Digital health interventions in the Global South often rely on transmission models, assuming that delivering correct medical information yields better care. We challenge this view through an analysis of a multi-platform social media intervention for Community-Based stroke Rehabilitation(CBR) in rural Thailand. Following a collaborative development process with clinicians and a deployment across roughly 2,000 villages, we interviewed 28 caregivers, patients, and health volunteers. We found that communities appropriated the technology in unexpected ways, such as using videos as “social objects” to manage family hierarchies, integrating rehabilitation into Buddhist merit-making, and prioritising offline peer networks over online discussion. Our findings suggest that effective Human-Computer-Interaction (HCI) for digital health in Low- and Middle-Income Countries (LMICs) should look beyond engagement metrics to support the appropriation of digital tools, enabling communities to integrate clinical protocols into their existing cultural and relational fabrics.

People living with dementia are at risk of social isolation, and conversational AI agents can potentially support such individuals by reducing their loneliness. In our study, a conversational AI agent, called MindTalker, co-designed with therapists and utilizing the GPT-4 Large Language Model (LLM), was developed to support people with early-stage dementia, allowing them to experience a new type of “social relationship” that could be extended to real life. Eight PwD engaged with MindTalker for one month or even longer, and data was collected from interviews. Our findings emphasized that participants valued the novelty of AI, but sought more consistent, deeper interactions. They desired a personal touch from AI, while stressing the irreplaceable value of human interactions. The findings underscore the complexities of AI engagement dynamics, where participants commented on the artificial nature of AI, highlighting important insights into the future design of conversational AI for this population.

The development of AI to function as communicators (i.e., conversational agents), has opened the opportunity to rethink AI’s place within people’s social worlds, and the process of sense-making between humans and machines, especially for people with autism who may stand to benefit from such interactions. The current study aims to explore the interactions of six autistic and six non-autistic adults with a conversational virtual human (CVH/conversational agent/chatbot) over 1–4 weeks. Using semi-structured interviews, conversational chatlogs and post-study online questionnaires, we present findings related to human-chatbot interaction, chatbot humanization/dehumanization and chatbot’s autistic/non-autistic traits through thematic analysis. Findings suggest that although autistic users are willing to converse with the chatbot, there are no indications of relationship development with the chatbot. Our analysis also highlighted autistic users’ expectations of empathy from the chatbot. In the case of the non-autistic users, they tried to stretch the conversational agent’s abilities by continuously testing the AI conversational/cognitive skills. Moreover, non-autistic users were content with Kuki’s basic conversational skills, while on the contrary, autistic participants expected more in-depth conversations, as they trusted Kuki more. The findings offer insights to a new human-chatbot interaction model specifically for users with autism with a view to supporting them via companionship and social connectedness.

Abstract The ‘Three Good Things’ (TGT), a structured journaling exercise, has shown great effect in helping improve mental well-being. However, the exercise could be quite tedious and difficult to carry out daily. To improve the user experience of the exercise, we developed ‘Happy Click’, a mobile application which makes use of a physically present smart button to enable users to record their daily TGT in a more engaging manner. Two preliminary exploratory studies were carried out to examine the feasibility of our approach where the effects of the TGT intervention designed around the mobile and tangible happy click concepts were examined. The findings suggested that participants had significantly less stress after carrying out the TGT using the tangible interface. Participants also reported better user experience and acceptance when carrying out the exercise using the tangible interface compared to the mobile and higher levels of attractiveness and stimulation with the mobile TGT compared to the traditional approach. While these promising results should be interpreted with caution due to the limited sample size, they also underscore the potential advantages of our ‘Happy click’ approach and highlight the need for further, larger-scale evaluations to be conducted to validate its efficacy and scalability.

Web advertising services have exhibited consistent growth over the years. However, the conventional methods of web advertising recommendations, relying on keyword matching with search queries and browsing histories, encounter challenges when it comes to effectively targeting users with hidden or latent interests. In contrast, the use of mobile device location data in advertising recommendations often centers around physical store proximity. To address these limitations, our research aims to enhance web advertising recommendations by analyzing latent user interests through real-world behavioral data. This study specifically investigates the influence of area size on user behavioral analysis and its subsequent impact on the accuracy of predicting visit probabilities. We achieve this by extracting the user’s activity range from user behavior (movement) log data and geotagged tweets. Subsequently, we tally the places visited by the user, considering spot attributes, and convert this data into feature vectors. Utilizing these feature vectors in conjunction with various classification methods, we build learning models. In this paper, we present and evaluate these learning models employing different area sizes, verifying their accuracy in predicting user visits to specific stores.

While conversational agents have traditionally been used for simple tasks such as scheduling meetings and customer service support, recent advancements have led researchers to examine their use in complex social situations, such as to provide emotional support and companionship. For mourners who could be vulnerable to the sense of loneliness and disruption of self-identity, such technology offers a unique way to help them cope with grief. In this study, we explore the potential benefits and risks of such a practice, through semi-structured interviews with 10 mourners who actively used chatbots at different phases of their loss. Our findings indicated seven approaches in which chatbots were used to help people cope with grief, by taking the role of listener, acting as a simulation of the deceased, romantic partner, friend and emotion coach. We then highlight how interacting with the chatbots impacted mourners’ grief experience, and conclude the paper with further research opportunities.

In recent years, the number of people who run for the purpose of improving their health and physical fitness has been increasing, but it is not easy to continue running. Therefore, we believe that it is very important to develop a running support system. In our previous study, we developed a running support system and an application that enables users to run with a virtual runner created by using their past running data in an acoustic augmented reality space. However, this system has the problem that it can only race against the past running records of oneself or one's acquaintance, and can only race against a single running record. Therefore, we considered it necessary to develop a system that allows users to race against an unspecified number of users and that allows users to arbitrarily select the distance and number of times they wish to race. In this paper, we propose an online marathon system that enables large-scale online races and examine the effect of the number of competitors on user motivation.

In recent years, running has become increasingly popular as an effective exercise activity which could help improve and maintain one’s physical health. However, even if we understand that exercising regularly has many benefits to our body and brain, it is not easy for people to stay motivated. To deal with this issue, in our previous research, we developed a running support system based on the users’ own records. Our system enables users to run and compete against virtual runners in an audio-based augmented reality space. However, in the AR running support system, competing with real users is more immersive than competing against one’s own running records. In this study, we aim to improve the motivation of running users, not only using the users’ own records, we also use the GPS information and time data from other real runners. We believe that by using information from real runners, our virtual running rival generation/recommendation method can improve the enthusiasm of users. Therefore, we propose a rival recommendation approach for our acoustic-based AR running support system considering the athletic ability of users. In addition, as a preliminary analysis, we conduct a questionnaire survey to clarify users’ needs for the future development of our running support system.

As an important foundation of the smart city, MaaS (Mobility as a Service) provides a seamless passenger experience using multiple modes of transport from the beginning until the end of a journey, and its developments have attracted keen interest from the industry in recent years [1] [2] . Furthermore, as the last mile of the MaaS, electric bicycle is an important and environmental-friendly transportation [3] [4] .

Prolong Grief Disorder (PGD) is a condition in which mourners are stuck in the grief process for a prolonged period and continue to suffer from an intense, mal-adaptive level of grief. Despite the increased popularity of virtual mourning practices, and subsequently the emergence of HCI research in this area, there is little research looking into how continuing bonds maintained digitally promote or impede bereavement adjustment. Through a one-month diary study and in-depth interviews with 17 participants who recently lost their loved ones, we identified four broad mechanisms of how grievers engage in what we called ”backstage” grieving (as opposed to bereavement through digital public space like social media). We further discuss how this personal and private grieving is important in maintaining emotional well-being hence avoiding developing PGD, as well as possible design opportunities and challenges for future digital tools to support grieving.

In recent years, there are various review sites on the Web. In online shopping, review sites are important because they strongly influence consumers’ purchasing decisions. We focus on cosmetic reviews and consider the skin type and usability of individual users. In order to realize the cosmetic recommendation system, we are working on the development of a review recommendation method by an automatic scoring system using an evaluative expression dictionary for each cosmetic item classification. Since cosmetic items have detailed classifications, it is necessary to build an evaluative expression dictionary for each cosmetic classification in order to perform automatic scoring. Therefore, it is desirable that the evaluative expression dictionary construction method be efficient and semi-automatic. In this paper, we try to improve the evaluative expression dictionary and examine the efficient method for developing evaluative expression dictionary based on Word2Vec for cosmetics.

Despite the substantial research interest in using Virtual Reality (VR) in healthcare in general and in Psychological, Cognitive, and Behavioral (PC&B) interventions in specific, as well as emerging research supporting the efficacy of VR in healthcare, the design process of translating therapies into VR to meet the needs of critical stakeholders such as users and clinicians is rarely addressed. In this paper, we aim to shed light onto the design needs, opportunities and challenges in designing efficient and effective PC&B-VR interventions. Through analyzing the co-design processes of four user-centered PC&B-VR interventions, we examined how therapies were adapted into VR to meet stakeholders’ requirements, explored design elements for meaningful experiences, and investigated how the understanding of healthcare contexts contribute to the VR intervention design. This paper presents the HCI research community with design opportunities and challenges as well as future directions for PC&B-VR intervention design.

Advertisement (ad) recommendation services for mobile users are rapidly increasing. The conventional ways of recommending ads are based on the analysis of user's explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on a method to analyze the user's latent interest in web browsing history which categorized positive and negative behaviors. However, we think that the latent interest of users appears not only in the web space but also in the physical space. In this paper, we adapt the method of the linked pages to physical space locations using geo-tagged tweets. Based on several evaluations, we discuss the possibility to recommend ads according to the user's current location.

In recent years, an evolutionary change occurred in the digital world when various social networking sites became popular. This led to a big increase of users who share their activities in digital form. A huge amount of digital information has become available, providing researchers with a unique insight into the behavior and activities of entire population. The use of Geo-tagged social media data became an emerging trend to represent user actions and behaviour at specific geographical locations. However, the inaccuracies of Geo-tagged data from social media often limits the utility of this data source in micro scale analysis (at the street or place of interest level (POI)). Our study supports how social media data could be matched accurately with specific physical locations. More specifically, our investigation includes Geo-tweet data and image data from flickr to map accurately physical location using machine leaning and deep learning techniques. In this paper, a preliminary discussion of this work is provided, using Geo-tagged data from Twitter and Open StreetMap in cities such as San Francisco and London as well as for image mapping using Flickr data in cities such as San Francisco and Kyoto.

Many people with dementia (PWD) residing in long-term care may face barriers in accessing experiences beyond their physical premises; this may be due to location, mobility constraints, legal mental health act restrictions, or offence-related restrictions. In recent years, there have been research interests towards designing non-pharmacological interventions aiming to improve the Quality of Life (QoL) for PWD within long-term care. We explored the use of Virtual Reality (VR) as a tool to provide 360°-video based experiences for individuals with moderate to severe dementia residing in a locked psychiatric hospital. We discuss at depth the appeal of using VR for PWD, and the observed impact of such interaction. We also present the design opportunities, pitfalls, and recommendations for future deployment in healthcare services. This paper demonstrates the potential of VR as a virtual alternative to experiences that may be difficult to reach for PWD residing within locked setting.

Nowadays routing systems can provide optimal routes in terms of time and travel distance. However, they do not consider special needs of certain group of users. For example, people recovering from alcohol and drug addiction may want to travel a route that is alcohol and drug-free. In this demonstration, we propose a system we built that helps with this special need. We detect if a street is related to alcohol and drug by exploiting Web open data, including Foursquare, microblog tweets, Google Street View images, and crime data. We calculate an alcohol and drug relevance score using unsupervised methods, to be used in route ranking. Our system prototype is ready to be tested for the cities of San Francisco and Kyoto.

The vast and growing amount of publicly available real-time information from social network services such as Twitter could provide many benefits for improving public health and safety, especially towards the area of crime prevention. While prior studies have leveraged such data to help in the prediction of criminal incidents, we have developed a crime investigation tool which utilizes Twitter data to aid in crime analysis. The tool provides contextual information about crime incidents by visualizing the spatial and time-based characteristics of a crime and its context using data from nearby tweets and from the criminal history of a target place. In addition, sentiment analysis is also carried out with the identified tweets to further examine the negative characteristics of the spatial areas related to the different crimes in question. A demonstration prototype of this tool was developed as a web application for the area of San Francisco.

In recent years, running has become increasingly popular as an effective exercise activity which could help improve and maintain one's physical health. However, it is generally difficult to motivate people to adhere to their fitness regimens and persist in such activities. To help address this problem, we developed a Gamified Acoustic AR running support system where we use the previous running records of a user to project a "Virtual runner" into an augmented reality space for runners to compete against. The presence of the virtual runner is conveyed through the sound of running footsteps and breathing: users hear the sound of the virtual runner while they are running and are able to virtually compete against themselves in real time. In this paper, we describe how such a system could be implemented and discuss the results of an experiment study which highlights the effectiveness of our Gamified AR system.

For a tourist who wishes to stroll in an unknown city, it is useful to have a recommendation of not just the shortest routes but also routes that are pleasant. This paper demonstrates a system that provides pleasant route recommendation. Currently, we focus on routes that have much green and bright views. The system measures pleasure scores by extracting colors or objects in Google Street View panorama images and re-ranks shortest paths in the order of the computed pleasure scores. The current prototype provides route recommendation for city areas in Tokyo, Kyoto and San Francisco.

This paper proposes a novel system which utilizes information from a social network services to suggest food venues to users based on crowd preferences. To recommend an appropriate food venue for each crowd preference, the system ranks food venues in each region by using an improved collaborative filtering method based on the differences between locations and languages in geo-tagged tweets. A key feature of the proposed system is the ability to suggest food venues in regions where very few geo-tagged tweets are available in a specific language by using the weighted similarity by others' preferences. To implement the system, more than 26 million tweets from European countries were collected and analyzed based on 6 languages and 7 regions. Afterwards, we provide an evaluation of the ranked venues proposed by the system based on 89 French speakers in 7 European countries.

Online advertising has become a popular method for companies to market their products and services to potential customers. The methods used by conventional web advertisement systems to decide on which advertisements to display to users in a real-time bidding environment generally do not consider the latent interests of users and as such it is difficult for advertisers to target and acquire new customers with potential interest in the product. Therefore, we proposed the development of a recommender system which could recommend advertisements to users based on their latent interests. In this paper, we outline two experiment studies related to the development of this system. The first study was carried out to examine the effect of using a long and short browsing history acquisition period to train the user model and predict user interests. The results suggested that a longer browsing history acquisition period did not necessarily result in better predictive performance. The second study examined the use of a categorized web browsing history to predict user interest. The results showed the accuracy of the classifiers increased when website categories were used instead of Fully Qualified Domain Names.

Education plays an important role in helping developing nations reduce poverty and improving quality of life. Ubiquitous and mobile technologies could greatly enhance education in such regions by providing augmented access to learning. This paper presents a three-year iterative study where a ubiquitous sensor based learning platform was designed, developed and tested to support science learning among primary school students in underprivileged Northern Thailand. The platform is built upon the school's existing mobile devices and was expanded to include sensor-based technology. Throughout the iterative design process, observations, interviews and group discussions were carried out with stakeholders. This lead to key reflections and design concepts such as the value of injecting anthropomorphic qualities into the learning device and providing personally and culturally relevant learning experiences through technology. Overall, the results outlined in this paper help contribute to knowledge regarding the design, development and implementation of ubiquitous sensor-based technology to support learning.

This paper describes a study aimed to understand the use of 3D virtual world (VW) technology to support life engagement for people with dementia in long-term care. Three versions of VW prototypes (reminiscence room, virtual tour and gardening) utilising gestured-base interaction were developed iteratively. These prototypes were tested with older residents (80+) with dementia in care homes and their caregivers. Data collection was based on observations of how the residents and care staff interacted collaboratively with the VW. We discussed in depth the use of VWs in stimulating past memories and how this technology could help enhance their sense of self through various means. We also highlighted key approaches in designing VWs to sustain attention, create ludic experiences and facilitate interaction for older people with dementia.

There is an increasing need to find innovative activities to help the older population maintain a healthy life. Virtual worlds, which can provide social engagement, entertainment and creativity as well as useful information and services for older people might offer a solution to this issue. Although emerging studies have begun to look into the benefits of virtual worlds in healthcare, little has been done in the context of older people. Based on semi-structured interviews and previous research on healthy aging, we identified and described in depth four areas in which virtual worlds could be useful to support older people. In general, it was found that virtual worlds could help empower older people to manage their disabilities, facilitate social engagement, provide mental stimulation and productive activities.

3D virtual worlds are becoming increasingly popular as tool for social interaction, with the potential of augmenting the user’s perception of physical and social presence. Thus, this technology could be of great benefit to older people, providing home-bound older users with access to social, educational and recreational resources. However, so far there have been few studies looking into how older people engage with virtual worlds, as most research in this area focuses on younger users. In this study, an online experiment was conducted with 30 older and 30 younger users to investigate age differences in the perception of presence in the use of virtual worlds for social interaction. Overall, we found that factors such as navigation and prior experience with text messaging tools played a key role in older people’s perception of presence. Both physical and social presence was found to be linked to the quality of social interaction for users of both age groups. In addition, older people displayed proxemic behavior which was more similar to proxemic behavior in the physical world when compared to younger users.

This article reports a study that investigated the occurrences of empathy in online support communities for teenagers. Qualitative content analysis with 400 messages from a discussion board about depression was used to identify how empathy was expressed in the specific online communication. Emphasis was also placed on the comparison of this age group to older people, by comparing the results with those from a previous study on empathy in an online support community about depression for older people. Specifically, the analysis focused on the frequencies of the categories of the code scheme, linguistic characteristics of the communication content, the occurring components of empathy, and the roles as well as activities of the members. From our analysis, we concluded that young people exchanged a substantial amount of empathic emotional communication when participating in an online support community, and they communicated on a more personal level compared to older people, who tended to engage in a more formal communication. In addition, teenagers also showed a high level of understanding but lower level of concern compared to older persons when expressing empathy online.