Upper-limb impairments due to stroke can severely affect the quality of life in patients. Scientific evidence supports that repetitive rehabilitation exercises can improve motor ability in stroke patients. Rehabilitation games gained tremendous interest among researchers and clinicians because of their potential to make the seemingly mundane, enduring rehabilitation therapies more engaging. However, routine and longitudinal use of rehabilitation games in real-world clinical settings has not been investigated in depth. Particularly, we know little about current practices, challenges, and their potential impacts on therapeutic outcomes. To address this gap, we established a partnership with a rehabilitation hospital where game-assisted rehabilitation was routinely employed over a 2-year period. We then conducted an observational study, in which we observed 11 game-assisted therapy sessions and interviewed 15 therapists who moderated the therapy. Significant findings include (1) different engagement patterns of stroke patients in game-assisted therapy, (2) imperative roles of therapists in moderating games and challenges that therapists face during game-assisted therapy, and (3) lack of support for therapists in delivering patient-centered, personalized therapy to individual stroke patients. Furthermore, we discuss design implications for more effective rehabilitation game therapies that take into consideration both patients and therapists and their specific needs.

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.

In the U.S., people spend approximately 90 percent of their time indoors. Unfortunately, indoor air quality (IAQ) may be two to five times worse than the air outdoors, and is often overlooked. Existing IAQ monitoring technologies focus on IAQ measurements and visualization. However, the lack of information about the pollution sources as well as the seriousness of the pollution makes people feel powerless and frustrated, resulting in the ignorance of the polluted air at their homes. In this work, we fill this critical gap by presenting AirSense, an intelligent home-based IAQ sensing system that is able to automatically detect pollution events, identify pollution sources, estimate personal exposure to indoor air pollution, and provide actionable suggestions to help people improve IAQ. We have deployed AirSense at five homes to evaluate its performance and investigate how users interact with it. We demonstrate that AirSense can accurately detect pollution events, identify pollution sources, and forecast IAQ information within five minutes in both controlled and real-world settings. We further show the great potential of AirSense in increasing users' awareness of IAQ and helping them better manage IAQ at their homes.

The unique aquatic nature of swimming makes it difficult to use social or technical strategies to mitigate the tediousness of monotonous exercises. In this study, we propose the use of a smartphone-based multiplayer exergame named MobyDick . MobyDick is designed to be played while swimming, where a team of swimmers collaborate to hunt down a virtual monster. To this end, we take into account both human factors and technical challenges under swimming contexts. First, we perform a comparative analysis of a variety of wireless networking technologies in the aquatic environment and identify various technical constraints on wireless networking. Second, we develop a swimming activity recognition system to enable precise and real-time game inputs. Third, we devise a multiplayer game design by employing the unique interaction mode viable in an underwater environment, where the abilities of human communication are highly limited. Finally, we prototype MobyDick on waterproof off-the-shelf Android phones, and we deploy it in real swimming pool environments ( n = 8). Our qualitative analysis of user interview data reveals certain unique aspects of multiplayer swimming games.

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.

Exergame controllers are intended to add fun to monotonous exercise. However, studies on exergame controllers mostly focus on designing new controllers and exploring specific application domains without analyzing human factors, such as performance, comfort, and effort. In this paper, we examine the characteristics of a speed-based exergame controller that bear on human factors related to body movement and exercise. Users performed tasks such as changing and maintaining exercise speed for avatar control while their performance was measured. The exergame controller follows Fitts' law, but requires longer movement time than a gamepad and Wiimote. As well, resistance force and target speed affect performance. User experience data confirm that the comfort and mental effort are adequate as practical game controllers. The paper concludes with discussion on applying our findings to practical exergame design.

Finding a missing child is an important problem concerning not only parents but also our society. It is essential and natural to use serendipitous clues from neighbors for finding a missing child. In this paper, we explore a new architecture of crowd collaboration to expedite this mission-critical process and propose a crowd-sourced cooperative mobile application, CoSMiC. It helps parents find their missing child quickly on the spot before he or she completely disappears. A key idea lies in constructing the location history of a child via crowd participation, thereby leading parents to their child easily and quickly. We implement a prototype application and conduct extensive user studies to assess the design of the application and investigate its potential for practical use.

The unique aquatic nature of swimming makes it very difficult to use social or technical strategies to mitigate the tediousness of monotonous exercises. In this study, we propose MobyDick, a smartphone-based multi-player exergame designed to be used while swimming, in which a team of swimmers collaborate to hunt down a virtual monster. In this paper, we present a novel, holistic game design that takes into account both human factors and technical challenges. Firstly, we perform a comparative analysis of a variety of wireless networking technologies in the aquatic environment and identify various technical constraints on wireless networking. Secondly, we develop a single phone-based inertial and barometric stroke activity recognition system to enable precise, real-time game inputs. Thirdly, we carefully devise a multi-player interaction mode viable in the underwater environment highly limiting the abilities of human communication. Finally, we prototype MobyDick on waterproof off-the-shelf Android phones, and deploy it to real swimming pool environments (n = 8). Our qualitative analysis of user interview data reveals certain unique aspects of multi-player swimming games.

Social exergames provide immersive experiences of social interaction via online multiplayer games, ranging from simple group exercises (e.g., virtual cycling/rowing) to more structured multiplayer games (e.g., cooperative boat racing). In exergame design, interpersonal synchrony plays an important role as it enhances social rapport and pro-social behavior. In this paper, we build ExerSync platform that supports various assistive mechanisms for facilitating interpersonal synchrony even with heterogeneous exercise modalities. We consider a rhythm of body movements in repetitive aerobic exercises and explore the design space of incorporating rhythm into exergames. We build a prototype system and comparatively evaluate the effectiveness of various assistive mechanisms. The experiment results show that rhythm significantly lowers the perceived workloads and provides better competence and engagement, but the accuracy of interpersonal synchrony is not dependent on the use of rhythm.

This paper discusses an approach for transforming solitary exercises into social exergames. We frame our discussion by highlighting the relation between the original exercises and game interactions, and by analyzing an example exergame which is successfully transformed from its original solitary exercise. We present a user study of the exergame that evaluates the need for holistic transformation strategies from solitary exercises into social exergames.

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.

We demonstrate PosFree, a platform supporting posture-free motion-based mobile games. It allows the developers to support diverse postural gameplay contexts in the motion-based mobile games, without much knowledge on the specific characteristics of motion sensors with respect to posture changes. It detects orientation of mobile devices at runtime and automatically converts motion sensor data as if the devices are manipulated in the standard posture, as well as provides an intuitive user interface to help players understand their postural gameplay contexts. The platform prototype is currently implemented inside the Android Framework, and thereby off-the-shelf motion-based mobile games can utilize the feature of PosFree without modifying their original source codes.

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.

We demonstrate E-Gesture, a collaborative architecture for energy-efficient gesture recognition on a hand-worn sensor device and an off-the-shelf smartphone that greatly reduces energy consumption while achieving high accuracy recognition under dynamic mobile situations. E-gesture employs a novel gesture segmentation and classification architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing energy consumption characteristics in both sensor and smartphone.

Gesture is a promising mobile User Interface modality that enables eyes-free interaction without stopping or impeding movement. In this paper, we present the design, implementation, and evaluation of E-Gesture, an energy-efficient gesture recognition system using a hand-worn sensor device and a smartphone. E-gesture employs a novel gesture recognition architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing the energy consumption characteristics of both sensors and smartphones. We developed a closed-loop collaborative segmentation architecture, that can (1) be implemented in resource-scarce sensor devices, (2) adaptively turn off power-hungry motion sensors without compromising recognition accuracy, and (3) reduce false segmentations generated from dynamic changes of body movement. We also developed a mobile gesture classification architecture for smartphones that enables HMM-based classification models to better fit multiple mobility situations.

In this paper, we present Orchestrator, an active resource orchestration framework for mobile context monitoring. Emerging pervasive environments will introduce a PAN-scale sensor-rich mobile platform consisting of a mobile device and many wearable and space-embedded sensors. In such environments, it is challenging to enable multiple context-aware applications requiring continuous context monitoring to simultaneously run and share highly scarce and dynamic resources. Orchestrator enables multiple applications to effectively share the resources while exploiting the full capacity of overall system resources and providing high-quality service to users. For effective orchestration, we propose an active resource use orchestration approach that actively finds appropriate resource uses for applications and flexibly utilizes them depending on dynamic system conditions. Orchestrator is built upon a prototype platform that consists of off-the-shelf mobile devices and sensor motes. We present the detailed design, implementation, and evaluation of Orchestrator. The evaluation results show that Orchestrator enables applications in a resource-efficient way.

We designed and implemented a pervasive game called Swan Boat that targets the bland and tedious nature of running on a treadmill, making it fun through social interaction and immersive game play. We developed Swan Boat on top of PSD, a platform for pervasive games, and using the Interactive Treadmill hardware. We conducted a user study to evaluate our game.

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.