Skip to main content
SpringerLink
Log in

Layered hidden Markov models to recognize activity with built-in sensors on Android smartphone

  • Industrial and Commercial Application
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Recently, activity recognition using built-in sensors in a mobile phone becomes an important issue. It can help us to provide context-aware services: health care, suitable content recommendation for a user’s activity, and user adaptive interface. This paper proposes a layered hidden Markov model (HMM) to recognize both short-term activity and long-term activity in real time. The first layer of HMMs detects short, primitive activities with acceleration, magnetic field, and orientation data, while the second layer exploits the inference of the previous layer and other sensor values to recognize goal-oriented activities of longer time period. Experimental results demonstrate the superior performance of the proposed method over the alternatives in classifying long-term activities as well as short-term activities. The performance improvement is up to 10 % in the experiments, depending on the models compared.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Lane ND, Miluzzo E, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150

    Article  Google Scholar 

  2. Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor 15(3):1192–1209

    Article  Google Scholar 

  3. Bulling A, Blanke U, Schiele B (2014) A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv 46(3):1–33

    Article  Google Scholar 

  4. Chen L, Hoey J, Nugent C, Cook D, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):790–808

    Article  Google Scholar 

  5. Choudhury T, Consolvo S, Harrison B, Hightower J, LaMarca A, LeGrand L, Rahimi A, Rea A, Borriello G, Hemingway B, Klasnja PP, Koscher K, Landay JA, Lester J, Wyatt D, Haehnel D (2008) The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput 7(2):32–41

    Article  Google Scholar 

  6. Hwang K-S, Cho S-B (2009) Landmark detection from mobile life log using a modular Bayesian network model. Expert Syst Appl 36(10):12065–12076

    Article  Google Scholar 

  7. Bieber G, Voskamp J, Urban B (2009) Activity recognition for everyday life on mobile phones. In: Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction, part II: intelligent and ubiquitous interaction environments 289–296

  8. Park S, Aggarwal KJ (2004) A hierarchical Bayesian network for event recognition of human actions and interactions. Multimedia Syst 10(9):164–179

    Article  Google Scholar 

  9. Wang AW-H, Tung C-L (2008) Dynamic gesture recognition based on dynamic Bayesian networks. WSEAS Trans Bus Econ 4(11):168–173

    Google Scholar 

  10. Du Y, Chen F, Xu W, Zhang W (2006) Interacting activity recognition using hierarchical durational-state dynamic Bayesian network. Lect Notes Comput Sci 4261:185–192

    Article  Google Scholar 

  11. Min J-K, Hong J-H, Cho S-B (2015) Combining localized fusion and dynamic selection for high-performance SVM. Expert Syst Appl 42:9–20

    Article  Google Scholar 

  12. Song SK, Jang J, Park S (2008) A phone for human activity recognition using triaxial acceleration sensor. IEEE Int Conf Consum Electron 1–2. doi:10.1109/ICCE.2008.4587903

  13. Zappi P, Lombriser C, Stiefmeier T, Farella E, Roggen D, Benini L, Tröster G (2008) Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. Lect Notes Comput Sci 4913:17–43

    Article  Google Scholar 

  14. Ganti RK, Jayachandran P, Abdelzaher TF, Stankovic JA (2006) Satire: a software architecture for smart attire. In: Proceedings of the 4th International Conference on Mobile Systems, Applications and Services 110–123

  15. Khan AM, Lee YK, Lee SY, Kim TS (2010) Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis. In: 5th International Conference on Future Information Technology 1–6

  16. Kwapisz JR, Weiss GM, Moore SA (2010) Activity recognition using cell phone accelerometers. In: 4th ACM SIGKDD International Workshop on Knowledge Discovery from Sensor Data

  17. Maguire D, Frisby R (2009) Comparison of feature classification algorithm for activity recognition based on accelerometer and heart rate data. In: 9th IT & T Conference

  18. Győrbíró N, Fábián Á, Hományi G (2009) An activity recognition system for mobile phone. Mob Netw Appl 14(1):82–91

    Article  Google Scholar 

  19. Berchtold M, Budde M, Gordon D, Schmidtke H, Beigl M (2010) ActiServ: activity recognition service for mobile phones. In: 2010 International Symposium on Wearable Computers 1–8

  20. Gupta P, Dallas T (2014) Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans Biomed Eng 61(6):1780–1786

    Article  Google Scholar 

  21. Pei L, Guinness R, Chen R, Liu J, Kuusniemi H, Chen Y, Chen L, Kaistinen J (2013) Human behavior cognition using smartphone sensors. Sensors 13(2):1402–1424

    Article  Google Scholar 

  22. Travelsi D, Mohammed S, Chamroukhi F, Oukhellou L, Amirat Y (2013) An unsupervised approach for automatic activity recognition based on hidden Markov model regression. IEEE Trans Autom Sci Eng 10(3):829–835

    Article  Google Scholar 

  23. Lu H, Pan W, Lane ND, Choudhury T, Campbell AT (2009) SoundSense: Scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of the 7th International Conference on Mobile Systems 165–178

  24. Reddy S, Burke J, Estrin D, Hansen M, Srivastava M (2008) Determining transportation mode on mobile phones. In: Proceedings of 12th IEEE International Symposium on Wearable Computers 25–28

  25. Yang J-Y, Chen Y-P, Lee G-Y, Liou S-N, Wang J-S (2007) Activity recognition using one triaxial accelerometer: a neuro-fuzzy classifier with feature reduction. Lect Notes Comput Sci 4740:395–400

    Article  Google Scholar 

  26. Aarno D, Kragic D (2006) Layered HMM for motion intention recognition. IEEE RSJ Int Conf Intell Robot Syst 5130–5135. doi:10.1109/IROS.2006.282606

  27. Mengistu KT, Hannemann M, Baum T, Wendemuth A (2008) Hierarchical HMM-based semantic concept labeling model. IEEE Spoken Language Technology Workshop, IEEE, Goa, pp 57–60. doi:10.1109/SLT.2008.4777839

  28. Oliver N, Garg A, Horvitz E (2004) Layered representations for learning and inferring office activity from multiple sensory channels. Comput Vision Image Underst 96(2):163–180

    Article  Google Scholar 

  29. Michalak K, Kwaśnicka H (2006) Correlation-based feature selection strategy in classification problems. Appl Math Comput Sci 16(4):503–511

    MathSciNet  MATH  Google Scholar 

  30. Huynh T, Schiele B (2005) Analyzing features for activity recognition. In: Proceedings of the Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-aware Services: Usages and Technologies 159–164

  31. Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. Lect Notes Comput Sci 3001:1–17

    Article  Google Scholar 

  32. Kern N, Schiele B, Schmidt A (2003) Multi-sensor activity context detection for wearable computing. Lect Notes Comput Sci 2875:220–232

    Article  Google Scholar 

  33. Lee Y-S, Cho S-B (2011) Activity recognition using hierarchical hidden Markov models on a smartphone with 3D accelerometer. Lect Notes Artif Intell 6678:460–467

    Google Scholar 

  34. Reddy S, Mun M, Burke J, Estrin D, Hansen M, Srivastava M (2010) Using mobile phones to determine transportation modes. ACM Trans Sens Netw 6(2) article 13. doi:10.1145/1689239.1689243

  35. Peirolo R (2011) Information gain as a score for probabilistic forecasts. Meteorol Appl 18:9–17

    Article  Google Scholar 

  36. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  37. Chen FR, Wilcox LD, Bloomberg DS (1995) A comparison of discrete and continuous hidden Markov models for phrase spotting in text images. In: Proceedings of the Third International Conference on Document Analysis and Recognition 398–402

Download references

Acknowledgments

This work was supported by the industrial strategic technology development program, 10044828, Development of augmenting multisensory technology for enhancing significant effect on service industry, funded by the Ministry of Trade, Industry & Energy (MI, Korea).

Author information

Authors and Affiliations

  1. Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-749, South Korea

    Young-Seol Lee & Sung-Bae Cho

Authors
  1. Young-Seol Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sung-Bae Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sung-Bae Cho.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, YS., Cho, SB. Layered hidden Markov models to recognize activity with built-in sensors on Android smartphone. Pattern Anal Applic 19, 1181–1193 (2016). https://doi.org/10.1007/s10044-016-0549-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-016-0549-8

Keywords

Search

Navigation