Applied Intelligence

, Volume 35, Issue 2, pp 226–241 | Cite as

Semi-Markov conditional random fields for accelerometer-based activity recognition

  • La The Vinh
  • Sungyoung Lee
  • Hung Xuan Le
  • Hung Quoc Ngo
  • Hyoung Il Kim
  • Manhyung Han
  • Young-Koo Lee
Article

Abstract

Activity recognition is becoming an important research area, and finding its way to many application domains ranging from daily life services to industrial zones. Sensing hardware and learning algorithms are two important components in activity recognition. For sensing devices, we prefer to use accelerometers due to low cost and low power requirement. For learning algorithms, we propose a novel implementation of the semi-Markov Conditional Random Fields (semi-CRF) introduced by Sarawagi and Cohen. Our implementation not only outperforms the original method in terms of computation complexity (at least 10 times faster in our experiments) but also is able to capture the interdependency among labels, which was not possible in the previously proposed model. Our results indicate that the proposed approach works well even for complicated activities like eating and driving a car. The average precision and recall are 88.47% and 86.68%, respectively, which are higher than results obtained by using other methods such as Hidden Markov Model (HMM) or Topic Model (TM).

Keywords

Activity recognition Wearable sensors Accelerometer Hidden Markov Model (HMM) Conditional Random Fields (CRF) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Proceedings of the 2nd international conference on pervasive computing, vol 3001, pp 1–17 Google Scholar
  2. 2.
    Blanke U, Schiele B (2009) Daily routine recognition through activity spotting. In: Proceedings of the 4th international symposium on location and context-awareness, vol 5561, pp 192–206 Google Scholar
  3. 3.
    Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge MATHGoogle Scholar
  4. 4.
    Brdiczka O, Maisonnasse J, Reignier P, Crowley JL (2009) Detecting small group activities from multimodal observations. Appl Intell 30:47–57 CrossRefGoogle Scholar
  5. 5.
    Doherty MK (2007) Gene prediction with conditional random fields. Master’s thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Google Scholar
  6. 6.
    Favela J, Tentori M, Castro LA, Gonzalez VM, Moran EB, Martínez-García AI (2007) Activity recognition for context-aware hospital applications: issues and opportunities for the deployment of pervasive networks. Mob Netw Appl 12:155–171 CrossRefGoogle Scholar
  7. 7.
    Huynh T, Blanke U, Schiele B (2007) Scalable recognition of daily activities with wearable sensors. In: Proceedings of the 3rd international symposium on location and context-awareness, vol 4718, pp 50–67 Google Scholar
  8. 8.
    Huynh T, Fritz M, Schiele B (2008) Discovery of activity patterns using topic models. In: Proceedings of the 10th international conference on ubiquitous computing, vol 344, pp 10–19 Google Scholar
  9. 9.
    Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th international conference on machine learning, pp 282–289 Google Scholar
  10. 10.
    Lampe M, Strassner M, Fleisch E (2004) A ubiquitous computing environment for aircraft maintenance. In: Proceedings of ACM symposium on applied computing, pp 1586–1592 Google Scholar
  11. 11.
    Liao L, Choudhury T, Fox D, Kautz HA (2007) Training conditional random fields using virtual evidence boosting. In: Proceedings of the 20th international joint conference on artificial intelligence, pp 2530–2535 Google Scholar
  12. 12.
    Liao L, Fox D, Kautz HA (2007) Extracting places and activities from gps traces using hierarchical conditional random fields. Int J Rob Res 26:119–134 CrossRefGoogle Scholar
  13. 13.
    Lukowicz P, Starner TE, Troster G, Ward JA (2006) Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Trans Pattern Anal Mach Intell 28:1553–1567 CrossRefGoogle Scholar
  14. 14.
    Najafi B, Aminian K, Loew F, Blanc Y, Robert PA (2002) Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Trans Biomed Eng 49:843–851 CrossRefGoogle Scholar
  15. 15.
    Okanohara D, Miyao Y, Tsuruoka Y, Tsujii J (2006) Improving the scalability of semi-Markov conditional random fields for named entity recognition. In: Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the association for computational linguistics, pp 465–472 Google Scholar
  16. 16.
    Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. In: Proceedings of the IEEE, vol 77, pp 257–286 Google Scholar
  17. 17.
    Ravi N, Dandekar N, Mysore P, Littman ML (2005) Activity recognition from accelerometer data. In: Proceedings of the 20th national conference on artificial intelligence, vol 20, pp 1541–1546 Google Scholar
  18. 18.
    Sánchez D, Tentori M, Favela J (2008) Activity recognition for the smart hospital. IEEE Intell Syst 23:50–57 CrossRefGoogle Scholar
  19. 19.
    Sarawagi S, Cohen W (2005) Semi-Markov conditional random fields for information extraction. In: Proceedings of the 2004 conference on neural information processing systems, pp 1185–1192 Google Scholar
  20. 20.
    Suutala J, Pirttikangas S, Röning J (2007) Discriminative temporal smoothing for activity recognition from wearable sensors. In: Proceedings of the 4th international symposium on ubiquitous computing systems, vol 4836, pp 182–195 Google Scholar
  21. 21.
    Tapia EM, Intille SS, Larson K (2004) Activity recognition in the home setting using simple and ubiquitous sensors. In: Proceedings of the 2nd international conference on pervasive computing, vol 3001, pp 158–175 Google Scholar
  22. 22.
    Truyen TT, Phung DQ, Bui HH, Venkatesh S (2009) Hierarchical semi-Markov conditional random fields for recursive sequential data. In: Proceedings of the 22nd annual conference on neural information processing systems, pp 1657–1664 Google Scholar
  23. 23.
    Vail DL, Veloso MM, Lafferty JD (2007) Conditional random fields for activity recognition. In: Proceedings of the 6th international joint conference on autonomous agents and multi-agent systems, p 235 Google Scholar
  24. 24.
    World Health Organization (2004) Global strategy on diet, physical activity and health. Technical report Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • La The Vinh
    • 1
  • Sungyoung Lee
    • 1
  • Hung Xuan Le
    • 1
  • Hung Quoc Ngo
    • 1
  • Hyoung Il Kim
    • 1
  • Manhyung Han
    • 1
  • Young-Koo Lee
    • 1
  1. 1.Dept. of Computer EngineeringKyung Hee UniversityGyeonggi-doRepublic of Korea

Personalised recommendations