research-article

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

Authors:

Murali Annavaram,

Quinn A. Jacobson,

Bhaskar Krishnamachari,

Norman SadehAuthors Info & Claims

MobiSys '09: Proceedings of the 7th international conference on Mobile systems, applications, and services

Pages 179 - 192

https://doi.org/10.1145/1555816.1555835

Published: 22 June 2009 Publication History

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

[1]

A. T. Campbell, S. B. Eisenman, K. Fodor, N. D. Lane, H. Lu, E. Miluzzo, M. Musolesi, R. A. Peterson, and X. Zheng. Transforming the social networking experience with sensing presence from mobile phones. In Proceedings of SenSys Raleigh, NC, USA, 2008.

Digital Library

[2]

B. Hoh, M. GruteserandR. Herring, J. Ban, D. Work, J. Herrera, A. M. Bayen, M. Annavaram, and Q. Jacobson. Virtual trip lines for distributed privacy-preserving traffic monitoring. In Proceedings of MobiSys Breckenridge, CO, USA, June 2008.

Digital Library

[3]

Facebook. http://www.facebook.com.

[4]

MySpace. http://www.myspace.com.

[5]

H. W. Gellersen, A. Schmidt, and M. Beigl. Multi-sensor context-awareness in mobile devices and smart artifacts. Mobile Networks and Applications 7(5):341--351, 2002.

Digital Library

[6]

T. Stiefmeier, D. Roggen, G. Troster, G. Ogris, and P. Lukowicz. Wearable activity tracking in car manufacturing. In Pervasive Computing volume 7, pages 42--50, April-June 2008.

Digital Library

[7]

A. Andrew, Y. Anokwa, K. Koscher, J. Lester, and G. Borriello. Context to make you more aware. In Proceedings of ICDCSW Toronto, Ontario, Canada, 2007.

Digital Library

[8]

J. Lester, B. Hannaford, and G. Borriello. Are you with me?-using accelerometers to determine if two devices are carried by the same person. In Pervasive Computing pages 33--50, 2004.

[9]

T. Choudhury, G. Borriello, S. Consolvo, D. Haehnel, B. Harrison, B. Hemingway, J. Hightower, P. Klasnja, K. Koscher, An. LaMarca, J. A. Landay, L. LeGrand, J. Lester, A. Rahimi, A. Rea, and D. Wyatt. The mobile sensing platform:An embedded activity recognition system. In Pervasive Computing volume 7, pages 32--41, 2008.

Digital Library

[10]

N. D. Lane, H. Lu, S. B. Eisenman, and A. T. Campbell. Cooperative techniques supporting sensor-based people -centric inferencing. Lecture Notes in Computer Science 5013/2008:75--92, 2008.

Digital Library

[11]

P. Zappi, T. Stiefmeier, E. Farella, D. Roggen, L. Benini, and G. Troster. Activity recognition from on-body sensors by classifier fusion:sensor scalability and robustness. In Proceedings of ISSNIP 2007.

[12]

L. Bao and S. S. Intille. Activity recognition from user-annotated acceleration data. Pervasive Computing pages 1--17, 2004.

[13]

A. Schmidt, K. A. Aidoo, A. Takaluoma, U. Tuomela, K. V. Laerhoven, and W. V. D. Velde. Advanced interaction in context. In Proceedings of HUC Karlsruhe, Germany, 1999.

Digital Library

[14]

J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava. Participatory sensing. In WSW Workshop at SenSys Boulder, Colorado, USA, 2006.

[15]

D. Siewiorek, A. Smailagic, J. Furukawa, N. Moraveji, K. Reiger, and J. Shaffer. Sensay:a context-aware mobile phone. In Proceedings of ISWC White Plains, NY, USA, 2003.

Digital Library

[16]

E. Miluzzo, N. D. Lane, S. B. Eisenman, and A. T. Campbell. Cenceme -injecting sensing presence into social networking applications. In Gerd Kortuem, Joe Finney, Rodger Lea, and Vasughi Sundramoorthy, editors, EuroSSC volume 4793 of Lecture Notes in Computer Science pages 1--28. Springer, 2007.

Digital Library

[17]

E. Miluzzo, N. Lane, K. Fodor, R. Peterson, S. Eisenman, H. Lu, M. Musolesi, X. Zheng, and A. Campbell. Sensing meets mobile social networks: The design, implementation and evaluation of the cenceme application. In Proceedings of SenSys Raleigh, NC, USA, November 2008.

Digital Library

[18]

S. Gaonkar, J. Li, R. R. Choudhury, L. Cox, and A. Schmidt. Micro-blog:sharing and querying content through mobile phones and social participation. In Proceedings of MobiSys Breckenridge, Colorado, USA, 2008.

Digital Library

[19]

E. Welbourne, J. Lester, A. LaMarca, and G. Borriello. Mobile context inference using low-cost sensors. In Workshop on Location- and Context-Awareness Boulder, Colorado, USA, 2005.

Digital Library

[20]

P. Mohan, V. Padmanabhan, and R. Ramjee. Nericell: Rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of SenSys Raleigh, NC, USA, 2008.

Digital Library

[21]

K. Whitesell, B. Kutler, N. Ramanathan, and D. Estrin. Determining indoor air quality from images of an air filter captured on cell phones. In ImageSense Workshop at SenSys Raleigh, NC, USA, 2008.

[22]

J. Lester, T. choudhury, G. Borriello, S. Consolvo, J. Landay, K. Everitt, and I. Smith. Sensing and modeling activities to support physical fitness. In Proceedings of UbiComp Tokyo, Japan, 2005.

[23]

S. Biswas and M. Quwaider. Body posture identification using hidden markov model with wearable sensor networks. In BodyNets Workshop Tempe, AZ, USA, March 2008.

Digital Library

[24]

M. Annavaram, N. Medvidovic, U. Mitra, S. Narayanan, D. Spruijt-Metz, G. Sukhatme, Z. Meng, S. Qiu, R. Kumar, and G. Thatte. Multimodal sensing for pediatric obesity applications. In UrbanSense08 Workshop at SenSys Raleigh, NC, USA, November 2008.

[25]

T. Gao, C. Pesto, L. Selavo, Y. Chen, J. Ko, J. Kim, A. Terzis, A. Watt, J. Jeng, B. Chen, K. Lorincz, and M. Welsh. Wireless medical sensor networks in emergency response:Implementation and pilot results. In HST Waltham, MA, USA, May 2008.

[26]

D. Jea, J. Liu, T. Schmid, and M. Srivastava. Hassle free fitness monitoring. In HealthNet Workshop at MobiSys Brekenridge, CO, USA, 2008.

Digital Library

[27]

W. H. Wu, L. K. Au, B. Jordan, T. Stathopoulos, M. A. Batalin, W. J. Kaiser, A. Vahdatpour, M. Sarrafzadeh, M. Fang, and J. Chodosh. Smartcane system:An assistive device for geriatrics. In BodyNets Workshop Tempe, AZ, USA, 2008.

Digital Library

[28]

M. A. Viredaz, L. S. Brakmo, and W. R. Hamburgen. Energy management on handheld devices. ACM Queue 1:44--52, 2003.

Digital Library

[29]

A. Krause, M. Ihmiq, E. Rankin, D. Leong, G. Smriti, D. Siewiorek, A. Smailaqic, M. Deisher, and U. Senqupta. Trading off prediction accuracy and power consumption for context-aware wearable computing. In Proceedings of ISWC Osaka, Japan, 2005.

Digital Library

[30]

P. Pillai and K. G. Shin. Real-time dynamic voltage scaling for low-power embedded operating systems. In Proceedings of SOSP Banff, Alberta, Canada, 2001.

Digital Library

[31]

E. Shih, P. Bahl, and M. J. Sinclair. Wake on wireless: an event driven energy saving strategy for battery operated devices. In Proceedings of MobiCom Atlanta, Georgia, USA, 2002.

Digital Library

[32]

J. Sorber, N. Banerjee, M. D. Corner, and S. Rollins. Turducken:hierarchical power management for mobile devices. In Proceedings of MobiSys Seattle, Washington, USA, 2005.

Digital Library

[33]

S. Kang, J. Lee, H. Jang, H. Lee, Y. Lee, S. Park, T. Park, and J. Song. Seemon:scalable and energy-efficient context monitoring framework for sensor-rich mobile environments. In Proceedings of MobiSys Breckenridge, CO, USA, 2008.

Digital Library

[34]

Nokia Energy Profiler. http://www.forum.nokia.com/main/resources/user experience/powermanagement/nokia energy profiler/.

[35]

SKYHOOK Wireless. http://www.skyhookwireless.com/.

[36]

G. Lu and T. Hankinson. A technique towards automatic audio classification and retrieval. In Proceedings of ICSP Beijing, China, 1998.

[37]

B. Gajic and K. K. Paliwal. Robust speech recognition in noisy environments based on subband spectral centroid histograms. In IEEE Transactions on speech and audio processing pages 600--608, 2006.

Digital Library

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Index Terms

A framework of energy efficient mobile sensing for automatic user state recognition
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

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Published In

cover image ACM Conferences

MobiSys '09: Proceedings of the 7th international conference on Mobile systems, applications, and services

June 2009

370 pages

ISBN:9781605585666

DOI:10.1145/1555816

Co-chair:
Krzysztof Zielinski
AGH University of Science and Technology, Poland
,
General Chair:
Adam Wolisz
Technische Universität Berlin, Germany
,
Program Chairs:
Jason Flinn
University of Michigan, USA
,
Anthony LaMarca
Intel Research Seattle, USA

Copyright © 2009 ACM.

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. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 22 June 2009

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Mobisys '09: The 7th International Conference on Mobile Systems, Applications, and Services

June 22 - 25, 2009

Kraków, Poland

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Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

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https://doi.org/10.1109/TMC.2022.3227299
Nagarajan SDevarajan GMohammed ARamana TGhosh U(2023)Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical SystemsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.322384410:5(2429-2438)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TNSE.2022.3223844
Keyvanpour MMehrmolaei SShojaeddini SEsmaeili F(2023)HAR-CO: A comparative analytical review for recognizing conventional human activity in stream data relying on challenges and approachesMultimedia Tools and Applications10.1007/s11042-023-16795-883:14(40811-40856)Online publication date: 10-Oct-2023
https://doi.org/10.1007/s11042-023-16795-8
Wang DZhang DWang DZhang D(2023)Further ReadingsSocial Edge Computing10.1007/978-3-031-26936-3_8(155-163)Online publication date: 20-Feb-2023
https://doi.org/10.1007/978-3-031-26936-3_8
Pedram MSah RRokni SNourollahi MGhasemzadeh H(2022)Probabilistic Cascading Classifier for Energy-Efficient Activity Monitoring in WearablesIEEE Sensors Journal10.1109/JSEN.2022.317588122:13(13407-13423)Online publication date: 1-Jul-2022
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Nam HFleming SBrunskill ERanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Reinforcement learning with state observation costs in action-contingent noiselessly observable markov decision processesProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541459(15650-15666)Online publication date: 6-Dec-2021
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