Social events such as networking parties are excellent opportunities to expand one's social network and make new social ties, as well as simply have fun and enjoy oneself. With the emergence of pervasive technology, we now have the opportunity to discover face-to-face socializing behaviors of individual guests and support them in their socializing goals. Quantitative measures of socializing behaviors will be useful in diverse applications from social therapy to social event management. We propose a system that detects and analyzes group-level socializing behaviors to support real-time and ex post facto social applications deployed in real social event situations. In our approach, we exploit attendees' characteristics of socializing such as proximity and body orientation to determine social proximity among guests and subsequently detect socializing groups. We performed preliminary studies and multiple deployments of our system and example applications in real social networking parties. Evaluation shows the system can detect socializing groups accurately and support applications that satisfy the interests of guests and party hosts.

On school field trips, chaperoning teachers' foremost concern is the safety of the children, particularly ensuring that none of them go missing. However, they have limited attention resources and face many challenges in keeping track of their charges. We present RubberBand, an assistive application that helps alleviate the teacher's burden. Our approach adapts to diverse field trip environmental and child behavioral dynamicity, utilizing observations of the relative dispersion of children and their tendency to form sub-groups.

We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.

We demonstrate a pervasive social exergame platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. Also, we show the potential of using multiple exercise devices as game controllers and incorporating multiple heterogeneous controllers into a game. Specifically, we consider a class of exercise equipment used for repetitive, individual, and aerobic (RIA) exercises such as treadmill running, stationary cycling, hula hooping, and jump roping.

Field trips in kindergarten imply excellent chances to attain a wide spectrum of educational clues for the children. However, in-depth observation on their exploratory behaviors is uniquely challenging. Teachers mostly take all possible precautions against any incidents, sparing little time and attention for observation. We collaborated with kindergarten teachers to develop a system for delegated observation of the children's exploratory behaviors by using smartphones and sensor technologies.

In this article, we present a serendipitous social network that makes use of individuals' contextual information to connect people in shared immediate situations. We build a prototype microblogging service that uses a serendipitous social network as a substrate. We performed an initial user study and found that users engaged in shared immediate situations are likely to exchange posts relevant to their situation that provide useful information or reflect emotional understanding.

A kindergarten is an interesting community of young children. The children continuously share their interactions and experiences, and grow along similar developmental stages. In this setting, studying relative differences among them can be an interesting approach to investigating how to help their individual and social development. In this study, we present our intuition on inter-child behavioral relativity and apply it to a real kindergarten environment. We conduct a close user study necessitating the monitoring of the children's behavior. Then, utilizing wearable sensor technologies, we perform a field study to explore various interesting aspects of behavioral relativity in an automatic and quantitative fashion. We consulted the kindergarten teachers with our results obtained from our field study in order to validate the practical benefits in the kindergarten environment. We further discuss the potential, limitations, and opportunities of our approach.

The key feature of many emerging pervasive computing applications is to proactively provide services to mobile individuals. One major challenge in providing users with proactive services lies in continuously monitoring users' context based on numerous sensors in their PAN/BAN environments. The context monitoring in such environments imposes heavy workloads on mobile devices and sensor nodes with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the mobile device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.

Mobile phones are truly personal devices loaded with personal data such as photos, contacts, and call history. Yet it is often necessary or desirable to share our phones with others. This is especially true as mobile phones are integrating features conventionally provided by other dedicated devices, from MP3 players to games consoles. Yet existing phones assume a single user and provide little protection for private data and applications when a phone is shared. That is, when we lend our phones to others, we give away complete access. In this work, we present xShare, a protection solution to address this problem. xShare allows phone owners to rapidly specify what they want to share and place the phone into a restricted mode where only the data and applications intended for sharing can be accessed. We first present two formative user studies and derive the design requirements of xShare. We then offer the design of xShare based on file-level access control. We describe the implementation of xShare on Windows Mobile and report a comprehensive evaluation, including performance measurements, usability, and a one-month field trial.

Loaded with personal data, e.g. photos, contacts, and call history, mobile phones are truly personal devices. Yet it is often necessary or desirable to share our phones with others. This is especially true as mobile phones are integrating features conventionally provided by other dedicated devices, from MP3 players to games consoles. Unfortunately, when we lend our phones to others, we give away complete access because existing phones assume a single user and provide little protection for private data and applications. In this work, we present xShare, a protection solution to address this problem. xShare allows phone owners to rapidly specify what they want to share and place the phone into a restricted mode where only the data and applications intended for sharing can be accessed.

Proactively providing services to mobile individuals is essential for emerging ubiquitous applications. The major challenge in providing users with proactive services lies in continuously monitoring their contexts based on numerous sensors. The context monitoring with rich sensors imposes heavy workloads on mobile devices with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.