Mobile Networks and Applications

, Volume 18, Issue 1, pp 60–80 | Cite as

MobiSens: A Versatile Mobile Sensing Platform for Real-World Applications

Article

Abstract

We present the design, implementation and evaluation of MobiSens, a versatile mobile sensing platform for a variety of real-life mobile sensing applications. MobiSens addresses common requirements of mobile sensing applications on power optimization, activity segmentation, recognition and annotation, interaction between mobile client and server, motivating users to provide activity labels with convenience and privacy concerns. After releasing three versions of MobiSens to the Android Market with evolving UI and increased functionalities, we have collected 13,993 h of data from 310 users over five months. We evaluate and compare the user experience and the sensing efficiency in each release. We show that the average number of activities annotated by a user increases from 0.6 to 6. This result indicates the activity auto-segmentation/recognition feature and certain UI design changes significantly improve the user experience and motivate users to use MobiSens more actively. Based on the MobiSens platform, we have developed a range of mobile sensing applications including Mobile Lifelogger, SensCare for assisted living, Ground Reporting for soldiers to share their positions and actions horizontally and vertically, and CMU SenSec, a behavior-driven mobile Security system.

Keywords

Mobile sensing Activity recognition Lifelogger Anomaly detection Human behavior modeling 

References

  1. 1.
    Adamson DM, Burnam MA, Burns RM, Caldarone LB, Cox RA, D’Amico E, Diaz C, Eibner C, Fisher G, Helmus TC, Tanielian T, Karney BR, Kilmer B, Marshall GN, Martin LT, Meredith LS, Metscher KN, Osilla KC, Pacula RL, Ramchand R, Ringel JS, Schell TL, Sollinger JM, Jaycox LH, Vaiana ME, Williams KM, Yochelson MR (2008) Invisible wounds of war: psychological and cognitive injuries, their consequences, and services to assist recovery. RAND Corporation, Santa Monica, CAGoogle Scholar
  2. 2.
    Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. Springer, New York, pp 1–17Google Scholar
  3. 3.
    Burke J, Estrin D, Hansen M, Parker A, Ramanathan N, Reddy S, Srivastava MB (2006) Participatory sensing, pp 117–134Google Scholar
  4. 4.
    Buthpitiya S, Zhang Y, Dey A, Griss M (2011) n-gram geo-trace modelingGoogle Scholar
  5. 5.
    Chennuru S, Chen P-w, Zhu J, Zhang Y (2010) Mobile lifelogger—recording, indexing, and understanding a mobile user’s life. In: MobiCase (September 2009)Google Scholar
  6. 6.
    Choudhury T, Consolvo S, Harrison B, Hightower J, LaMarca A, LeGrand L, Rahimi A, Rea A, Bordello G, Hemingway B, Klasnja P, Koscher K, Landay J, Lester J, Wyatt D, Haehnel D (2008) The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Computing 7(2):32–41CrossRefGoogle Scholar
  7. 7.
    Duong TV, Bui HH, Phung DQ, Venkatesh S (2005) Activity recognition and abnormality detection with the switching hidden semi-Markov model. In: Proceedings of the 2005 IEEE Computer Society conference on computer vision and pattern recognition (CVPR’05), vol 1. IEEE Computer Society, Washington, DC, pp 838–845CrossRefGoogle Scholar
  8. 8.
    Eagle N, Pentland A (2005) Reality mining: sensing complex social systemsGoogle Scholar
  9. 9.
    Falaki H, Mahajan R, Kandula S, Lymberopoulos D, Govindan R, Estrin D (2010) Diversity in smartphone usage. In: MobiSys ’10: Proceedings of the 8th international conference on mobile systems, applications and services. ACM, New YorkGoogle Scholar
  10. 10.
    Ghasemzadeh H, Barnes J, Guenterberg E, Jafari R (2008) View-invariant modeling and recognition of human actions using grammars. In: 5th IEEE international conference on mobile ad hoc and sensor systems, 2008. MASS 2008. IEEE, Piscataway, pp 58–68Google Scholar
  11. 11.
    Guerra-Filho G, Fermuller C, Aloimonos Y (2005) Discovering a language for human activity. In: Proceedings of the AAAI 2005 fall symposium on anticipatory cognitive embodied systems, Washington, DCGoogle Scholar
  12. 12.
    Herrera JC, Work DB, Herring R, Ban XJ, Jacobson Q, Bayen AM (2010) Evaluation of traffic data obtained via gps-enabled mobile phones: the mobile century field experiment. Transp Res, Part C Emerg Technol 18(4):568–583CrossRefGoogle Scholar
  13. 13.
    Jiang Y, Li D, Yang G, Lv Q, Liu Z (2011) Deliberation for intuition: a framework for energy-efficient trip detection on cellular phones. In: Proceedings of the 13th international conference on ubiquitous computing, UbiComp ’11. ACM, New York, pp 315–324CrossRefGoogle Scholar
  14. 14.
    Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE (2005) Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Arch Gen Psychiatry 62:593–602CrossRefGoogle Scholar
  15. 15.
    Lapinski M, Berkson E, Gill T, Reinold M, Paradiso JA (2009) A distributed wearable, wireless sensor system for evaluating professional baseball pitchers and batters. In: Proceedings of the 2009 international symposium on wearable computers, ISWC ’09. IEEE Computer Society, Washington, DC, pp 131–138CrossRefGoogle Scholar
  16. 16.
    Logan B, Healey J, Philipose M, Tapia E, Intille S (2007) A long-term evaluation of sensing modalities for activity recognition. In: Proceedings of the 9th international conference on ubiquitous computing. Springer, Berlin, pp 483–500Google Scholar
  17. 17.
    Miluzzo E, Lane ND, Fodor K, Peterson R, Lu H, Musolesi M, Eisenman SB, Zheng X, Campbell AT (2008) Sensing meets mobile social networks: The design, implementation and evaluation of the cenceme application. In: Proceedings of the international conference on embedded networked sensor systems (SenSys). ACM Press, New York, pp 337–350Google Scholar
  18. 18.
    MIT MediaLab. Funf open sensing framework (2011) http://funf.media.mit.edu/about.html. 16 Dec 2011
  19. 19.
    Nguyen LT, Cheng H-T, Wu P, Buthpitiya S, Zhang Y (2012) Pnlum: system for prediction of next location for users with mobility. In: Proceedings of mobile data challenge by Nokia workshop at the tenth international conference on pervasive computing, Newcastle, UKGoogle Scholar
  20. 20.
    Pantelopoulos A, Bourbakis NG (2010) A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern, Part C Appl Rev 40(1):1–12.CrossRefGoogle Scholar
  21. 21.
    Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput 28(6):976–990CrossRefGoogle Scholar
  22. 22.
    Roy P, Bouzouane A, Giroux S, Bouchard B (2011) Possibilistic activity recognition in smart homes for cognitively impaired people. Appl Artif Intell 25(10):883–926.CrossRefGoogle Scholar
  23. 23.
    Ryoo MS, Aggarwal JK (2006) Recognition of composite human activities through context-free grammar based representation. In: 2006 IEEE Computer Society conference on computer vision and pattern recognition, CVPR06, vol 2, pp 1709–1718Google Scholar
  24. 24.
    Soucy P, Mineau GW (2005) Beyond TFIDF weighting for text categorization in the vector space model. In: Proceedings of the 19th international joint conference on artificial intelligence (IJCAI 2005), pp 1130–1135Google Scholar
  25. 25.
    Tapia E, Intille S, Larson K (2004) Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha A, Mattern F (eds) Pervasive computing, vol 3001 of Lecture notes in computer science, chapter 10. Springer, Berlin, pp 158–175Google Scholar
  26. 26.
    van Kasteren T, Noulas A, Englebienne G, Kröse B (2008) Accurate activity recognition in a home setting. In: Proceedings of the 10th international conference on ubiquitous computing, UbiComp ’08. ACM, New York, pp 1–9CrossRefGoogle Scholar
  27. 27.
    Varkey JP, Pompili D, Walls T (2011) Human motion recognition using a wireless sensor-based wearable system. In: Personal and ubiquitous computing. Springer, Berlin, pp 1–14Google Scholar
  28. 28.
    Virone G, Wood A, Selavo L, Cao Q, Fang L, Doan T, He Z, Stoleru R, Lin S, Stankovic JA (2006) An advanced wireless sensor network for health monitoringGoogle Scholar
  29. 29.
    Woodbridge J, Nahapetian A, Noshadi H, Sarrafzadeh M, Kaiser W (2009) Wireless health and the smart phone conundrum. SIGBED Rev 6(2):11:1–11:6CrossRefGoogle Scholar
  30. 30.
    Wu P, Peng H-K, Zhu J, Zhang Y (2011) Senscare: semi-automatic activity summarization system for elderly care. In: International conference on mobile computing, applications, and services (MobiCASE)Google Scholar
  31. 31.
    Wu W, Au L, Jordan B, Stathopoulos T, Batalin M, Kaiser W, Vahdatpour A, Sarrafzadeh M, Fang M, Chodosh J (2008) The smartcane system: an assistive device for geriatrics. In: Proceedings of the ICST 3rd international conference on body area networks, BodyNets ’08. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels, pp 2:1–2:4Google Scholar
  32. 32.
    Zhu J, Wu P, Wang X, Perrig A, Hong J, Zhang JY (2013) Sensec: mobile application security through passive sensing. In: Proceedings of international conference on computing, networking and communications (ICNC 2013). San Diego, CA, USA, 28–31 January 2013Google Scholar
  33. 33.
    Zhu J, Zhang Y (2011) Towards accountable mobility model: A language approach on user behavior modeling in office WiFi networks. In: Proceedings of the IEEE international conference on computer communications and networks (ICCCN 2011). Maui, Hawaii, 31 July–4 August 2011Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Carnegie Mellon University, Silicon Valley CampusMoffett FieldUSA

Personalised recommendations