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.

We propose a social network data collection method that uses wearable social sensors to automatically detect social interactions. This method offers clear advantages over traditional methods since data is automatically collected by electronic sensors rather than humans. We present the design, implementation and deployment of a wearable social sensing platform that can measure and analyze human behavior in a variety of settings and applications. Social sensors are capable of capturing individual and collective patterns of behavior by automatically measuring the amount of face-to-face interaction, conversational dynamics, physical proximity to other people, and physical activity levels. We describe five studies that have been carried out using this platform and discuss other possible application scenarios.

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We show how a wearable computing research platform for measuring and analyzing human behavior can be used to understand social systems. Using a wearable sociometric badge capable of automatically measuring the amount of face-to-face interaction, physical proximity to other people, and relative location, we are able to construct a dynamic view of an organization’s social network by viewing interactions as links between actors. Combining this with email data, where e-mail exchanges indicate a social tie, we are able to form a robust view of the social network, using proximity information to remove spurious e-mail exchanges. We attempt to use on-body sensors in large groups of people for extended periods of time in naturalistic settings for the purpose of identifying, measuring, and quantifying social interactions, information flow, and organizational dynamics. We discuss how this system can lead to an automatic intervention system that could optimize the social network in real time by facilitating the addition and removal of links based on objective metrics in a socially natural way. We deployed this research platform in a group of 22 employees working in a real organization over a period of one month, and we found that betweenness in the combined social network had a high negative correlation of r = −0.49 (p <0.05) with perceived group interaction quality.

proposes a public interface that functions

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Sociometric badges are wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, prosodic style, physical proximity to other people, and physical activity levels, using social signals derived from vocal features, body motion, and relative location. We present the prior and current state-of-the art in this area of wearable computing and propose several applications that haven’t been fully exploited.

New research reveals the surprising power of ancient — and largely unconscious — forms of human communication.

We present the design, implementation and deployment of a wearable computing research platform for measuring and analyzing human behavior in a variety of settings and applications. We propose the use of wearable sociometric badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels using social signals derived from vocal features, body motion, and relative location to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. We attempt to use on-body sensors in large groups of people for extended periods of time in naturalistic settings for the purpose of identifying, measuring, and quantifying social interactions, information flow, and organizational dynamics. We deployed this research 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 ’ selfassessment of productivity, job satisfaction, and their own perception of group interaction quality. An initial exploratory data analysis indicates that it is possible to automatically