We explore whether a peripheral observation of apparent mutual social presence between a real human (RH) and a virtual human (VH) can in turn increase a subject's sense of social presence with the VH. In other words, we explore whether social presence can “transfer” from one RH-VH interaction to another. Specifically, we carried out an experiment where human subjects were asked to play a game with a VH. As they entered the game room, approximately half of the subjects were exposed to a brief but apparently engaging conversation between an RH and the VH. The subjects who were exposed to the brief RH-VH interaction had significantly higher measures of both emotional connection and the attentional allocation dimension of social presence for the VH, compared to those who were not. We describe the motivation, the experiment, and the results.

While performing everyday interactions, we often incidentally touch and move objects in subtle ways. These objects are not necessarily directly related to the task at hand, and the movement of an object might even be entirely unintentional. If another person is touching the object at the same time, the movement can transfer through the object and be experienced — however subtly — by the other person. For example, when one person hands a drink to another, at some point both individuals will be touching the glass, and consequently exerting small (often unnoticed) forces on the other person. Despite the frequency of such subtle incidental movements of shared objects in everyday interactions, few have examined how these movements affect human-virtual human (VH) interaction. We ran an experiment to assess how presence and social presence are affected when a person experiences subtle, incidental movement through a shared real-virtual object. We constructed a real-virtual room with a table that spanned the boundary between the real and virtual environments. The participant was seated on the real side of the table, which visually extended into the virtual world via a projection screen, and the VH was seated on the virtual side of the table. The two interacted by playing a game of “Twenty Questions,” where one player asked the other a series of 20 yes/no questions to deduce what object the other player was thinking about. During the game, the “wobbly” group of subjects experienced subtle incidental movements of the real-virtual table: the entire real-virtual table tilted slightly away/toward the subject when the virtual/real human leaned on it. The control group also played the same game, except the table did not wobble. Results indicate that the wobbly group had higher presence and social presence with the virtual human in general, with statistically significant increases in presence, co-presence, and attentional allocation. We present the experiment and results, and discuss some potential implications for virtual human systems and some potential future studies.

This paper examines just-in-time adaptive interventions (JITAIs) for stress, a pervasive and affective computing application with significant implications for long-term health and quality of life. We discuss fundamental components needed to enabling JITAIs based for one kind of affect data stress. Chronic stress has significant long-term behavioral and physical health consequences, including an increased risk of cardiovascular disease, cancer, anxiety and depression. This paper conducts post-hoc experiments and simulations to demonstrate feasibility of both real-time stress forecasting and stress intervention adaptation and optimization. Using physiological data collected by ten individuals in the natural environment for one week, we show 1) that simple Hidden Markov Models (HMMs) can be used to forecast physiological measures of stress with up to 3 minutes in advance; and 2) Q-Learning (QL) combined with eligibility traces could be used by an affective computing system to adapt and deliver any number and type of interventions in response to changes in affect. Our hope is that this work will take us one step closer to using pervasive devices to assist in the daily management of chronic stress and other affect-related challenges.

We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.

Crowd sensing (CS) is an approach to collecting many samples of a phenomena of interest by distributing the sampling across a large number of individuals. While any one individual may not provide sufficient samples, aggregating samples across many individuals provides high-quality, high-coverage measurements of the phenomena. Thus, for participatory sensing to be successful, one must motivate a large number of individuals to participate. In this work, we review a variety of incentive mechanisms that motivate people to contribute to a CS effort. We then establish a set of design constraints or minimum requirements that any incentive mechanism for CS must have. These design constrains are then used as metrics to evaluate those approaches and determine their advantages and disadvantages. We also contribute a taxonomy of CS incentive mechanisms and show how current systems fit within this taxonomy. We conclude with the identification of new types of incentive mechanisms that require further investigation.

We introduce a new paradigm of collaborative computing called the Ubiquitous Collaborative Activity Virtual Environment (UCAVE). UCAVEs are portable immersive virtual environments that leverage mobile communication platforms, motion trackers and displays to facilitate ad-hoc virtual collaboration. We discuss design criteria and research challenges for UCAVEs, as well as a prototype hardware configuration that enables UCAVE interactions using modern smart phones and head mounted displays.

Wearable sensors are revolutionizing healthcare and science by enabling capture of physiological, psychological, and behavioral measurements in natural environments. However, these seemingly innocuous measurements can be used to infer potentially private behaviors such as stress, conversation, smoking, drinking, illicit drug usage, and others. We conducted a study to assess how concerned people are about disclosure of a variety of behaviors and contexts that are embedded in wearable sensor data. Our results show participants are most concerned about disclosures of conversation episodes and stress - inferences that are not yet widely publicized. These concerns are mediated by temporal and physical context associated with the data and the participant's personal stake in the data. Our results provide key guidance on the extent to which people understand the potential for harm and data characteristics researchers should focus on to reduce the perceived harm from such datasets.

Micro-incentives represent a new but little-studied trend in participant compensation for user studies. In this paper, we use a combination of statistical analysis and models from labor economics to evaluate three canonical micro-payment schemes in the context of high-burden user studies, where participants wear sensors for extended durations. We look at how these strategies affect compliance, data quality, and retention, and show that when used carefully, micro-payments can be highly beneficial. We find that data quality is different across the micro-incentive schemes we experimented with, and therefore the incentive strategy should be chosen with care. We think that adaptive micro-payment based incentives can be used to successfully incentivize future studies at much lower cost to the study designer, while ensuring high compliance, good data quality, and lower retention issues.

Effect of psychosocial stress on health has been a central focus area of public health research. This demonstration features a wireless sensor suite called AutoSense that collects and processes cardiovascular, respiratory, and thermoregularity measurements that can inform about the general stress state of test subjects in their natural environment.

The effect of psychosocial stress on health has been a central focus area of public health research. However, progress has been limited due a to lack of wearable sensors that can provide robust measures of stress in the field. In this paper, we present a wireless sensor suite called AutoSense that collects and processes cardiovascular, respiratory, and thermoregularity measurements that can inform about the general stress state of test subjects in their natural environment. AutoSense overcomes several challenges in the design of wearable sensor systems for use in the field. First, it is unobtrusively wearable because it integrates six sensors in a small form factor. Second, it demonstrates a low power design; with a lifetime exceeding ten days while continuously sampling and transmitting sensor measurements. Third, sensor measurements are robust to several sources of errors and confounds inherent in field usage. Fourth, it integrates an ANT radio for low power and integrated quality of service guarantees, even in crowded environments. The AutoSense suite is complemented with a software framework on a smart phone that processes sensor measurements received from AutoSense to infer stress and other rich human behaviors. AutoSense was used in a 20+ subject real-life scientific study on stress in both the lab and field, which resulted in the first model of stress that provides 90% accuracy.

This paper proposes virtual social perspective-taking (VSP). In VSP, users are immersed in an experience of another person to aid in understanding the person's perspective. Users are immersed by 1) providing input to user senses from logs of the target person's senses, 2) instructing users to act and interact like the target, and 3) reminding users that they are playing the role of the target. These guidelines are applied to a scenario where taking the perspective of others is crucial - the medical interview. A pilot study (n = 16) using this scenario indicates VSP elicits reflection on the perspectives of others and changes behavior in future, similar social interactions. By encouraging reflection and change, VSP advances the state-of-the-art in training social interactions with virtual experiences.

This paper proposes after-action review (AAR) with human-virtual human (H-VH) experiences. H-VH experiences are seeing increased use in training for real-world, H-H experiences. To improve training, the users of H-VH experiences need to review, evaluate, and get feedback on them. AAR enables users to review their H- VH interaction, evaluate their actions, and receive feedback on how to improve future real-world, H-H experiences. The Interpersonal Scenario Visualizer (IPSViz), an AAR tool for H-VH experiences, is presented. IPSViz allows medical students to review their interactions with VH patients. To enable review, IPSViz generates spatial, temporal, and social visualizations of H- VH interactions. Visualizations are generated by treating the interaction as a set of signals. Interaction signals are captured, logged, and processed to generate visualizations for review, evaluation and feedback. In a study (N=27), reviewing the visualizations helped students become self-aware of their actions with a virtual human and gain insight into how to improve interactions with real humans.

Any new tool introduced for education needs to be validated. We developed a virtual human experience called the Virtual Objective Structured Clinical Examination (VOSCE). In the VOSCE, a medical student examines a life-size virtual human who is presenting symptoms of an illness. The student is then graded on interview skills. As part of a medical school class requirement, thirty three second year medical students participated in a user study designed to determine the validity of the VOSCE for testing interview skills. In the study, participant performance in the VOSCE is compared to participant performance in the OSCE, an interview with a trained actor. There was a significant correlation (r(33)=.49, p<.005) between overall score in the VOSCE and overall score in the OSCE. This means that the interaction skills used with a virtual human translate to the interaction skills used with a real human. Comparing the experience of virtual human interaction to real human interaction is the critical validation step towards using virtual humans for interpersonal skills education.

This paper provides key insights into the construction and evaluation of interpersonal simulators--systems that enable interpersonal interaction with virtual humans. Using an interpersonal simulator, two studies were conducted that compare interactions with a virtual human to interactions with a similar real human. The specific interpersonal scenario employed was that of a medical interview. Medical students interacted with either a virtual human simulating appendicitis or a real human pretending to have the same symptoms. In Study I (n = 24), medical students elicited the same information from the virtual and real human, indicating that the content of the virtual and real interactions were similar. However, participants appeared less engaged and insincere with the virtual human. These behavioral differences likely stemmed from the virtual human's limited expressive behavior. Study II (n = 58) explored participant behavior using new measures. Nonverbal behavior appeared to communicate lower interest and a poorer attitude toward the virtual human. Some subjective measures of participant behavior yielded contradictory results, highlighting the need for objective, physically-based measures in future studies.

This paper reports on a study to examine the similarities and differences in experiencing an interpersonal scenario with real and virtual humans. A system that allows medical students to interview a life-size virtual patient using natural speech and gestures was used as a platform for this comparison. Study participants interviewed either a virtual patient or a standardized patient, an actor trained to represent a medical condition. Subtle yet substantial differences were found in the participants’ rapport with the patient and the flow of the conversation. The virtual patient’s limited expressiveness was a significant source of these differences. However, overall task performance was similar, as were perceptions of the educational value of the interaction.

University of Florida This paper presents a system which allows medical students to experience the interaction between a patient and a medical doctor using natural methods of interaction with a high level of immersion. We also present our experiences with a pilot group of medical and physician assistant students at various levels of training. They interacted with projector-based life-sized virtual characters using gestures and speech. We believe that natural interaction and a high level of immersion facilitates the education of communication skills. We present the system details as well as the participants ’ performance and opinions. The study confirmed that the level of immersion contributed significantly to the experience, and participants reported that the system is a powerful tool for teaching and training. Applying the system to formal communication skills evaluation and further scenario development will be the focus of future research and refinement.