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.

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.

Mobile telephones, company ID badges, and similar common devices form a sensor network which can be used to map human activity, and especially human interactions. The most informative sensor data seem to be measurements of personto-person proximity, and statistics of vocalization and body movement measurements. Using this data to model individual behavior as a stochastic process allows prediction of future activity, with the greatest predictive power obtained by modeling the interactions between individual processes. Experiments show that between 40 % and 95 % of the variance in human behavior may be explained by such models.

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.

Today's mobile phones are essentially off-the-shelf, mass-market wearable computers. In this paper, we describe mobile social software that uses location, proximity and tone of voice to create a sophisticated understanding of people's social lives, by mining their face-to-face and phone interactions. We describe several applications of our system – automatic characterization of social and workplace interactions, a courtesy reminder for phone conversations, and a personal trainer for dating encounters.

Non-linguistic social signals (e.g., `tone of voice’) are often as important as linguistic content in predicting behavioural outcomes [1,2]. This paper describes four automated measures of such social signalling within the experimental context of speech in speed dating.

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes aset of recommendations for enabling the development of the next generation of socially-aware computing.

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Wearable computers, embedded in clothing or seamlessly integrated in devices we carry with us, have a tremendous advantage to become the main gateway to personal health management. Current stateof -the-art devices allow to monitor basic physical or physiological parameters.