Aging often comes with declining social interaction, a known adversarial factor impacting the life satisfaction of senior population. Such decline appears even in family–a permanent social circle, as their adult children eventually go independent. We present MomentMeld, an AI-powered, cloud-backed mobile application that blends with everyday routine and naturally encourages rich and frequent inter-generational interactions in a family, especially those between the senior generation and their adult children. Firstly, we design a photographic interaction aid called mutually stimulatory memento, which is a cross-generational juxtaposition of semantically related photos to bring natural arousal of context-specific inter-generational empathy and reminiscence. Secondly, we build comprehensive ensemble AI models consisting of various deep neural networks and a runtime system that automates the creation of mutually stimulatory memento on top of the user’s usual photo-taking routines. We deploy MomentMeld in-the-wild with six families for an eight-week period, and discuss the key findings and further implications.

Real-time remote interaction has become easier and richer powered by recent advances in mobile computing and communication. A number of research have been explored on enriching family interaction by augmenting an interaction channel with asynchronous communication [6] or additional sensory stimuli [5]. However, it is still far from achieving a sense of living together for family members involuntarily living apart, especially in context-aware impromptu interaction. For families living together, it is trivial to naturally perceive behavioral and situational contexts of the other and initiate a relevant interaction intuitively. For example, a wife starts a casual chat with asking her husband what he is going to cook when she sees him going to the kitchen or hears a simmering sound.

Continuous tracking young children's development is important for parents because early detection of developmental delay can lead to better treatment through early intervention. Screening tests, often based on questions answered by a parent, are used to assess children's development, but responses from only one parent can be subjective and even inaccurate due to limited memory and observations. In this work, we propose a collaborative child development tracking system, where screening test responses are collected through collaboration between parents or caregivers. We implement BebeCODE, a mobile system that encourages parents to independently answer all developmental questions for a given age and resolve disagreements through chatting, image/video sharing, or asking a third person. A 4-week deployment study of BebeCODE with 12 families found that parents had approximately 22% disagreements about questions regarding their children's developmental and BebeCODE helped them reach a consensus. Parents also reported that their awareness of their child's development, increased with BebeCODE.

People in work-separated families have been heavily relying on cutting-edge face-to-face communication services. Despite their ease of use and ubiquitous availability, experiences in living together are still far incomparable to those through remote face-to-face communication. We envision that enabling a remote person to be spatially superposed in one's living space would be a breakthrough to catalyze pseudo living-together interactivity. We propose HomeMeld, a zero-hassle self-mobile robotic system serving as a co-present avatar to create a persistent illusion of living together for those who are involuntarily living apart. The key challenges are 1) continuous spatial mapping between two heterogeneous floor plans and 2) navigating the robotic avatar to reflect the other's presence in real time under the limited maneuverability of the robot. We devise a notion of functionally equivalent location and orientation to translate a person's presence into another in a heterogeneous floor plan. We also develop predictive path warping to seamlessly synchronize the presence of the other. We conducted extensive experiments and deployment studies with real participants.

No abstract available.

We introduce Zaturi, a system enabling parents to create an audio book for their babies by utilizing micro spare time at work. We define micro spare time at work as tiny fragments of time with low cognitive loads that frequently occur at work, such as waiting for an elevator. We show that putting together micro spare time at work helps a working parent (1) build a tangible symbol conveying his/her thoughts to the beloved baby and (2) develop his/her own feelings of parental achievement without compromising regular working hours. Zaturi lets the parent immediately be aware of micro spare time and provides a crafted interface to seamlessly record the book piece by piece, so that the baby can enjoy listening to the book recorded in the parent's own voice. Through an extensive design process, we characterize the notion of micro spare time and build a working prototype of Zaturi. We also report parents' perceptions and family reactions after a two-week deployment.

Research shows the critical role of social relationships in behavior change, and the advancement of mobile technologies brings new opportunities of using online social support for persuasive applications. In this paper, we propose Relational Norm Intervention (RNI) model for behavior change, which involves two individuals as a target user and a helper respectively. RNI model uses Negative Reinforcement and Other-Regarding Preferences as motivating factors for behavior change. The model features the passive participation of a helper who will undergo artificially generated discomforts (e.g., limited access to a mobile device) when a target user performs against a target behavior. Based on in-depth discussions from a two-phase design workshop, we designed and implemented BeUpright, a mobile application employing RNI model to correct sitting posture of a target user. Also, we conducted a two-week study to evaluate the effectiveness and user experience of BeUpright. The study showed that RNI model has a potential to increase efficacy, in terms of behavior change, compared to conventional notification approaches. The most influential factor of RNI model in the changing the behavior of target users was the intention to avoid discomforting their helpers. RNI model also showed a potential to help unmotivated individuals in behavior change. We discuss the mechanism of RNI model in relation to prior literature on behavior change and implications of exploiting discomfort in mobile behavior change services.

Knowing individuals' relational orientation is imperative for effective offline, as well as online, interactions and collaborations. We use attachment theory to examine the link between Facebook users' relational orientation (in terms of attachment styles: anxiety and avoidance) and their relational activities. Our research examines whether and how the two key relational processes identified in offline social relationships (self-expression and responsiveness) are manifested on online social networks and related to attachment styles. We describe our dataset of 640 Facebook users, their attachment scale survey results, and their 525,334 posts. We define four features that map onto relational activities on Facebook: status updates and status updates with emotional words (self-expression); comments and likes (responsiveness). We find significant relationships between the users' attachment styles and their self-expression and responsiveness activities on Facebook. A key takeaway of our research is that without relying on self-reported surveys, a computational analysis of a Facebook user's self-expressing and responding activities alone can reveal the user's underlying relational orientation (i.e., attachment style).

Many theories and empirical research show the critical role of social relationships in shaping human behavior. Mobile and persuasive technologies present increased opportunities for affecting people's behavior. We propose the relational intervention model, which provokes a paired helper when the user performs a target behavior. In this demo, we present BeUpright, a posture-correction mobile application based on the relational intervention model.