2026-09-18T22:52:53Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/421692022-01-13T07:55:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Alex P. Pentland.
Olguín Olguín, Daniel
Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2008-09-03T14:47:10Z
2008-09-03T14:47:10Z
2007
2007
http://hdl.handle.net/1721.1/42169
228845374
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.
Includes bibliographical references (p. 137-144).
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' self-assessment of productivity, job satisfaction, and their own perception of group interaction quality. An initial exploratory data analysis indicates that it is possible to automatically capture patterns of behavior using this wearable platform.
by Daniel Olguín Olguín.
S.M.
144 p.
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Architecture. Program in Media Arts and Sciences.
Sociometric badges : wearable technology for measuring human behavior
Thesis
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<Abstract>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' self-assessment of productivity, job satisfaction, and their own perception of group interaction quality. An initial exploratory data analysis indicates that it is possible to automatically capture patterns of behavior using this wearable platform.</Abstract>
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