The Social Interaction Experiences of Older People in a 3D Virtual Environment

Virtual worlds offer much potential in supporting social interaction for older adults, particularly as a platform which can provide an interactive and immersive social experience. Yet, there has not been much work carried out to study the use, interaction and behavior of older people in 3D virtual world systems, especially studies which investigate their interactions in a fully functional virtual world. Most focus on issues related to usability such as cognitive difficulties when navigation in a 3D space and we know little about their perceptions and preferences when socializing in a virtual space. In this chapter, we report an experimental study examining the various factors which affected the social experience of older users in virtual worlds. The study involved 38 older participants engaging with a 3D and non-3D virtual grocery store. A mixed method of questionnaire and contextual interview was used for data collection and analysis. Overall, we found that physical presence was a significant predictor of many measures defining the quality of social interaction, yet participants often reported a sense of artificiality in their virtual experience. Interestingly, avatars were not considered directly important for social interaction and instead were only seen as a “place holder” to complete the tasks. Two factors contributed to this, the lack of non-verbal communication and the perceived difficulty in embodying physical people with virtual avatars

Investigation of virtual worlds as a platform to support healthy aging for older people

Due to the aging of the population in recent years, it is becoming essential to find innovative activities to help the increasing older population maintain an active lifestyle and delay the need for institutionalized care. Virtual worlds, which have many potential values such as in providing social engagement, could be used to support older people in this aspect. Despite this, most research and design of virtual worlds today are based on young users and do not coincide well with the interests and requirements of older people. It is therefore necessary to investigate how virtual worlds can be designed to not only meet the needs of older users but also to provide opportunities for social engagement and support healthy aging. In the first stage of the research, a series of studies were carried out with older virtual world users to investigate their characteristics, interests and activities. This includes a qualitative interview study and an empirical study. Older users were able to develop interpersonal relationships in virtual worlds and were interested in activities which made useful contribution to society or those which allowed them to socialize with people who share similar interests. Next, two experiment studies were carried out, the first to investigate age related differences in virtual social interaction and the second to determine how different factors influence the social interaction experience. Factors such as navigation were found to influence social interaction and the study revealed limitations relating to the usefulness of the avatar. The findings from this thesis helps extend our theoretical understanding of the interactions and activities of older people in virtual worlds and how previously identified concepts regarding virtual social interaction relate to older users. In addition, the findings were also applied into guidelines to aid developers in creating better virtual worlds to facilitate social interaction and healthy aging

A User-POI-Guide Cost Optimization Method for Tourism Planning Considering Social Distance and User Preferences

MaaS (Mobility as a Service) itself has come into common use, and these developments have attracted keen interest from the industry in recent years. MaaS can be applied as a solution to deal with the current situation by considering the social distance. However, due to the time-share mechanism, personal assets are monopolized by specific users for a long time that cannot be shared with other users at the same time. Thus, the sharing economy companies in the tourism industry (e.g., Airbnb Experience and Huber) are in a dilemma of low productivity and high cost. In this research, we propose a new travel guide sharing service that considers the concept of social distance and user preferences. The user side only needs to select simple conditions such as travel time and the number of POIs (Point of Interest) that she/he plans to visit, meanwhile, the guide side simply inputs the POIs that she/he can guide. Furthermore, by analyzing these basic information, our proposed system can recommend the tour guides, scenic spots, and route planning to provide a real-time tour guide plan, which addressed the user's preferences and reduced the face-to-face communication to users in advance. To verify the effectiveness of our proposed method, we also ask 68 users to evaluate our system and analyze the results of questionnaires

Detecting Train Delays using Railway Network Topology in Twitter

This paper presents a novel train delay detection method based on topic propagation analysis of geo-tagged tweets between railway stations. Our goal is to detect traffic accidents and to predict train delays in railway network topology by tracing how relevant tweets propagate in real space and cyberspace. In our method, we utilize railway network as the topology of real space, and extract the topology of social network that is mapped on the railway network. This permits observing the influence of delays on stations with a few tweets, or predicting related tweets of affected stations even if the tweets contain indirect topics about delays

A city-wide examination of fine-grained human emotions through social media analysis.

The proliferation of Social Media and Open Web data has provided researchers with a unique opportunity to better understand human behavior at different levels. In this paper, we show how data from Open Street Map and Twitter could be analyzed and used to portray detailed Human Emotions at a city wide level in two cities, San Francisco and London. Neural Network classifiers for fine-grained emotions were developed, tested and used to detect emotions from tweets in the two cites. The detected emotions were then matched to key locations extracted from Open Street Map. Through an analysis of the resulting data set, we highlight the effect different days, locations and POI neighborhoods have on the expression of human emotions in the cities

A Reflection on Virtual Reality Design for Psychological, Cognitive & Behavioral Interventions: Design Needs, Opportunities & Challenges

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

Extracting POIs for navigation based on analyzed user residentiality using SNS photos

This paper presents a novel POI (Point of Interest) extraction method based on the residency characteristics of SNS users. Our goal is to present SNS photos of extracted POIs with high visibility and high awareness for each user on their navigation routes. In our method, we first determine the residential region of each user using geo-tagged tweets and then extract POIs at the nonresidential locations by calculating the residential users' appearance frequency based on geo-tagged tweets. This allows us to present the SNS photos of the extracted POIs by each residency characteristic on the navigation routes

An EMG-based eating behaviour monitoring system with haptic feedback to promote mindful eating

Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a need for an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback. In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of such a system in combination with real time wristband haptic feedback to facilitate mindful eating. Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different classifiers. We demonstrated that eating behaviour can be automatically assessed accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants showing that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38)=58.243, p<0.001). These findings may have major implications for research in eating behaviour, providing key new insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes

Living Memory Home: Understanding Continuing Bond in the Digital Age through Backstage Grieving

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