcends human-centric interaction design paradigms, evidencing that simulated animal perspectives effectively extend human environmental cognition.

For the next few decades, cars with autonomous capabilities will still require drivers to resume control of the vehicle, when necessary, known as “takeovers”. However, takeovers are often difficult for drivers as they typically arise in time-sensitive situations. Two experiments were conducted to test the effectiveness of takeover warning designs. The first experiment explored whether two-stage warnings led to better takeover performance than single-stage warnings. Thirty-six undergraduate students completed two drives in autonomous mode on a 2D driving simulator where they took over control of the vehicle when alerted by a takeover warning. The second experiment investigated how pre-warning location and content impacted driver takeover performance. Forty undergraduate students completed four autonomous drives on a driving simulator. In both experiments, initial deceleration, distance to the hazard at the first brake pedal input, and distance to the hazard at the end response constituted takeover performance. Additionally, participant feedback about AV features uncovers potential drawbacks in warning designs that may be of interest to human factors experts. Findings from Experiment 1 show that that two-stage warnings led to better takeover performance than single-stage warnings. In Experiment 2, infotainment-based pre-warnings and dynamic pre-warnings led to prompter takeovers and higher helpfulness ratings than dash-based pre-warnings and static pre-warnings. This research provides stakeholders with takeover alert designs that enable quicker responses and safer stopping distances.

This study compares wrist-worn wearables and palm-based sensors for electrodermal activity (EDA) measurement in automated driving scenarios. Data were collected using the Empatica EmbracePlus smartwatch and EdaMove4 device during simulated driving experiences designed to elicit emotional responses like surprise and discomfort. Results indicate that the EdaMove4 provided higher-quality data with clearer skin conductance responses (SCRs) and levels (SCLs) than the smartwatch, which was more affected by motion artifacts and lower signal amplitude. For some participants, the smartwatch data was classified as entirely invalid due to poor sensor placement. Despite limitations from a reduced participant pool caused by data quality issues, it highlights the trade-off between the convenience of wrist-worn devices and the reliability of palm-based sensors, contributing to research on wearable biosensors in dynamic, real-world settings. Further research is needed to improve data quality and validate findings with larger samples.

This paper describes a proposal for drivers’ effective detection for precise scanning to unexpected events during train operations. The study highlights the importance of gaze behavior and visual exploring strategies in improving reaction times and ensuring passenger safety. The paper also discusses the effectiveness of visual feedback at real time in a simulator of virtual reality. The system navigates the driver’s eye movement to scan the whole screen efficiently, balancing focus between central and peripheral vision. The feedback mechanism includes a colour-coded visual indicator that prompts the driver to look broader area at appropriate intervals. The study concludes by outlining future improvements, such as adjusting the scan interval according to speed and improving the display settings for better training effects.

As automated vehicles (AVs) become increasingly integrated into real-world traffic, understanding drivers’ trust dynamics and intervention tendencies in potential hazard scenarios is critical for ensuring safe and effective human-AV collaboration. This study examines how drivers calibrate trust and adjust their intervention behavior when repeatedly exposed to ghost probe events – sudden emergences of vulnerable road users from occluded areas. A driving simulator experiment was conducted to investigate drivers’ responses to AVs exhibiting defensive (decelerating and stopping) and assertive (maintaining speed without collision) driving styles in potential hazard scenarios. Twelve participants experienced five consecutive ghost probe events, with only the fourth involving an actual pedestrian running into the road. Trust levels and intervention tendencies were assessed after each event using self-reported questionnaires, followed by a short interview. Results indicate that in the first three events, the defensive AV style facilitated progressive trust stabilization and a gradual decline in intervention tendency, whereas the assertive AV style initially led to trust erosion, followed by adaptive recalibration and a subsequent decrease in intervention tendency. The fourth event induced increased trust in defensive AVs and further reduced intervention tendency, while it diminished trust in assertive AVs yet also lowered intervention tendency. By the fifth event, both trust levels and intervention tendencies returned to levels observed in the third event. These findings offer insights into trust adaptation mechanisms and driver intervention behaviors in AV interactions, informing the design of AV systems to optimize trust calibration, reduce unnecessary interventions, and enhance overall safety in mixed-traffic environments.

In the digital transformation of global supply chains, the efficiency of in-plant logistics is substantially hindered by delayed information interactions. While in-vehicle personal digital assistants (PDAs) present a viable solution to these challenges, research on their effectiveness especially in dynamic environments, such as forklift operations, is overlooked. This study employed a VR-based simulation to assess the efficiency of manual data entry and retrieval for forklift operators using in-vehicle PDAs while driving. A controlled variable ergonomics experiment was conducted to evaluate operator’s performance during typical warehouse operations. The experiment used button size and button spacing on the PDA interface as independent variables, while task completion time, task completion accuracy, and driving safety were treated as the dependent variables. Data collected from 30 participants were analyzed using various statistical methods. Findings of the study indicate that the configuration of 25 mm button size and 0 mm button spacing outperformed other combinations in reducing human-computer interaction time, improving interaction accuracy, and enhancing driving safety. It is hoped that the results of this study will provide useful insights for the optimization of in-vehicle PDA interface design in in-plant logistics.

With the acceleration of urbanization and the widespread adoption of sustainable mobility concepts, two-wheeled electric vehicles (E2Ws) have emerged as a green transportation alternative, playing an increasingly vital role in residents’ daily commutes. However, existing community parking facilities for E2Ws face challenges such as insufficient capacity, uneven distribution of charging equipment, and inadequate maintenance, significantly impacting residents’ quality of life and commuting experience. This study proposes an innovative design methodology for community E2W parking facilities by integrating the Kano-QFD and FBS-TRIZ models. First, the Kano model is employed to classify and prioritize user demands, which are subsequently translated into design parameters through Quality Function Deployment (QFD). Second, the Function-Behavior-Structure (FBS) model is utilized to map and transform functional requirements, while TRIZ theory resolves design conflicts. The results demonstrate that this methodology effectively enhances parking capacity, charging accessibility, and management efficiency, significantly improving user experience and providing novel insights for the sustainable development of smart communities.