HARF: A Hierarchical Activity Recognition Framework Using Smartphone Sensors

  • Manhyung Han
  • Jae Hun Bang
  • Chris Nugent
  • Sally McClean
  • Sungyoung Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8276)


Activity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables utilizing different sources of sensor data. In this paper, we propose a smartphone based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user’s activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%.


Activity Recognition Smartphone Multimodal Sensors Naïve Bayes Life-log 


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  1. 1.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity Recognition using Cell Phone Accelerometers. ACM SIGKDD Explorations Newsletter 12, 74–82 (2010)CrossRefGoogle Scholar
  2. 2.
    Ward, J.A., Lukowicz, P., Troster, G., Starner, T.: Activity Recognition of Assembly Tasks using Body-Worn Microphones and Accelerometers. IEEE Transactions on Pattern Anal. Mach. Intell. 28, 1553–1567 (2006)CrossRefGoogle Scholar
  3. 3.
    Brezmes, T., Gorricho, J.L., Cotrina, J.: Activity recognition from accelerometer data on a mobile phone. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009, Part II. LNCS, vol. 5518, pp. 796–799. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Lu, H., Yang, J., Liu, Z., Lane, N.D., Choudhury, T., Campbell, A.T.: The Jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys 2010, pp. 71–84 (2010)Google Scholar
  5. 5.
    Peebles, D., Lu, H., Lane, N.D., Choudhury, T., Campbell, A.T.: Community-guided learning: Exploiting mobile sensor users to model human behavior. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15 (2010)Google Scholar
  6. 6.
    Frank, J., Mannor, S., Precup, D.: Activity recognition with mobile phones. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 630–633. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Khan, A.M., Lee, Y.K., Lee, S.Y., Kim, T.S.: A Triaxial Accelerometer-Based Physical-Activity Recognition Via Augmented-Signal Features and a Hierarchical Recognizer. IEEE Transactions on Information Technology in Biomedicine 14(5), 1166–1172 (2010)CrossRefGoogle Scholar
  8. 8.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity Recognition from Accelerometer Data. In: Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence, pp. 1541–1549 (2005)Google Scholar
  9. 9.
    Liao, L., Fox, D., Kautz, H.: Extracting Places and Activities from GPS Traces using Hierarchical Conditional Random Fields. Int. J. Rob. Res. 26, 119–134 (2007)CrossRefGoogle Scholar
  10. 10.
    Vinh, L.T., Lee, S.Y., Park, Y.T., d’Auriol, B.: A Novel Feature Selection Method Based on Normalized Mutual Information. Appl. Intell. 37, 100–120 (2012)CrossRefGoogle Scholar
  11. 11.
    Han, M., Vinh, L.T., Lee, Y.-K., Lee, S.: Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone. Journal of Sensors 12(9), 12588–12605 (2012)CrossRefGoogle Scholar
  12. 12.
    Minnen, D., Starner, T., Ward, J., Lukowicz, P., Troester, G.: Recognizing and discovering human actions from on-body sensor data. In: Proc. IEEE Int. Conf. Multimedia Expo, pp. 1545–1548 (2005)Google Scholar
  13. 13.
    Shen, K.Q., Ong, C.J., Li, X.P., Wilder-Smith, E.P.V.: Novel Multi-Class Feature Selection Methods using Sensitivity Analysis of Posterior Probabilities. In: Proc. of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1116–1121 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Manhyung Han
    • 1
  • Jae Hun Bang
    • 1
  • Chris Nugent
    • 2
  • Sally McClean
    • 3
  • Sungyoung Lee
    • 1
  1. 1.Department of Computer EngineeringKyung Hee University (Global Campus)Korea
  2. 2.School of Computing and MathematicsUniversity of UlsterJordanstownU.K.
  3. 3.School of Computing and Information EngineeringUniversity of UlsterColeraineU.K.

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