Skip to main content
SpringerLink
Log in
Book cover

International Conference on Ubiquitous Computing and Ambient Intelligence

UCAmI 2015: Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information pp 265–276Cite as

Building Personalized Activity Recognition Models with Scarce Labeled Data Based on Class Similarities

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9454))

Abstract

With the recent advent of new devices with embedded sensors, Human Activity Recognition (HAR) has become a trending topic in the last years because of its potential applications in pervasive health care, assisted living, exercise monitoring, etc. Most of the works on HAR either require from the user to label the activities as they are performed so the system can learn them, or rely on a trained device that expects a “typical” ideal user. The first approach is impractical, as the training process easily become time consuming, expensive, etc., while the second one drops the HAR precision for many non-typical users. In this work we propose a “crowdsourcing” method for building personalized models for HAR by combining the advantages of both user-dependent and general models by finding class similarities between the target user and the community users. We evaluated our approach on 4 different public datasets and showed that the personalized models outperformed the user-dependent and general models when labeled data is scarce.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Brush, A., Krumm, J., Scott, J.: Activity recognition research: the good, the bad, and the future. In: Proceedings of the Pervasive 2010 Workshop on How to Do Good Research in Activity Recognition, Helsinki, Finland, pp. 17–20 (2010)

    Google Scholar 

  2. Martínez-Pérez, F.E., González-Fraga, J.Á., Cuevas-Tello, J.C., Rodríguez, M.D.: Activity inference for ambient intelligence through handling artifacts in a healthcare environment. Sensors 12(1), 1072–1099 (2012)

    Article  Google Scholar 

  3. Han, Y., Han, M., Lee, S., Sarkar, A.M.J., Lee, Y.-K.: A framework for supervising lifestyle diseases using long-term activity monitoring. Sensors 12(5), 5363–5379 (2012)

    Article  Google Scholar 

  4. Mitchell, E., Monaghan, D., O’Connor, N.E.: Classification of sporting activities using smartphone accelerometers. Sensors 13(4), 5317–5337 (2013)

    Article  Google Scholar 

  5. Banos, O., Galvez, J.-M., Damas, M., Pomares, H., Rojas, I.: Window size impact in human activity recognition. Sensors 14(4), 6474–6499 (2014)

    Article  Google Scholar 

  6. Andrea Mannini and Angelo Maria Sabatini: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)

    Article  Google Scholar 

  7. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 253–260. ACM (2002)

    Google Scholar 

  8. Lockhart, J.W., Weiss, G.M.: Limitations with activity recognition methodology & data sets. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, UbiComp 2014 Adjunct, pp. 747–756. ACM, New York (2014)

    Google Scholar 

  9. Varkey, J.P., Pompili, D., Walls, T.A.: Human motion recognition using a wireless sensor-based wearable system. Pers. Ubiquit. Comput. 16(7), 897–910 (2012)

    Article  Google Scholar 

  10. 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 Trans. Inf. Technol. Biomed. 14(5), 1166–1172 (2010)

    Article  Google Scholar 

  11. Zhang, M., Sawchuk, A.A.: A feature selection-based framework for human activity recognition using wearable multimodal sensors. In; Proceedings of the 6th International Conference on Body Area Networks, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 92–98 (2011)

    Google Scholar 

  12. Lara, Ó.D., Pérez, A.J., Labrador, M.A., Posada, J.D.: Centinela: a human activity recognition system based on acceleration and vital sign data. Pervasive Mob. Comput. 8(5), 717–729 (2012)

    Article  Google Scholar 

  13. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216–223. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  14. Khan, A.M., Lee, Y.-K., Lee, S., Kim, T.-S.: Accelerometers position independent physical activity recognition system for long-term activity monitoring in the elderly. Med. Biol. Eng. Comput. 48(12), 1271–1279 (2010)

    Article  Google Scholar 

  15. Garcia-Ceja, E., Brena, R.F., Carrasco-Jimenez, J.C., Garrido, L.: Long-term activity recognition from wristwatch accelerometer data. Sensors 14(12), 22500–22524 (2014)

    Article  Google Scholar 

  16. Guan, D., Yuan, W., Lee, Y.-K., Gavrilov, A., Lee, S.: Activity recognition based on semi-supervised learning. In: 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, 2007, RTCSA 2007, pp. 469–475 (2007)

    Google Scholar 

  17. Stikic, M., Van Laerhoven, K., Schiele, B.: Exploring semi-supervised and active learning for activity recognition. In: 12th IEEE International Symposium on Wearable Computers, 2008, ISWC 2008, pp. 81–88. IEEE (2008)

    Google Scholar 

  18. Lane, N.D., Xu, Y., Lu, H., Hu, S., Choudhury, T., Campbell, A.T., Zhao, F.: Enabling large-scale human activity inference on smartphones using community similarity networks (Csn). In: Proceedings of the 13th International Conference on Ubiquitous Computing, UbiComp 2011, pp. 355–364. ACM, New York (2011)

    Google Scholar 

  19. Parviainen, J., Bojja, J., Collin, J., Leppänen, J., Eronen, A.: Adaptive activity and environment recognition for mobile phones. Sensors 14(11), 20753–20778 (2014)

    Article  Google Scholar 

  20. Lu, H., Frauendorfer, D., Rabbi, M., Mast, M.S., Chittaranjan, G.T., Campbell, A.T., Gatica-Perez, D., Choudhury, T.: StressSense: detecting stress in unconstrained acoustic environments using smartphones. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp 2012, pp. 351–360. ACM, New York (2012)

    Google Scholar 

  21. Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: a user-centered approach. In: AAAI, vol. 10, pp. 236–241 (2010)

    Google Scholar 

  22. Abdallah, Z.S., Gaber, M.M., Srinivasan, B., Krishnaswamy, S.: StreamAR: incremental and active learning with evolving sensory data for activity recognition. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI), vol. 1, pp. 1163–1170 (2012)

    Google Scholar 

  23. Vo, Q.V., Hoang, M.T., Choi, D.: Personalization in mobile activity recognition system using K-medoids clustering algorithm. Int. J. Distrib. Sens. Netw. 2013(315841), 12 (2013). doi:10.1155/2013/315841

  24. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  MATH  Google Scholar 

  25. Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013)

    Article  Google Scholar 

  26. Therneau, T.M., Atkinson, E.J.: An introduction to recursive partitioning using the rpart routines. Technical report 61 (1997)

    Google Scholar 

  27. Casale, P., Pujol, O., Radeva, P.: Personalization and user verification in wearable systems using biometric walking patterns. Pers. Ubiquit. Comput. 16(5), 563–580 (2012)

    Article  Google Scholar 

  28. Activity recognition from single chest-mounted accelerometer data set (2012). https://archive.ics.uci.edu/ml/datasets/Activity+Recognition+from+Single+Chest-Mounted+Accelerometer. Accessed 2015

  29. Bruno, B., Mastrogiovanni, F., Sgorbissa, A.: A public domain dataset for adl recognition using wrist-placed accelerometers. In: 2014 RO-MAN: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 738–743 (2014)

    Google Scholar 

  30. Dataset for adl recognition with wrist-worn accelerometer data set (2014). https://archive.ics.uci.edu/ml/datasets/Dataset+for+ADL+Recognition+with+Wrist-worn+Accelerometer. Accessed 2015

  31. Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12(2), 74–82 (2011)

    Article  Google Scholar 

  32. Activity prediction dataset (2012) . http://www.cis.fordham.edu/wisdm/dataset.php. Accessed 2015

  33. Human activity recognition using smartphones data set (2012). http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones. Accessed 2015

Download references

Acknowledgements

Enrique Garcia-Ceja would like to thank Consejo Nacional de Ciencia y Tecnología (CONACYT) and the AAAmI research group at Tecnológico de Monterrey for the financial support in his PhD. studies.

Author information

Authors and Affiliations

  1. Tecnológico de Monterrey, Campus Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, Nuevo León, Mexico

    Enrique Garcia-Ceja & Ramon Brena

Authors
  1. Enrique Garcia-Ceja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramon Brena
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Enrique Garcia-Ceja .

Editor information

Editors and Affiliations

  1. Universidad de Alicante, Alicante, Spain

    Juan M. García-Chamizo

  2. University of Calabria, Rende, Italy

    Giancarlo Fortino

  3. Universidad de Chile, Santiago, Chile

    Sergio F. Ochoa

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Garcia-Ceja, E., Brena, R. (2015). Building Personalized Activity Recognition Models with Scarce Labeled Data Based on Class Similarities. In: García-Chamizo, J., Fortino, G., Ochoa, S. (eds) Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information. UCAmI 2015. Lecture Notes in Computer Science(), vol 9454. Springer, Cham. https://doi.org/10.1007/978-3-319-26401-1_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26401-1_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26400-4

  • Online ISBN: 978-3-319-26401-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation