Advances in ubiquitous and wearable sensing and HCI research have made stress monitoring increasingly accessible, enabling the development of personalized stress management technologies. Yet, stress is a subjective and contextual experience, making effective intervention design challenging. Prior studies often isolate stress detection or intervention, without providing an integrated view of how these components connect and are evaluated in real-world use. To address this gap, we conducted a systematic review of 2,152 papers and selected 52 empirical studies where stress tracking informed interventions. Using a framework based on three stress constructs (subjective stress, psycho-physiological stress, and exposure stress), we analyzed how definitions of stress shape detection indicators, intervention design and timing, and evaluation methods. We show that stress conceptualization strongly influences system design, and we propose a conceptual framework linking detection, intervention, and evaluation to guide future user-centered stress management technologies.

Mobile phone numbers function as single keys to banking, government, and commerce, making the Subscriber Identity Module (SIM) a critical element of security. In April 2025, South Korea’s largest carrier experienced a SIM breach that compromised authentication keys and exposed nearly 27 million subscriber identifiers. We conducted semi-structured interviews with mental-model elicitation (N = 33) to examine user awareness, responses, and understanding of SIM-based authentication. Results reveal a pronounced awareness–action gap: participants recognized the breach yet held incomplete mental models, perceived little personal risk, and rarely acted protectively, even when affected. Learned helplessness, reliance on carriers, and the invisibility of SIM shaped these passive responses. Brief educational interventions improved conceptual understanding but seldom produced lasting behavioral change. Our findings demonstrate how technical opacity and psychological factors jointly inhibit protective action and offer design implications for usable security, emphasizing interventions that realign users’ mental models with system risks to foster sustainable practices.

Call agents, a representative group of emotion workers, must manage emotions under constrained autonomy, yet workplace stress sensing has primarily centered on knowledge work. We ask how the task‑aligned cycle of emotional labor, alternating customer interaction (CI) and non‑customer interaction (nCI), shapes stress and how it manifests in data. We conducted a month-long in-the-wild formative mixed-methods study with professional call agents, collecting structured task logs, environmental and behavioral signals, and per-call stress self-reports, followed by semi-structured interviews. Task logs, used as a new sensor modality, were incorporated as primary sensing signals, and task-related features were extracted by respecting CI boundaries for modeling. Our results showed that a short 5-minute windowing approach was comparable to task-aligned windowing using multimodal sensors, with task-related features being considered the most important across all generalized models. Personalized models improved further and shifted importance toward diverse data sources, revealing individual differences in preparation patterns. Interviews support those findings, reveal key modelling challenges, and highlight potential benefits of semi-automated self-tracking. We discuss implications for timing interventions at breakpoints suited for work patterns, and ethically deploying stress support for emotion workers.

While family informatics has been developed for monitoring and tracking family-centered health data, there remains a gap in understanding how family informatics can support families in reflecting on their social behaviors and emotional dynamics. We address this gap with SELaD, a system that captures and visualizes social-emotional data from daily family interactions using audio, video, and physiological sensors. In a semi-naturalistic study with 17 families (n = 51), we investigated how this data facilitates reflection. Our findings reveal a process we term relational reflection, where families collaboratively interpret multimodal data to deepen their understanding of conversational dynamics and emotional influences by recalling their shared history and expectation of good communication. This process was particularly enriched by emotional data from multiple sources that families could cross-reference and reconcile. This work presents SELaD as a technology probe and empirically grounds the concept of relational reflection, positioning it as a foundation for designing future reflective technologies.

The growing demand for mental health support has highlighted the limitations of traditional counseling accessibility, increasing the usage of digital mental health interventions. There has been a rising interest in using robots to support mental health due to their benefits in engagement and rapport. Capitalizing on the opportunity of placemaking for designing a feasible robotic digital mental health intervention, our study explores the Robot Counseling Center (RCC) and its robotic counselor, Cumpa, designed to improve mental health accessibility and user engagement. A two-week field study with 20 participants evaluated RCC's impact on their mental health, engagement, and sense of place within a counseling environment. Results indicate that RCC positively influences emotional awareness and engagement. Our findings provide insights into the role of social robots in mental health interventions and offer design implications for developing robotic counseling centers as supportive, effective spaces, contributing to building better places and interactive systems.

Hate speech remains a persistent and unresolved challenge in online platforms. Content moderators, working on the front lines to review user-generated content and shield viewers from hate speech, often find themselves unprotected from the mental burden as they continuously engage with offensive language. To safeguard moderators' mental well-being, we designed HateBuffer, which anonymizes targets of hate speech, paraphrases offensive expressions into less offensive forms, and shows the original expressions when moderators opt to see them. Our user study with 80 participants consisted of a simulated hate speech moderation task set on a fictional news platform, followed by semi-structured interviews. Although participants rated the hate severity of comments lower while using HateBuffer, contrary to our expectations, they did not experience improved emotion or reduced fatigue compared with the control group. In interviews, however, participants described HateBuffer as an effective buffer against emotional contagion and the normalization of biased opinions in hate speech. Notably, HateBuffer did not compromise moderation accuracy and even contributed to a slight increase in recall. We explore possible explanations for the discrepancy between the perceived benefits of HateBuffer and its measured impact on mental well-being. We also underscore the promise of text-based content modification techniques as tools for a healthier content moderation environment.

Personal informatics (PI) systems are widely used in various domains such as mental health to provide insights from self-tracking data for behavior change. Users are highly interested in examining relationships from the self-tracking data, but identifying causality is still considered challenging. In this study, we design DeepStress, a PI system that helps users analyze contextual factors causally related to stress. DeepStress leverages a quasi-experimental approach to address potential biases related to confounding factors. To explore the user experience of DeepStress, we conducted a user study and a follow-up diary study using participants’ own self-tracking data collected for 6 weeks. Our results show that DeepStress helps users consider multiple contexts when investigating causalities and use the results to manage their stress in everyday life. We discuss design implications for causality support in PI systems.

In this paper, we investigate the challenges users face with a user-centric context-aware intervention system. Users often face gaps when the system’s responses do not align with their goals and intentions. We explore these gaps through a prototype system that enables users to specify context-action intervention rules as they desire. We conducted a lab study to understand how users perceive and cope with gaps while translating their intentions as rules, revealing that users experience context-mapping and context-recognition uncertainties (instant evaluation cycle). We also performed a field study to explore how users perceive gaps and make adaptations of rules when the operation of specified rules in real-world settings (delayed evaluation cycle). This research highlights the dynamic nature of user interaction with context-aware systems and suggests the potential of such systems in supporting digital well-being. It provides insights into user adaptation processes and offers guidance for designing user-centric context-aware applications.

Significant investment of time and effort for language learning has prompted a growing interest in microlearning. While microlearning requires frequent participation in 3-to-10-minute learning sessions, the recent widespread of smart speakers in homes presents an opportunity to expand learning opportunities by proactively providing microlearning in daily life. However, such proactive provision can distract users. Despite the extensive research on proactive smart speakers and their opportune moments for proactive interactions, our understanding of opportune moments for more-than-one-minute interactions remains limited. This study aims to understand user perceptions and opportune moments for more-than-one-minute microlearning using proactive smart speakers at home. We first developed a proactive microlearning service through six pilot studies (n=29), and then conducted a three-week field study (n=28). We identified the key contextual factors relevant to opportune moments for microlearning of various durations, and discussed the design implications for proactive conversational microlearning services at home.

With increased interest in leveraging personal data collected from 24/7 mobile sensing for digital healthcare research, supporting user-friendly consent to data collection for user privacy has also become important. This work proposes PriviAware, a mobile app that promotes flexible user consent to data collection with data exploration and contextual filters that enable users to turn off data collection based on time and places that are considered privacy-sensitive. We conducted a user study (N = 58) to explore how users leverage data exploration and contextual filter functions to explore and manage their data and whether our system design helped users mitigate their privacy concerns. Our findings indicate that offering fine-grained control is a promising approach to raising users’ privacy awareness under the dynamic nature of the pervasive sensing context. We provide practical privacy-by-design guidelines for mobile sensing research.

In public health and safety, precise detection of blood alcohol concentration (BAC) plays a critical role in implementing responsive interventions that can save lives. While previous research has primarily focused on computer-based or neuropsychological tests for BAC identification, the potential use of daily smartphone activities for BAC detection in real-life scenarios remains largely unexplored. Drawing inspiration from Instrumental Activities of Daily Living (I-ADL), our hypothesis suggests that Smartphone-based Activities of Daily Living (S-ADL) can serve as a viable method for identifying BAC. In our proof-of-concept study, we propose, design, and assess the feasibility of using S-ADLs to detect BAC in a scenario-based controlled laboratory experiment involving 40 young adults. In this study, we identify key S-ADL metrics, such as delayed texting in SMS, site searching, and finance management, that significantly contribute to BAC detection (with an AUC-ROC and accuracy of 81%). We further discuss potential real-life applications of the proposed BAC model.

People with mental health issues often stay indoors, reducing their outdoor activities. This situation emphasizes the need for self-tracking technology in homes for mental health research, offering insights into their daily lives and potentially improving care. This study leverages a multimodal smart speaker to design a proactive self-tracking research system that delivers mental health surveys using an experience sampling method (ESM). Our system determines ESM delivery timing by detecting user context transitions and allowing users to answer surveys through voice dialogues or touch interactions. Furthermore, we explored the user experience of a proactive self-tracking system by conducting a four-week field study (n=20). Our results show that context transition-based ESM delivery can increase user compliance. Participants preferred touch interactions to voice commands, and the modality selection varied depending on the user’s immediate activity context. We explored the design implications for home-based, context-aware self-tracking with multimodal speakers, focusing on practical applications.

Recent advances in wearable technology have led to the development of various methods for stress sensing in both controlled laboratory and real-life environments. However, existing methods often rely on specialized or expensive sensors that may not be easily accessible to the general population. In this study, we investigate the feasibility of using off-the-shelf smartwatches for stress detection in real-life scenarios. To achieve this, we propose SOSW, a comprehensive methodology for robust sensor data processing by considering both physiological and contextual data. SOSW employs a two-layer machine learning (ML) architecture. The first-layer ML model is trained and validated using carefully collected data under controlled laboratory conditions. The second-layer ML model is trained and validated using data collected in real-life settings. We conducted evaluations with 26 and 18 participants in controlled laboratory and real-life conditions, respectively. The results indicate that our methodology can successfully detect stressful events with an F-1 score of up to 0.84 in laboratory conditions and 0.71 in real-life scenarios using off-the-shelf smartwatches. The results are comparable to those achieved by the state of the art methods that rely on dedicated wearables.

This work presents FamilyScope, a sensor-based family informatics system that enables reflection upon family data collected from family activity scenarios (e.g., game playing and movie watching) that include affective aspects of a family's social interactions. We conducted a user study with ten families (n=30) in a smart home testbed to observe how our system supports data reflection of the affective and behavioral states among family members. Our findings showed that FamilyScope facilitated family data reflection on affective and behavioral aspects of family interactions. Overall, families reported that the system well reflected family members' general tendencies in terms of affective and behavioral responses and even helped them gain new insights about each other. Based on the findings, we provide practical design approaches for collective reflection in family informatics systems.

A semi-automated manufacturing system that entails human intervention in the middle of the process is a representative collaborative system that requires active interaction between humans and machines. User behavior induced by the operator’s decision-making process greatly impacts system operation and performance in such an environment that requires human-machine collaboration. There has been room for utilizing machine-generated data for a fine-grained understanding of the relationship between the behavior and performance of operators in the industrial domain, while multiple streams of data have been collected from manufacturing machines. In this study, we propose a large-scale data-analysis methodology that comprises data contextualization and performance modeling to understand the relationship between operator behavior and performance. For a case study, we collected machine-generated data over 6-months periods from a highly automated machine in a large tire manufacturing facility. We devised a set of metrics consisting of six human-machine interaction factors and four work environment factors as independent variables, and three performance factors as dependent variables. Our modeling results reveal that the performance variations can be explained by the interaction and work environment factors (R2 = 0.502, 0.356, and 0.500 for the three performance factors, respectively). Finally, we discuss future research directions for the realization of context-aware computing in semi-automated systems by leveraging machine-generated data as a new modality in human-machine collaboration.

Conversational agents are widely used in today's multi-channel service environments. However, little is known regarding the challenges faced by user experience (UX) designers. This study explores the challenges faced by conversational UX designers working in multidisciplinary teams for understanding the design process involved in the transformation of conventional graphical interfaces to conversation flows. In-depth interviews with UX designers working in industries reveal the key challenges in the form-to-flow transformation process. Moreover, collaborative work with various stakeholders in complex work environments involves conversational artificial intelligence-engendered gap-filling work phenomena wherein UX designers tend to work across role boundaries. Our results indicate the need for added support for CUX designer including tools and guidelines to suppport form-to-flow design, tools for testing, and defining extended roles for CUX designers with collaborative perspectives for designing conversational agents in multi-channel service environments.

Socially assistive robots can support people in making behavior changes by socially engaging in or moderating certain behaviors, such as physical exercise and snacking. However, there has not been much work on designing social robots that aim to support both social engagement and behavior moderation, i.e., offering social interactions for engaging in behaviors without over-engagement. This work explores how social robots can moderate alcohol consumption while socially engaging them in a solitary drinking context. As alcohol consumption can have benefits when done in moderation, this companion robot aims to guide the user toward moderate drinking by using social engagement (i.e., creating an enjoyable atmosphere) and drinking moderation (i.e., regulating the drinking pace). Our preliminary user study (n=20) reveals that the robot is perceived as a friendly companion, and its human-likeness is partly attributed to the robot's intervention. Most participants followed the robot's guidance and perceived it as an intelligent friend due to its social interactions and behavior tracking features. We discuss the benefit of physical interactions for social engagement, utilizing interaction rituals for enjoyable but moderate commensality, and ethical considerations in solitary drinking contexts.

Many digital wellbeing tools help users monitor and control social media use on their smartphones by tracking and setting limits on their usage time. Tracking is typically done at the granularity of phone- or app-level; however, recent social media apps provide various features such as direct messaging, comment reading/posting, and content uploading/viewing. While it is possible to track and analyze within-app feature usage, little is known about the effect of granularity on smartphone interventions. We designed and developed FinerMe to explore how the granularity of interventions (app-level vs. feature-level) affects the usage of popular social media such as Instagram and YouTube on smartphones. We conducted a field study with 56 participants over 16 days that consisted of three phases: baseline collection, self-reflection, and self-reflection with restrictive interventions. The results showed that while both app-level and feature-level interventions similarly reduced social media use, feature-level interventions enabled users to spend less time on passive app features related to content consumption (e.g., following feed on Instagram, and viewing comments on YouTube) than app-level interventions. Moreover, when self-reflection is combined with restrictive interventions at the feature-level, users were more reflective on their usage behavior than when done at the app-level.

The automated recognition of human emotions plays an important role in developing machines with emotional intelligence. Major research efforts are dedicated to the development of emotion recognition methods. However, most of the affective computing models are based on images, audio, videos and brain signals. Literature lacks works that focus on utilizing only peripheral signals for emotion recognition (ER), which can be ideally implemented in daily life settings. Therefore, this paper present a framework for ER on the arousal and valence space, based on using multi-modal peripheral signals. The data used in this work were collected during a debate between two people using wearable devices. The emotions of the participants were rated by multiple raters and converted into classes in correspondence to the arousal and valence space. The use of a dynamic threshold for ratings conversion was investigated. An ER model is proposed that uses a Long Short-Term Memory (LSTM)-based architecture for classification. The model uses heart rate (HR), temperature (T), and electrodermal activity (EDA) signals as its inputs with emotional cues. Additionally, a post-processing prediction mechanism is introduced to enhance the recognition performance. The model is implemented to study the use of individual and different combinations of the peripheral signals, as well as utilizing annotations from different ratings. Additionally, it is employed for classification of valence and arousal in an independent and combined fashion, under subject dependent and independent scenarios. The experimental results have justified the efficient performance of the proposed framework, achieving classification accuracy 96% and 93% for the independent and combined classification scenarios, accordingly. The comparison of the achieved performance against the baseline methods shows the superiority of the proposed framework and the ability to recognize arousal-valance levels with high accuracy from peripheral signals, in real-life scenarios.

YouTube is a space where people with disabilities can reach a wider online audience to present what it is like to have disabilities. Thus, it is imperative to understand how content creators with disabilities strategically interact with algorithms to draw viewers around the world. However, considering that the algorithm carries the risk of making less inclusive decisions for users with disabilities, whether the current algorithmic experiences (AXs) on video platforms is inclusive for creators with disabilities is an open question. To address that, we conducted semi-structured interviews with eight YouTubers with disabilities. We found that they aimed to inform the public of diverse representations of disabilities, which led them to work with algorithms by strategically portraying disability identities. However, they were disappointed that the way the algorithms work did not sufficiently support their goals. Based on findings, we suggest implications for designing inclusive AXs that could embrace creators’ subtle needs.

Mobile experience sampling methods (ESMs) are widely used to measure users’ affective states by randomly sending self-report requests. However, this random probing can interrupt users and adversely influence users’ emotional states by inducing disturbance and stress. This work aims to understand how ESMs themselves may compromise the validity of ESM responses and what contextual factors contribute to changes in emotions when users respond to ESMs. Towards this goal, we analyze 2,227 samples of the mobile ESM data collected from 78 participants. Our results show ESM interruptions positively or negatively affected users’ emotional states in at least 38% of ESMs, and the changes in emotions are closely related to the contexts users were in prior to ESMs. Finally, we discuss the implications of using the ESM and possible considerations for mitigating the variability in emotional responses in the context of mobile data collection for affective computing.

We demonstrate StressTrendmeter, a mobile app that targets college students for anonymously sharing the source of stress via the form of hashtags, viewing stress topics based on trends, and providing social support through the empathy button and hashtag-based chat.

The massive influx of mobile devices and their increasing use in recent years have resulted in the overprovision of access points (APs) in networks. Unlike in residential environments, network administrators in enterprises and universities make every endeavor to enhance the user experience (UX) of WiFi networks where the network dynamics (e.g., traffic load and user mobility) are usually unexpected. To this end, an existing mechanism for WiFi association is client driven, i.e., users associate themselves to the AP with higher signal strength. However, they still incur dissatisfaction due to the insufficient available bandwidth. To cope with this in a centralized manner, we propose DARCAS, a software-defined network (SDN)-enabled WiFi framework for association regulation. DARCAS adopts a notion of bandwidth satisfaction ratio (BSR), which is closely related to UX. It maximizes the aggregated network throughput while satisfying the BSR of each user with sufficient airtime (i.e., channel occupancy time) provision. We use this idea in a metaheuristic genetic algorithm called DARCAS-GA, which effectively finds the suboptimal association distribution of the maximum BSR in polynomial time. We implement the DARCAS system on off-the-shelf wireless routers and an SDN controller. We report real-life experimental results in the considered scenarios and conduct extensive simulations on the NS-3 simulator to examine its performance with scalability. With fine-tuned settings, DARCAS exhibits up to 80% of the BSR gain compared to existing solutions.

People experience various stressful events in their daily lives. Receiving social support, especially from peers who went through a similar experience, helps individuals cope with such stress. We propose StressTrendmeter, a mobile application that targets college students for anonymously sharing the source of stress via the form of hashtags, viewing stress topics based on trends, and providing social support through the empathy button and hashtag-based chat. We deployed StressTrendmeter to 222 students from two universities for five weeks. With hashtags and trending features, students found StressTrendmeter (i)helpful to spontaneously yet concisely articulate their stress topics and (ii) easy to browse through and become aware of issues around the campus. Our study reveals that social sharing with StressTrendmeter brought awareness, resonance, and accountability as students empathized and expressed support. Based on our study, we share design implications for social support systems with community awareness.

Techno-spiritual practices refer to the use of digital technologies to support various spiritual activities, such as scripture reading. While prior human-computer interaction studies largely focus on understanding techno-spiritual practices in both personal and ministry environments, there is a lack of design research that explores novel design opportunities, based on longitudinal field deployment. As an important techno-spiritual practice, this work focuses on scripture reading and investigates the design space of "social scripture reading," as it is often organized into small groups for successful behavior maintenance. We designed and evaluated BibleCell, a social scripture reading tool that supports personalized reading plans, scripture reading, and social sharing. After the third year of deployment, we performed a two-month user study in Korean Protestant churches to deepen our understanding of techno-spiritual practices in social contexts via in-depth interviews (n = 27). We report the major themes of social techno-spiritual practices, such as social motivators, social interaction patterns, and leadership roles. We discuss our findings using a novel design concept of social-spiritual awareness that considers both the social and spiritual aspects of interactions in social computing systems.

Long-term, continuous physiological recordings are currently being intensely investigated for tracking emotions. Emotional valence has been of more interest due to its relevance to cardiac and neurophysiological disease. In this research, multiple configurable convolutional neural networks (CNNs) were developed for different image-encoding techniques used as their input. Ensemble classification was then used to achieve a combined performance of the multiple CNNs by training a simple support vector machine (SVM) classifier using the last output layers of the CNNs as its input. Valence-labelled signals from the heart rate (HR) recorded using a wearable sensor from a wristband in a daily setting for one week from 80 participants were used for the image transforms. Accuracies of more than 91% were achieved with the classification ensembling, showing an improvement of the binary classification of emotional valence by more than 19% compared to using CNNs on their own.

Traditional goal setting simply assumes a binary outcome for goal evaluation. This binary judgment does not consider a user’s effort, which may demotivate the user. This work explores the possibility of mitigating this negative impact with a slight modification on the goal evaluation criterion, by introducing a ‘margin’ that is widely used for quality control in the manufacturing fields. A margin represents a range near the goal where the user’s outcome will be regarded as ‘good enough’ even if the user fails to reach it. We explore users’ perceptions and behaviors through a large-scale survey study and a small-scale field experiment using a coaching system to promote physical activity. Our results provide positive evidence on the margin, such as lowering the burden of goal achievement and increasing motivation to make attempts. We discuss practical design implications on margin-enabled goal setting and evaluation for behavioral change support systems.

A commitment device, an attempt to bind oneself for a successful goal achievement, has been used as an effective strategy to promote behavior change. However, little is known about how commitment devices are used in the wild, and what aspects of commitment devices are related to goal achievements. In this paper, we explore a large-scale dataset from stickK, an online behavior change support system that provides both financial and social commitments. We characterize the patterns of behavior change goals (e.g., topics and commitment setting) and then perform a series of multilevel regression analyses on goal achievements. Our results reveal that successful goal achievements are largely dependent on the configuration of financial and social commitment devices, and a mixed commitment setting is considered beneficial. We discuss how our findings could inform the design of effective commitment devices, and how large-scale data can be leveraged to support data-driven goal elicitation and customization.

User-driven intervention tools such as self-tracking help users to self-regulate problematic smartphone usage. These tools basically assume active user engagement, but prior studies warned a lack of user engagement over time. This paper proposes GoldenTime, a mobile app that promotes self-regulated usage behavior via system-driven proactive timeboxing and micro-financial incentives framed as gain or loss for behavioral reinforcement. We conducted a large-scale user study (n = 210) to explore how our proactive timeboxing and micro-financial incentives influence users’ smartphone usage behaviors. Our findings show that GoldenTime’s timeboxing based micro-financial incentives are effective in self-regulating smartphone usage, and incentive framing has a significant impact on user behavior. We provide practical design guidelines for persuasive technology design related to promoting digital wellbeing.

Subjective well-being (SWB) is a well-studied, widely used construct that refers to how people feel and think about their lives as one of many comprehensive perspectives on well-being. Much research has analyzed the role and utilization of technologies to improve one's SWB; however, especially when it comes to user modeling, multifaceted and variational aspects of SWB are less frequently considered. This paper presents an analysis on identifying factors for smartphone-based data on SWB and modeling SWB changes, based on a four-month user study with 78 college students. Our regression analysis highlights the significance of user attributes (e.g., personality, self-esteem) on SWB and salient factors derived from smartphone data (e.g., time spent on campus, ratio of standing/sitting stationary, expenses) that significantly account for SWB. Our classification analysis shows the potential for detecting SWB changes with reasonable performance, as well as for improving a model to be more tailored to individuals.

Smartwatches have gained significant popularity in recent years. One major use of smartwatches is notification checking as an extended display. Smartwatch use provides an opportunity for energy saving because it affords a reduction in the frequency of smartphone use. However, sometimes user input is limited in smartwatches due to its small screen size and users are forced to use their smartphones to fully read notifications. In such cases, the advantage of an extended display in the smartwatch diminishes because users need to access their smartphones, which causes extra smartphone battery consumption. We define phone-preferable notification that requires a user to take further actions, such as checking detailed content and replying to a message. Given that phone-preferable notifications are likely to be handled on smartphones, it is possible to defer notification delivery to smartwatches. In this paper, we develop a novel notification manager, called PASS that automatically defers phone-preferable notifications and piggybacks them on watch-preferable notifications. For model building and evaluation, we collect 15,659 notifications in-the-wild from 11 users for approximately 31 days. In addition, for approximately two months we gather self-reported data from five users regarding which devices were used to respond to notifications. The results show that PASS can save daily battery for smartwatches and daily battery for smartphones up to 43.5 and 0.9 percent without introducing any noticeable negative results on user experiences.

Instant access and gratification make it difficult for us to self-limit the use of smartphone apps. We hypothesize that a slight increase in the interaction cost of accessing an app could successfully discourage app use. We propose a proactive intervention that requests users to perform a simple lockout task (e.g., typing a fixed length number) whenever a target app is launched. We investigate how a lockout task with varying workloads (i.e., pause only without number input, 10-digit input, and 30-digit input) influence a user's decision making, by a 3-week, in-situ experiment with 40 participants. Our findings show that even the pause-only task that requires a user to press a button to proceed discouraged an average of 13.1% of app use, and the 30-digit-input task discouraged 47.5%. We derived determinants of app use and non-use decision making for a given lockout task. We further provide implications for persuasive technology design for discouraging undesired behaviors.

Prolonged static and unbalanced sitting postures during computer usage contribute to musculoskeletal discomfort. In this paper, we investigated the use of a very slow moving monitor for unobtrusive posture correction. In a first study, we identified display velocities below the perception threshold and observed how users (without being aware) responded by gradually following the monitor's motion. From the result, we designed a robotic monitor that moves imperceptible to counterbalance unbalanced sitting postures and induces posture correction. In an evaluation study (n=12), we had participants work for four hours without and with our prototype (8 in total). Results showed that actuation increased the frequency of non-disruptive swift posture corrections and significantly reduced the duration of unbalanced sitting. Most users appreciated the monitor correcting their posture and reported less physical fatigue. With slow robots, we make the first step toward using actuated objects for unobtrusive behavioral changes.

Overheating smartphones could hamper user experiences. While there have been numerous reports on smartphone overheating, a systematic measurement and user experience study on the thermal aspect of smartphones is missing. Using thermal imaging cameras, we measure and analyze the temperatures of various smartphones running diverse application workloads such as voice calling, video recording, video chatting, and 3D online gaming. Our experiments show that running popular applications such as video chat, could raise the smartphone's surface temperature to over 50$^\circ$C in only 10 minutes, which could easily cause thermal pain to users. Recent ubiquitous scenarios such as augmented reality and mobile deep learning also have considerable thermal issues. We then perform a user study to examine when the users perceive heat discomfort from the smartphones and how they react to overheating. Most of our user study participants reported considerable thermal discomfort while playing a mobile game, and that overheating disrupted interaction flows. With this in mind, we devise a smartphone surface temperature prediction model, by using only system statistics and internal sensor values. Our evaluation showed high prediction accuracy with root-mean-square errors of less than 2$^\circ$C. We discuss several insights from our findings and recommendations for user experience, OS design, and developer support for better user-thermal interactions.

In recent years, large-scale data collection has become crucial in Human-Computer Interaction (HCI) research. With a sharp climb of the amount of data being gathered due to an increasing number of mobile and wearable devices, real-time maintenance of Data Quality (DQ) of data-collection campaigns has already become an overwhelming task, especially in large-scale experiments. This paper proposes EasyTrack, a platform that collects large-scale data in an automatized manners. We describe how our proposed solution detects and tackles issues in data collection campaigns in an automated manner.

Stickers, though similar in appearance to emoji, have distinct characteristics because they often contain animation, diverse gestures, and multiple characters and objects. Stickers can convey richer meaning than emoji, but their complexity and placement constraint may result in miscommunication. In this paper, we aim to understand how people perceive emotion in stickers, as well as how miscommunication related to sticker occurs in actual chat contexts. Toward this goal, we conducted an online survey (n = 87) and in-depth interviews (n = 28) in South Korea. We found emotional and contextual aspects of sticker misinterpretation. In particular, emotion misinterpretation mostly happened due to stickers' ambiguous (multiple) facial/bodily expressions and different perception of dynamism in gestures. In real chat settings, there were also contextual misinterpretations where senders and receivers differently interpret stickers' visual representation/reference, or/and corresponding textual messages. Based on these findings, we provide several practical design implications such as context awareness support in sticker interaction.

Community policing to collaboratively maintain community safety and order in conjunction with law enforcement is becoming increasingly popular and efficient with the use of mobile technologies. Beyond sharing information about local problems such as crime via online discussion forums, there has been an increased focus on the impact of mobile crowdsourcing systems on community policing. In this study, we designed a novel crowdsourced patrolling campaign in which community members schedule their own patrol times and routes, then perform bike-based patrolling with video capturing using their smartphones. We conducted a four-week field study (n=20) on a university campus to verify the campaign's feasibility and observe users' behavior. Our results show key findings about users' task scheduling, event capturing and reporting behaviors, factors affecting task selection and execution and user motivation and engagement. Finally, we discuss several practical design implications in building crowdsourcing systems for community policing.

Indoor localization is the key infrastructure for indoor location-aware applications. In this paper, we consider an emergency scenario, where a team of soldiers or first responders perform time-critical missions in a large and complex building. In particular, we consider the case where infrastructure-based localization is not feasible for various reasons such as installation/management costs, a power outage, and terrorist attacks. We design a novel algorithm called the collaborative indoor positioning scheme (CLIPS), which does not require any pre-existing indoor infrastructure. Given that users are equipped with a signal strength map for the intended area for reference, CLIPS uses this map to compare and extract a set of feasible positions from all positions on the map when the device measures signal strength values at run time. Dead reckoning is then performed to remove invalid candidate coordinates, eventually leading to only correct positions. The main departure from existing peer-assisted localization algorithms is that our approach does not require any infrastructure or manual configuration. We perform testbed experiments and extensive simulations, and our results verify that our proposed scheme converges to an accurate set of positions much faster than existing noncollaborative solutions.

In location-based social Q8A services, people ask a question with a high expectation that local residents who have local knowledge will answer the question. However, little is known about the locality of user activities in location-based social Q8A services. This study aims to deepen our understanding of location-based knowledge sharing by investigating the following: general behavioral characteristics of users, the topical and typological patterns related to geographic characteristics, geographic locality of user activities, and motivations of local knowledge sharing. To this end, we analyzed a 12-month period Q8A dataset from Naver KiN “Here,” a location-based social Q8A mobile app, in addition to a supplementary survey dataset obtained from 285 mobile users. Our results reveal several unique characteristics of location-based social Q8A. When compared with conventional social Q8A sites, users ask and answer different topical/typological questions. In addition, those who answer have a strong spatial locality wherein they primarily have local knowledge in a few regions, in areas such as their home and work. We also find unique motivators such as ownership of local knowledge and a sense of local community. The findings reported in the article have significant implications for the design of Q8A systems, especially location-based social Q8A systems.

The exponential rise in the volume of publications and the prevalence of multidisciplinary practice in scientific domains has made it increasingly difficult to keep track of changes in research trends. In this paper, we propose a framework for determining persistent and emerging research topics in scientific literature. The topics were represented as non-overlapping communities of keywords in a dynamic cooccurrence network derived from 21 million articles in PubMed that were published from 1980 to 2016. We detected a set of communities for each snapshot of the network and traced their instances in consecutive periods using a similarity threshold. Our approach provides a retrospective analysis of changes in research topics: their formation, growth, shrinkage, survival, merging, splitting, and dissolution. We also show that a feature set comprising of 43 temporal and structural attributes from these keyword communities can be used to predict their evolution. In particular, we found that the frequency of co-occurrences and the appearance of new keywords within the community are highly predictive of its persistence or dissolution in the next five years.

Recent advances in mobile computing technology have made it increasingly common for collocated users to perform collaborative photography over a large physical space in various group activity scenarios such as field trips, site surveys, and group tours. Unlike traditional collocated interactions in a shared physical space, we find that mobility and group dynamics make awareness of group activities over a large physical space very challenging. In this work, we design LetsPic, a group photoware that supports group awareness for in-situ collaborative photography over the large physical space. We have iteratively built the app and performed user studies in site survey and group tour scenarios (n = 31, n = 24). Our results confirmed that LetsPic effectively promotes group awareness, facilitates group coordination, and encourages collaboration in both scenarios. We discuss practical design implications based on our findings.

We consider community policing on the road with pervasive recording technologies such as dashcams and smartphones where citizens are actively volunteering to capture and report various threats to traffic safety to the police via mobile apps. This kind of novel community policing has recently gained significant popularity in Korea and India. In this work, we identify people's general attitude and concerns toward community policing on the road through an online survey. We then address the major concerns by building a mobile app that supports easy event capture/access, context tagging, and privacy preservation. Our two-week user study (n = 23) showed Roadwatch effectively supported community policing activities on the road. Further, we found that the critical factors for reporting are personal involvement and seriousness of risks, and participants were mainly motivated by their contribution to traffic safety. Finally, we discuss several practical design implications to facilitate community policing on the road.

We explore design opportunities for using interactive technologies to enrich group fitness exercises, such as group spinning and swimming, in which an instructor guides a workout program and members synchronously perform a shared physical activity. As a case study, we investigate group fitness swimming. The design challenge is to coordinate a large group of people by considering trade-offs between social awareness and information overload. Our resulting group fitness swimming game, SwimTrain, allows a group of people to have localized synchronous interactions over a virtual space. The game uses competitive and cooperative phases to help group members acquire group-wide awareness. The results of our user study showed that SwimTrain provides socially-enriched swimming experiences, motivates swimmers to follow a training regimen and exert more intensely, and allows strategic game play dealing with skill differences among swimmers. Consequently, we propose several practical considerations for designing group fitness exergames.

Prior studies have addressed many negative aspects of mobile distractions in group activities. In this paper, we present Lock n' LoL. This is an application designed to help users focus on their group activities by allowing group members to limit their smartphone usage together. In particular, it provides synchronous social awareness of each other's limiting behavior. This synchronous social awareness can arouse feelings of connectedness among group members and can mitigate social vulnerability due to smartphone distraction (e.g., social exclusion) that often results in poor social experiences. After following an iterative prototyping process, we conducted a large-scale user study (n = 976) via real field deployment. The study results revealed how the participants used Lock n' LoL in their diverse contexts and how Lock n' LoL helped them to mitigate smartphone distractions.

Dashcams support continuous recording of external views that provide evidence in case of unexpected traffic-related accidents and incidents. Recently, sharing of dashcam videos has gained significant traction for accident investigation and entertainment purposes. Furthermore, there is a growing awareness that dashcam video sharing will greatly extend urban surveillance. Our work aims to identify the major motives and concerns behind the sharing of dashcam videos for urban surveillance. We conducted two survey studies (n=108, n=373) in Korea. Our results show that reciprocal altruism/social justice and monetary reward were the major motives and that participants were strongly motivated by altruism and social justice. Our studies have also identified major privacy concerns and found that groups with greater privacy concerns had lower altruism and justice motive, but had higher monetary motive. Our main findings have significant implications on the design of a dashcam video-sharing service.

Prolonged inactivity in office workers is a well-known contributor to various diseases, such as obesity, diabetes, and cardiovascular dysfunction. In recent years, active workstations that incorporate physical activities such as walking and cycling into the workplace have gained significant popularity, owing to the accessibility of the workouts they offer. While their efficacy is well documented in medical and physiological literature, research regarding the user experience of such systems has rarely been performed, despite its importance for interactive systems design. As a case study, we focus on active workstations that incorporate under desk elliptical trainers, and conduct controlled experiments regarding work performance and a four week long field deployment to explore user experience with 13 participants. We investigate how such workouts influence work performance, when and why workers work out during working hours, and the general feelings of workers regarding usage. Our experimental results indicate that while work performance is not influenced, the cognitive load of tasks critically influences workout decisions. Active workstations were alternatively used as mood enhancers, footrests, and for fidgeting, and there exist unique social and technical aspects to be addressed, such as noise issues and space constraints. Our results provide significant implications for the design of active workstations and interactive workplaces in general.

Last encounter routing (LER) is an excellent routing paradigm that exploits distributed mobility diffusion to achieve both moving object tracking and packet networking services in dynamic mobile networks. From our observations, we discover that LER can be easily extended to a mobile DHT protocol that introduces excellent performance to high-speed mobility environments. This is of particular interest when higher level of mobility and membership dynamics go hand in hand. In our simple but powerful DHT paradigm, a data publish/look-up process consists of a sequence of spatial motion tracking of the rendezvous node that is responsible for the data resource. Thus, we name the protocol MX-DHT (Motion-MiX-DHT). As opposed to existing topology based DHT schemes, MX-DHT does not require additional management of logical or overlying look-up topologies, except for the one-hop encounter records of logical metadata carried by mobile nodes. Therefore, in high-speed mobility and dynamic membership environments, MX-DHT achieves a significant reduction in the communication costs of the publish/look-up and join/leave operations as compared to existing mobile DHT schemes. An extensive set of experiments showed that MX-DHT is a cost-effective solution to providing a content centric networking service in different types of networks with dynamic mobility and membership changes.

This article aims to deepen understanding of these mass interactions in online sports viewing through studying Naver Sports, the largest online sports viewing service in Korea. We examined the diverse aspects of mass interactions, including interactive experiences, usage motives, and relationships between usage patterns and motives, through analysis of almost 6 million chats from Naver Sports and from self-reporting survey data from 1,123 users. First, we found that online sports viewing provides unique interactive experiences when compared to other settings such as offline sports viewing and social TV viewing with friends. Second, we found the key motives inspiring online sports viewing include the following: sharing feelings/thoughts, wanting to be entertained, sharing information, and wanting to feel membership in a group. Third, these motives were significantly related to specific usage patterns. Finally, we explored how the study’s key findings can offer practical design implications to enhance online sports viewing services, and to show system designers how to support particular usage patterns to better accommodate specific user motives.

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.

Document capturing with smartphone cameras is performed increasingly often in our daily lives. However, our user study results (n=10) showed that more than 80% of landscape tasks had incorrect orientations. To solve this problem, we systematically analyzed user behavior of document capturing and proposed a novel solution called ScanShot that detects document capturing moments to help users correct the orientation errors. ScanShot tracks the gravity direction to capture document capturing moments, analyzes logged gyroscope data to automatically update orientation changes, and provides visual feedback of the inferred orientation for manual correction. Our user study results (n=20) confirmed that capturing moments can be recognized with accuracy of 97.5%, our update mechanism can reduce the orientation errors by 59 percentage points.

Our preliminary study reveals that individuals use various management strategies for limiting smartphone use, ranging from keeping smartphones out of reach to removing apps. However, we also found that users often had difficulties in maintaining their chosen management strategies due to lack of self-regulation. In this paper, we present NUGU, a group-based intervention app for improving self-regulation of limiting smartphone use through leveraging social support: groups of people limit their use together by sharing their limiting information. NUGU is designed based on social cognitive theory, and it has been developed iteratively through two pilot tests. Our three-week user study (n = 62) demonstrated that compared with its non-social counterpart, the NUGU users' usage amount significantly decreased and their perceived level of managing disturbances improved. Furthermore, our exit interview confirmed that NUGU's design elements are effective for achieving limiting goals.

We consider participatory parental mediation in which children engage with their parents in activities that encourage both parents and children to participate in co-learning of digital media use. To this end, we developed FamiLync, a mobile service that treats use-limiting as a family activity and provides the family with a virtual public space to foster social awareness and improve self-regulation. A three-week user study conducted with twelve families in Korea (17 parents and 18 teenagers) showed that FamiLync improves mutual understanding of usage behavior, thereby providing common grounds for parental mediation. Further, parents actively participated in use-limiting with their children, which significantly increased the children's desire to participate. As a consequence, parental mediation methods and parent-child interaction in relation to smartphone usage changed appreciably, and the participants smartphone usage amount significantly decreased.

Providing accurate color information to online shopping customers is important for their purchase decisions. However, due to the multiple imaging processes that product photos undergo, end-users often experience a color mismatch between the color of the photo online and the product received. Therefore, we use a crowdsourcing approach to generate what we term CrowdColor, which is the collective color reported by individuals using a mobile color picker. CrowdColor serves as a color review application from the customers' perspectives in the form of a color palette that represents the product color. We perform controlled experiments to evaluate the accuracy of CrowdColor and to understand how the effects of the device and lighting conditions may influence the crowd's color perception and input tasks. The quantitative results reveal that CrowdColor achieves high accuracy and is positively rated overall. Based on experimental analyses, we present design guidelines for crowdsourcing color perception tasks.

The negative aspects of smartphone overuse on young adults, such as sleep deprivation and attention deficits, are being increasingly recognized recently. This emerging issue motivated us to analyze the usage patterns related to smartphone overuse. We investigate smartphone usage for 95 college students using surveys, logged data, and interviews. We first divide the participants into risk and non-risk groups based on self-reported rating scale for smartphone overuse. We then analyze the usage data to identify between-group usage differences, which ranged from the overall usage patterns to app-specific usage patterns. Compared with the non-risk group, our results show that the risk group has longer usage time per day and different diurnal usage patterns. Also, the risk group users are more susceptible to push notifications, and tend to consume more online content. We characterize the overall relationship between usage features and smartphone overuse using analytic modeling and provide detailed illustrations of problematic usage behaviors based on interview data.

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.

In location-based social Q&A, the questions related to a local community (e.g. local services and places) are typically answered by local residents (i.e. who have the local knowledge). In this work, we wanted to deepen our understanding of the localness of knowledge sharing through investigating the topical and typological patterns related to the geographic characteristics, geographic locality of user activities, and motivations of local knowledge sharing. To this end, we analyzed a 12-month period Q&A dataset from Naver KiN "Here" and a supplementary survey dataset from 285 mobile users. Our results revealed several unique characteristics of location-based social Q&A. When compared with conventional social Q&A sites, Naver KiN "Here" had very different topical/typological patterns. Naver KiN "Here" exhibited a strong spatial locality where the answerers mostly had 1-3 spatial clusters of contributions, the topical distributions varied widely across different districts, and a typical cluster spanned a few neighboring districts. In addition, we uncovered unique motivators, e.g. ownership of local knowledge and sense of local community. The findings reported in the paper have significant implications for the design of Q&A systems, especially location-based social Q&A systems.

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.

Mobile underwater networks with acoustic communications are confronted with several unique challenges such as long propagation delays, high transmission power consumption, and node mobility. In particular, slow signal propagation permits multiple packets to concurrently travel in the underwater channel, which must be exploited to improve the overall throughput. To this end, we propose the delay-aware opportunistic transmission scheduling (DOTS) protocol that uses passively obtained local information (i.e., neighboring nodes' propagation delay map and their expected transmission schedules) to increase the chances of concurrent transmissions while reducing the likelihood of collisions. Our extensive simulation results document that DOTS outperforms existing solutions and provides fair medium access even with node mobility.

Despite the popularity of mobile pay-for-answer Q&A services, little is known about the people who answer questions on these services. In this paper we examine 18.8 million question and answer pairs from Jisiklog, the largest mobile pay-foranswer Q&A service in Korea, and the results of a complementary survey study of 245 Jisiklog workers. The data are used to investigate key motivators of participation, working strategies of experienced users, and longitudinal interaction dynamics. We find that answerers are rarely motivated by social factors but are motivated by financial incentives and intrinsic motives. Additionally, although answers are provided quickly, an answerer's topic selection tends to be broad, with experienced workers employing unique strategies to answer questions and judge relevance. Finally, analysis of longitudinal working patterns and community dynamics demonstrate the robustness of mobile pay-for-answer Q&A. These findings have significant implications on the design of mobile pay-for-answer Q&A.

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.

The proliferation of pictures and videos in the Internet is imposing heavy demands on mobile data networks. Though emerging wireless technologies will provide more bandwidth, the increase in demand will easily consume the additional capacity. To alleviate this problem, we explore the possibility of serving user requests from other mobile devices located geographically close to the user. For instance, when Alice reaches areas with high device density - Data Spots - the cellular operator learns Alice's content request, and guides her device to nearby devices that have the requested content. Importantly, communication between the nearby devices can be mediated by servers, avoiding many of the known problems of pure ad hoc communication. This paper argues this viability through systematic prototyping, measurements, and measurement-driven analysis.

Mobile underwater networking is a developing technology for monitoring and exploring the Earth's oceans. For effective underwater exploration, multimedia communications such as sonar images and low resolution videos are becoming increasingly important. Unlike terrestrial RF communication, underwater networks rely on acoustic waves as a means of communication. Unfortunately, acoustic waves incur long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery. On the positive side, the long propagation delay permits multiple packets to be “pipelined” concurrently in the underwater channel, improving the overall throughput and enabling applications that require sustained bandwidth. To enable session multiplexing and pipelining, we propose the Multi-session FAMA (M-FAMA) algorithm. M-FAMA leverages passively-acquired local information (i.e., neighboring nodes' propagation delay maps and expected transmission schedules) to launch multiple simultaneous sessions. M-FAMA's greedy behavior is controlled by a Bandwidth Balancing algorithm that guarantees max-min fairness across multiple contending sources. Extensive simulation results show that M-FAMA significantly outperforms existing MAC protocols in representative streaming applications.

In this paper, we propose SocioPhone, a novel initiative to build a mobile platform for face-to-face interaction monitoring. Face-to-face interaction, especially conversation, is a fundamental part of everyday life. Interaction-aware applications aimed at facilitating group conversations have been proposed, but have not proliferated yet. Useful contexts to capture and support face-to-face interactions need to be explored more deeply. More important, recognizing delicate conversational contexts with commodity mobile devices requires solving a number of technical challenges. As a first step to address such challenges, we identify useful meta-linguistic contexts of conversation, such as turn-takings, prosodic features, a dominant participant, and pace. These serve as cornerstones for building a variety of interaction-aware applications. SocioPhone abstracts such useful meta-linguistic contexts as a set of intuitive APIs. Its runtime efficiently monitors registered contexts during in-progress conversations and notifies applications on-the-fly. Importantly, we have noticed that online turn monitoring is the basic building block for extracting diverse meta-linguistic contexts, and have devised a novel volume-topography-based method. We show the usefulness of SocioPhone with several interesting applications: SocioTherapist, SocioDigest, and Tug-of-War. Also, we show that our turn-monitoring technique is highly accurate and energy-efficient under diverse real-life situations.

No abstract available.

Indoor localization has attracted much attention recently due to its potential for realizing indoor location-aware application services. This paper considers a time-critical scenario with a team of soldiers or first responders conducting emergency mission operations in a large building in which infrastructure-based localization is not feasible (e.g., due to management/installation costs, power outage, terrorist attacks). To this end, we design and implement a collaborative indoor positioning scheme (CLIPS) that requires no preexisting indoor infrastructure. We assume that each user has a received signal strength map for the area in reference. This is used by the application to compare and select a set of feasible positions, when the device receives actual signal strength values at run time. Then, dead reckoning is performed to remove invalid candidate coordinates eventually leaving only the correct one which can be shared amongst the team. Our evaluation results from an Android-based testbed show that CLIPS converges to an accurate set of coordinates much faster than existing noncollaborative schemes (more than 50% improvement under the considered scenarios).

Underwater mobile sensor networks have recently been proposed as a way to explore and observe the ocean, providing 4D (space and time) monitoring of underwater environments. We consider a specialized geographic routing problem called pressure routing that directs a packet to any sonobuoy on the surface based on depth information available from on-board pressure gauges. The main challenge of pressure routing in sparse underwater networks has been the efficient handling of 3D voids. In this respect, it was recently proven that the greedy stateless perimeter routing method, very popular in 2D networks, cannot be extended to void recovery in 3D networks. Available heuristics for 3D void recovery require expensive flooding. In this paper, we propose a Void-Aware Pressure Routing (VAPR) protocol that uses sequence number, hop count and depth information embedded in periodic beacons to set up next-hop direction and to build a directional trail to the closest sonobuoy. Using this trail, opportunistic directional forwarding can be efficiently performed even in the presence of voids. The contribution of this paper is twofold: a robust soft-state routing protocol that supports opportunistic directional forwarding; and a new framework to attain loop freedom in static and mobile underwater networks to guarantee packet delivery. Extensive simulation results show that VAPR outperforms existing solutions.

Content distribution in vehicular networks, such as multimedia file sharing and software updates, poses a great challenge due to network dynamics and high-speed mobility. In recent years, network coding has been shown to efficiently support distribution of content in such dynamic environments, thereby considerably enhancing the performance. However, the related work in the literature has mostly focused on theoretic or algorithmic aspects of network coding so far. In this paper, we provide an in-depth analysis on the implementation issues of network coding in wireless networks. In particular, we study the impact of resource constraints (namely CPU, disk, memory, and bandwidth) on the performance of network coding in the content distribution application. The contribution of this paper is twofold. First, we develop an abstract model of a general network coding process and evaluate the validity of the model via several experiments on real systems. This model enables us to find the key resource constraints that influence the network coding strategy and thus to efficiently configure network coding parameters in wireless networks. Second, we propose schemes that considerably improve the performance of network coding under resource constrained environments. We implement our overhead model in the QualNet network simulator and evaluate these schemes in a large-scale vehicular network. Our results show that the proposed schemes can significantly improve the network coding performance by reducing the coding overhead.

Recently questioning and answering (Q&A) communities that facilitate knowledge sharing among people have been introduced to the mobile environments such as Naver Mobile Q&A and ChaCha. These mobile Q&A services are very different from traditional Q&A sites in that questions/answers are short in length and are exchanged via mobile devices (e.g., SMS or mobile Internet). While traditional Q&A sites have been well investigated, so far little is known about the mobile Q&A usage. To understand mobile Q&A usage, we analyzed 2.4 million question/answer pairs spanning a 14 month period from Naver Mobile Q&A and performed a complementary survey study of 555 active mobile Q&A users. We find that mobile Q&A is deeply wired into users' everyday life activities - its usage is largely dependent on users' spatial, temporal, and social contexts; the key factors of mobile Q&A usage are accessibility/convenience of mobile Q&A, promptness of receiving answers, and users' satisficing behavior of information seeking (i.e., minimizing efforts and settling with good enough information). We also observe that users tend to seek more factual information attributed to everyday life activities than they do on traditional Q&A sites and that they exhibit unique interaction patterns such as repeating and refining questions as coping strategies in seeking information needs. Our main findings reported in the paper have significant implications on the design of mobile Q&A systems.

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.

Urban sensing where mobile users continuously gather, process, and share location-sensitive sensor data (e.g., street images, road condition, traffic flow) is emerging as a new network paradigm of sensor information sharing in urban environments. The key enablers are the smart phones (e.g., iPhones and Android phones) equipped with onboard sensors (e.g., cameras, accelerometer, compass, GPS), and various wireless devices (e.g., WiFi and 2/3G). The goal of this paper is to design a scalable sensor networking platform where millions of users on the move can participate in urban sensing and share location-aware information using always-on cellular data connections. We propose a two-tier sensor networking platform called GeoServ where mobile users publish/access sensor data via an Internet-based distributed P2P overlay network. The main contribution of this paper is two-fold: a location-aware sensor data retrieval scheme that supports geographic range queries, and a location-aware publish-subscribe scheme that enables efficient multicast routing over a group of subscribed users. We prove that GeoServ protocols preserve locality and validate their performance via extensive simulations.

Underwater Acoustic Sensor Networks (UW-ASNs) use acoustic links as a means of communications and are accordingly confronted with long propagation delays, low bandwidth, and high transmission power consumption. This unique situation, however, permits multiple packets to concurrently propagate in the underwater channel, which must be exploited in order to improve the overall throughput. To this end, we propose the Delay-aware Opportunistic Transmission Scheduling (DOTS) algorithm that uses passively obtained local information (i.e., neighboring nodes' propagation delay map and their expected transmission schedules) to increase the chances of concurrent transmissions while reducing the likelihood of collisions. Our extensive simulation results document that DOTS outperforms existing solutions and provides fair medium access.

A SEA Swarm (Sensor Equipped Aquatic Swarm) is a sensor "cloud" that drifts with water currents and enables 4D (space and time) monitoring of local underwater events such as contaminants, marine life and intruders. The swarm is escorted at the surface by drifting sonobuoys that collect the data from underwater sensors via acoustic modems and report it in real-time via radio to a monitoring center. The goal of this study is to design an efficient anycast routing algorithm for reliable underwater sensor event reporting to any one of the surface sonobuoys. Major challenges are the ocean current and the limited resources (bandwidth and energy). In this paper, we address these challenges and propose HydroCast, a hydraulic pressure based anycast routing protocol that exploits the measured pressure levels to route data to surface buoys. The paper makes the following contributions: a novel opportunistic routing mechanism to select the subset of forwarders that maximizes greedy progress yet limiting co-channel interference; and an efficient underwater "dead end" recovery method that outperforms recently proposed approaches. The proposed routing protocols are validated via extensive simulations.

AutoGait is a mobile platform that autonomously discovers a user's walking profile and accurately estimates the distance walked. The discovery is made by utilizing the GPS in the user's mobile device when the user is walking outdoors. This profile can then be used both indoors and outdoors to estimate the distance walked. To model the person's walking profile, we take advantage of the fact that a linear relationship exists between step frequency and stride length, which is unique to individuals and applies to everyone regardless of age. Autonomous calibration invisible to users allows the system to maintain a high level of accuracy under changing conditions. AutoGait can be integrated into any pedometer or indoor navigation software on handheld devices as long as they are equipped with GPS. The main contribution of this paper is two fold: (1) we propose an auto-calibration method that trains a person's walking profile by effectively processing noisy GPS readings, and (2) we build a prototype system and validate its performance by performing extensive experiments. Our experimental results confirm that the proposed auto-calibration method can accurately estimate a person's walking profile and thus significantly reduce the error rate.

There has been a variety of rate adaptation solutions proposed for both indoor and mobile scenarios. However, dynamic channel changing conditions (e.g., temporal channel variation due to unpredictable traffic pattern) make it virtually impossible to guarantee the same evaluation environment for all these schemes. Moreover, developing these schemes exhaustively on actual hardware can be gruesomely long. In this work, we propose an integrated framework which utilizes empirical data gathered from the vehicular testbed to objectively compare different rate adaptation schemes for Vehicular Ad-hoc Networks (VANETs). Using this framework, we implemented some well-known adaptation schemes and evaluated their performance. The main contribution of this paper is a methodology to compare rate adaptation schemes in an environment which is both realistic and repeatable. In addition, our results shed new light on impact of various different environment and channel factors on the performance of different schemes.

A SEA Swarm (Sensor Equipped Aquatic Swarm) is a collection of mobile underwater sensors that moves as a group with water current and enables 4D (space and time) monitoring of local underwater events such as contaminants and intruders. For prompt alert reporting, mobile sensors routes events to mobile sinks (i.e., autonomous underwater vehicles) via geographic routing that is known to be most efficient under mobility and scarce acoustic bandwidth. In order for a packet to be routed to the destination using geographical routing, it requires to know the location of the destination. This is accomplished by having a location service that returns the location of a requested node. Our goal is to design such location service for SEA Swarm. In this paper, we analyze various design choices to realize an efficient location service in SEA Swarm scenarios. We find that conventional ad hoc network location service protocols cannot be directly used, because the entire swarm moves along water current. We prove that maintaining location information in a 2D plane is a better design choice. Given this, we propose a bio-inspired location service called a Phero-Trail location service protocol. In Phero-Trail, location information is stored in a 2D upper hull of a SEA Swarm, and a mobile sink uses its trajectory (a la a pheromone trail of ants) projected to the 2D hull to maintain location information. This enables mobile sensors to efficiently locate a mobile sink. Our results show that Phero- Trail performs better than existing approaches.

Biochemical activities in sewer pipes generate various volatile substances that lead to several serious problems such as malodor complaints and lawsuits, concrete and metal corrosion, increased operational costs, and health risks. Frequent inspections are critical to maintain sewer health, yet are extremely expensive given the extent of the sewer system and the "unfriendliness" of the environment. In this paper we propose SewerSnort, a low cost, unmanned, fully automated in-sewer gas monitoring system. A sensor float is introduced at the upstream station and drifts to the end pumping station, collecting location tagged gas measurements. The retrieved SewerSnort provides an accurate gas exposure profile to be used for preventive maintenance and/or repair. The key innovations of SewerSnort are the fully automated, end-to-end monitoring solution and the low energy self localizing strategy. From the implementation standpoint, the key enablers are the float mechanical design that fits the sewer constraints and the embedded sensor design that matches the float form factor and complies with the tight energy constraints. Experiments based on a dry land emulator demonstrate the feasibility of the SewerSnort concept, in particular, the localization technique and the embedded sensor design.

Mobile wireless networks with intermittent connectivity, often called Delay/Disruption Tolerant Networks (DTNs), have recently received a lot of attention because of their applicability in various applications, including multicasting. To overcome intermittent connectivity, DTN routing protocols utilize mobility-assist routing by letting the nodes carry and forward the data. In this paper, we study the scalability of DTN multicast routing. As Gupta and Kumar showed that unicast routing is not scalable, recent reports on multicast routing also showed that the use of a multicast tree results in a poor scaling behavior. However, Grossglauser and Tse showed that in delay tolerant applications, the unicast routing overhead can be relaxed using the two-hop relay routing where a source forwards packets to relay nodes and the relay nodes in turn deliver packets to the destination via “mobility,” thus achieving a perfect scaling behavior of Θ(1). Inspired by this result, we seek to improve the throughput bound of wireless multicast in a delay tolerant setting using mobility-assist routing. To this end, we propose RelayCast, a routing scheme that extends the two-hop relay algorithm in the multicast scenario. Given that there are nssources each of which is associated with ndrandom destinations, our results show that RelayCast can achieve the throughput upper bound of Θ(min(1, n/nsnd)). We also analyze the impact of various network parameters and routing strategies (such as buffer size, multi-user diversity among multicast receivers, and delay constraints) on the throughput and delay scaling properties of RelayCast. Finally, we validate our analytical results with a simulation study.

Besides safe navigation (e.g., warning of approaching vehicles), car to car communications will enable a host of new applications, ranging from offlce-on-the-wheel support to entertainment. One of the most promising applications is content distribution among drivers such as multi-media files and software updates. Content distribution in vehicular networks is a challenge due to network dynamics and high mobility, yet network coding was shown to efficiently handle such dynamics and to considerably enhance performance. This paper provides an in-depth analysis of implementation issues of network coding in vehicular networks. To this end, we consider general resource constraints (e.g., CPU, disk, memory) besides bandwidth, that are likely to impact the encoding and storage management operations required by network coding. We develop an abstract model of the network coding procedures and implement it in the wireless network simulator to evaluate the impact of limited resources. We then propose schemes that considerably improve the use of such resources. Our model and extensive simulation results show that network coding parameters must be carefully configured by taking resource constraints into account.

Underwater environment represents a challenging and promising application scenario for sensor networks. Due to hard constraints imposed by acoustic communications and to high power consumption of acoustic modems, in underwater sensor networks (USN) energy saving becomes even more critical than in traditional sensor networks. In this paper the authors propose delay-tolerant data dolphin (DDD), an approach to apply delay-tolerant networking in the resource-constrained underwater environment. DDD exploits the mobility of a small number of capable collector nodes (namely dolphins) to harvest information sensed by low power sensor devices, while saving sensor battery power. DDD avoids energy-expensive multi-hop relaying by requiring sensors to perform only one-hop transmissions when a dolphin is within their transmission range. The paper presents simulation results to evaluate the effectiveness of randomly moving dolphins for data collection.

In this paper we study multi-hop ad hoc routing in a Underwater Sensor Network (UWSN), a novel network paradigm for ad hoc underwater investigation with a large number of low cost underwater sensors. In UWSN, sensors are mobile with water current and dispersion, and use a wireless acoustic channel for communications. However, the large propagation latency and very low bandwidth of an acoustic channel could cause widespread collisions. Moreover, the mobility of sensors requires route management and causes additional traffic, thus worsening the situation. In this paper, we propose Under-Water Diffusion (UWD), a multi-hop ad hoc routing and in-network processing protocol. Since any on-demand flood or proactive exchange is considered harmful in underwater, UWD uses no proactive routing message exchange and negligible amount of ondemand floods in the environment with homogeneous GPSfree nodes and random node mobility. We validate UWD through both the mathematical analysis and simulations.

Over recent years, mobile Internet devices such as laptops, PDAs, smart phones etc, have become extremely popular and widespread. Once on board of a vehicle, these devices can automatically connect to the vehicle processor and thus greatly amplify the communications and processing capabilities available to the owner in a "pedestrian mode". We envision that this "amplification" opportunity will be one of the drivers of car to car and car to curb communications. In fact, the car communications system will not be used exclusively for mobile Internet access, but also as a distributed platform for the "opportunistic" cooperation among people with shared interests/goals. Exchanging safety messages among vehicles is a compelling example. Stretching opportunistic cooperation well beyond safety messages, we discuss in this paper the concept of virtual "flea market" over VANET called FleaNet In FleaNet, customers, either mobile (i.e., vehicles) or stationary (i.e., pedestrians, roadside shop owner), express their demands/offers, e.g., want to buy or sell an item, via radio queries. These queries are opportunistically disseminated exploiting in part the mobility of other customers in order to find the customer/vendor with matching needs/resources. In the paper we identify the key performance metrics, namely query resolution latency, scalability, and mobility. Based on the metrics, using models and simulation, we show that FleaNet can efficiently support a market place over vehicular networks

There has been recent interests in studying the "goal" behind a user's Web query, so that this goal can be used to improve the quality of a search engine's results. Previous studies have mainly focused on using manual query-log investigation to identify Web query goals. In this paper we study whether and how we can automate this goal-identification process. We first present our results from a human subject study that strongly indicate the feasibility of automatic query-goal identification. We then propose two types of features for the goal-identification task: user-click behavior and anchor-link distribution. Our experimental evaluation shows that by combining these features we can correctly identify the goals for 90% of the queries studied.