A framework of energy efficient mobile sensing for automatic user state recognition

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A framework of energy efficient mobile sensing for automatic user state recognition

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International Conference On Mobile Systems, Applications And Services archive
Proceedings of the 7th international conference on Mobile systems, applications, and services table of contents

Kraków, Poland

SESSION: Mobile sensing and inference table of contents

Pages 179-192

Year of Publication: 2009

ISBN:978-1-60558-566-6

Authors

Yi Wang	University of Southern California, Los Angeles, CA, USA
Jialiu Lin	Carnegie Mellon University, Pittsburgh, USA
Murali Annavaram	University of Southern California, Los Angeles, CA, USA
Quinn A. Jacobson	Nokia Research Center, Palo Alto, CA, USA
Jason Hong	Carnegie Mellon University, Pittsburgh, USA
Bhaskar Krishnamachari	University of Southern California, Los Angeles, CA, USA
Norman Sadeh	Carnegie Mellon University, Pittsburgh, USA

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

Urban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, continuously capturing this contextual information on mobile devices consumes huge amount of energy. In this paper, we present a novel design framework for an Energy Efficient Mobile Sensing System (EEMSS). EEMSS uses hierarchical sensor management strategy to recognize user states as well as to detect state transitions. By powering only a minimum set of sensors and using appropriate sensor duty cycles EEMSS significantly improves device battery life. We present the design, implementation, and evaluation of EEMSS that automatically recognizes a set of users' daily activities in real time using sensors on an off-the-shelf high-end smart phone. Evaluation of EEMSS with 10 users over one week shows that our approach increases the device battery life by more than 75% while maintaining both high accuracy and low latency in identifying transitions between end-user activities.

REFERENCES

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