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Unformatted text preview: EmotionSense: A Mobile Phones based Adaptive Platform for Experimental Social Psychology Research Kiran K. Rachuri Computer Laboratory University of Cambridge firstname.lastname@example.org Mirco Musolesi School of Computer Science University of St. Andrews email@example.com Cecilia Mascolo Computer Laboratory University of Cambridge firstname.lastname@example.org Peter J. Rentfrow Faculty of Politics, Psychology, Sociology and International Studies University of Cambridge email@example.com Chris Longworth Department of Engineering University of Cambridge firstname.lastname@example.org Andrius Aucinas Computer Laboratory University of Cambridge email@example.com ABSTRACT Todays mobile phones represent a rich and powerful com- puting platform, given their sensing, processing and commu- nication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an excep- tionally suitable tool for conducting social and psychological experiments in an unobtrusive way. In this paper we illustrate EmotionSense, a mobile sens- ing platform for social psychology studies based on mobile phones. Key characteristics include the ability of sensing individual emotions as well as activities, verbal and prox- imity interactions among members of social groups. More- over, the system is programmable by means of a declara- tive language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale ex- periments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies us- ing traditional methods. In particular, we show how speakers and participants emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures. ACM Classification Keywords H.1.2 User/Machine Systems, J.4 Social and Behavioral Sci- ences, I.5 Pattern Recognition. General Terms Algorithms, Design, Experimentation. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee....
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This note was uploaded on 08/25/2011 for the course EEL 6788 taught by Professor Boloni,l during the Spring '08 term at University of Central Florida.
- Spring '08