We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees' self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = -0.55 (p 0.01), which has far-reaching implications for past and future research on social networks.

SummaryThis article introduces sociometric badges as a research tool that captures with great accuracy fine-scale speech patterns and body movements among a group of individuals at a scale that heretofore has been impossible in groups and teams studies. Such a tool offers great potential for studying the changing ecology of team structures and new modes of collaboration. Team boundaries are blurring as members disperse across multiple cultures, convene through various media, and operate in new configurations. As the how and why of collaboration evolves, an opportunity emerges to reassess the methods used to capture these interactions and to identify new tools that might help us create synergies across existing approaches to teams research. We offer sociometric badges as a complement to existing data collection methods-one that is well-positioned to capture real-time collaboration in new forms of teams. Used as one component in a multi-method approach, sociometric badges can capture what an observer or cross-sectional survey might miss, contributing to a more accurate understanding of group dynamics in new teams. We also revisit traditional teams research to suggest how we might use these badges to tackle long-standing challenges. We conclude with three case studies demonstrating potential applications of these sociometric badges.

As autonomous robots collaborate with people on authors. The researchers observed interactions between the tasks, the questions "who deserves credit?" and "who is to robot and workers in the hospital and conducted interviews blame?" are no longer simple. Based on insights from an with some of the workers. The robot was a Pyxis HelpMate observational study of a delivery robot in a hospital, this paper and its main function was to deliver medication from the deals with how robotic autonomy and transparency affect the attribution of credit and blame. In the study, we conducted a armc to nurig tsroundhelhospital. Terobotcwas 2x2 experiment to test the effects of autonomy and transparency able to navigate through hallways, ask for specific on attributions. We found that when a robot is more medications and call the elevator on its own. autonomous, people attribute more credit and blame to the From our analysis, an interesting pattern of interaction robot and less toward themselves and other participants. When emerged. When an unexpected situation occurred, people the robot explains its behavior (e.g. is transparent), people were easily confused and did not know who was to blame: the blame other participants (but not the robot) less. Finally, robot, themselves or the other workers in the hospital who transparency has a greater effect in decreasing the attribution of blame when the robot is more autonomous.interacted with the robot. In some cases, nurses would attribute incorrect blame or too much responsibility to the

Finding 10 balloons across the U.S. illustrates how the Internet has changed the way we solve highly distributed problems.

We present the Meeting Mediator (MM), a real-time portable system that detects social interactions and provides persuasive feedback to enhance group collaboration. Social interactions is captured using Sociometric badges [17] and are visualized on mobile phones to promote behavioral change. Particularly in distributed collaborations, MM attempts to bridge the gap among the distributed groups by detecting and communicating social signals. In a study on brainstorming and problem solving meetings, MM had a significant effect on overlapping speaking time and interactivity level without distracting the subjects. The Sociometric badges were also able to detect dominant players in the group and measure their influence on other participants. Most interestingly, in groups with one or more dominant people, MM effectively reduced the dynamical difference between co-located and distributed collaboration as well as the behavioral difference between dominant and non-dominant people. Our system encourages change in group dynamics that may lead to higher performance and satisfaction. We envision that MM will be deployed in real-world organizations to improve interactions across various group collaboration contexts.

We introduce the concept of sensor-based applications for the daily business settings of organizations and their individual workers. Wearable sensor devices were developed and deployed in a real organization, a bank, for a month in order to study the effectiveness and potential of using sensors at the organizational level. It was found that patterns of physical interaction changed dynamically while e-mail is more stable from day to day. Different patterns of behavior between people in different rooms and teams (p < 0.01), as well as correlations between communication and a worker's subjective productivity, were also identified. By analyzing a fluctuation of network parameters, i.e., "betweenness centrality," it was also found that communication patterns of people are different: some people tend to communicate with the same people in regular frequency (which is hypothesized as a typical pattern of throughput-oriented jobs) while some others drastically changed their communication day by day (which is hypothesized as a pattern of creative jobs). Based on these hypotheses, a reorganization, such that people having similar characteristics work together, was proposed and implemented.

Abstract. Compared to collocated interaction, videoconferencing disrupts the ability to use gaze and gestures to mediate interaction, direct reactions to specific people, and provide a sense of presence for the satellite (i.e., remote) participant. We developed a kinetic videoconferencing proxy with a swiveling display screen to indicate which direction that the satellite participant was looking. Our goal was to compare two alternative motion control conditions, in which the satellite participant directed the display screen's motion either explicitly (aiming the direction of the display with a mouse) or implicitly (with the screen following the satellite participant's head turns). We then explored the effectiveness of this prototype compared to a typical stationary video display in a lab study. We found that both motion conditions resulted in communication patterns that indicate higher engagement in conversation, more accurate responses to the satellite participant's deictic questions (i.e., "What do you think?"), and higher user rankings. We also discovered tradeoffs in attention and clarity between explicit versus implicit control, a tension in how motion toward one person can exclude other people, and ways that swiveling motion provides attention awareness, even without direct eye contact.

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