Conference Agenda

With the increasing aging population, home-based care has become a main nursing mode for the elderly. To better assist the elderly with daily activities, healthcare management and emotional support, smart technologies and products have been widely considered. However, the gap between the high requirements of smart products on users and the physical and cognitive degradation of the elderly has made it difficult for the elderly to accept new technologies. Therefore, this study aims to deeply investigate the smart product acceptance of the elderly in home-based care. For the purpose, a scenario-based analysis approach was adopted to investigate different living scenarios and analyze their smart product acceptance in different contexts. The combination of questionnaire survey and interview was employed, and 95 participants (age > 60) were involved. Statistical analysis and DEMATEL (Decision making trial and evaluation laboratory) were applied to reveal the structure and co-relations of the influencing factors. It shows that gender, age, health status, economic status, and technical proficiency have impacts on the smart product selection. Indicative and safe were identified with the most important influence on smart product acceptance in daily activities and healthcare scenarios. Trustful and efficient have moderate influence on entertainment and sports scenarios, and the influence of enjoyable and dependent factors is not that significant. Generally, this research can contribute to suggestions on the smart product design with full consideration of elderly’s physical and cognitive capacity. Moreover, it provides governments and the society with insights on the further reform of more practical elderly-care policies and social support strategies.

In depopulated areas, public transportation services provided by municipalities are important for traffic-vulnerable people. However, some municipalities need to scale back their services due to budget shortfalls. To tackle with this social and technological problem, transdisciplinary approach is necessary. This study proposes a new design for a sustainable and better public mobility service for the uncertain future beyond 10 years in Narita City, Japan. Narita City is a complex area where urbanization by the airport and rural farming villages coexist. We adopted a model-based design process based on engineering design practice with simulations. To estimate the impact of various conditions under public transportation services, we use Quality of Mobility (QoM) index, which comprehensively quantifies the quality of an individual's mobility and can estimate the time-series changes of public transportation in the future. The simulation results show that the QoM values of Narita residents can be divided into two groups under the current service, with the percentage of residents with lower QoM values increasing as they get further away from the urban area. In contrast, as the years passed, the average QoM in the urban area decreased, while that in the other areas increased. In other words, the elderly gradually clustered in the urban area as the years passed by natural population changes. Findings of our design proposal for a sustainable mobility service design in Narita City are to adopt demand-responsive transport like service to satisfy the small but uncertain future demand of traffic-vulnerable people in depopulated area at low cost.

The ever-increasing demand for user-centred smart products has driven the need for automation methodologies in product design processes, particularly for assessing usability and user experience (UX). Traditionally, practitioners construct functional (software) or physical (hardware) product prototypes to enable usability and UX assessments. Constructing a prototype followed by human testing are both time consuming and expensive activities. If virtual models and automated methods can replace some components of these activities, the time and cost of smart product development could be reduced while continuing to generate useful and beneficial products. In this paper, we survey the literature related to automated assessment methods for smart products. We identified five key activities on which to focus related to the testing or the design cycle: design thinking, design ideation, prototype creation, user data collection, and data analysis. The review methodology consisted of comprehensive search queries tailored to each activity to encapsulate automation methods pertinent to smart product development in research articles published from 2000-2023. Over 100 relevant articles were identified across engineering, human-computer interaction, human factors, industrial design, and other disciplines. This review highlights the effectiveness and limitations of various automation methods, benchmarked against traditional practice, providing valuable insights and practical recommendations for researchers and designers seeking to optimize smart product design processes for broad usability concerns. We are particularly interested in designing assistive mobility and rehabilitation devices. Development time and resources are often limited yet usability and UX directly impact important outcomes including perceived function, stigma, and device adoption. Improving these requires a transdisciplinary approach.

Stress and fatigue significantly affect an operator’s performance in vessel traffic management, underscoring the importance of their detection for maritime navigation safety. While most previous studies have relied on laboratory-based experiments to gather physiological data, it is recognized that the stress and fatigue induced in such controlled environments differ from those in actual situations. In this study, we have developed a platform that facilitates the collection of real-time heart rate data from operators during genuine VTS operations, while ensuring minimal operational disruption. Additionally, we employed a variety of machine learning models to validate the results of stress and fatigue detection in both within-day and cross-day contexts. These results offer valuable insights into the stress and fatigue levels experienced by operators in real-world settings. Furthermore, the analysis of this real-world data underscores the challenges faced in the practical implementation of stress and fatigue detection systems.