Talk1: EmotionSense: A Mobile Phones based Adaptive Platform for Experimental Social Psychology Research, Talk2: Energy-Accuracy Trade-offs in Querying Sensor Data for Continuous Sensing Mobile Systems

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• Kiran Rachuri (University of Cambridge)
• Tuesday 21 September 2010, 13:30-14:30
• FW11, Computer Laboratory, William Gates Builiding.

If you have a question about this talk, please contact Eiko Yoneki.

Talk1-Abstract: Today’s mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way. In this paper we illustrate EmotionSense, a mobile sensing platform for social psychology studies based on mobile phones. Key characteristics include the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative 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 experiments 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 using 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.

Talk1 is a dry run for UbiComp 2010.

Talk 2 – Abstract: A large number of context-inference applications run on off-the-shelf smart-phones and infer context from the data acquired by means of the sensors embedded in these devices. The use of efficient and effective sampling technique is of key importance for these applications. Aggressive sampling can ensure a more fine-grained and accurate reconstruction of context information but, at the same time, continuous querying of sensor data might lead to rapid battery depletion. In this paper, we present an adaptive sensor sampling methodology which relies on dynamic selection of sampling functions depending on history of context events. We also report on the experimental evaluation of a set of functions that control the rate at which the data are sensed from the Bluetooth device, accelerometer, and microphone sensors and we show that a dynamic adaptation mechanism provides a better trade-offs compared to simpler function based rate control methods. Furthermore, we show that the suitability of these mechanisms varies for each of the sensors, and the accuracy and energy consumption values stabilize after reaching a certain level.

Talk2 is a dry run for Workshop on Mobile context awareness workshop at UbiComp 2010.

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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