Skip to main content
SpringerLink
Log in

A New Approach to Human Activity Recognition Using Machine Learning Techniques

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

Recognition of human activities aims a wide diversity of applications. However, identifying complicated activities continues a challenging and active research area. In this work, we assess a new approach of feature selection for human activity recognition. For the task, we also compare state-of-the-art classifiers, e.g., Bayes classifier, kNN, MLP, SVM, MLM and MLM-NN. Based on the experiments, the MLM-NN is able to speed up the original MLM while holding equivalent accuracy. MLM and SVM achieved accuracy of more than 99.2% in the original data set and 98.1% using new feature selection method. Results show that the proposed feature selection approach is a promising alternative to activity recognition on smartphones.

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

Access this chapter

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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

Institutional subscriptions

Notes

  1. 1.

    The Harmonic Means (HM) between sensitivity and specificity is computed by \(HM = 2 \cdot \frac{Se \cdot Sp}{Se + Sp}\).

References

  1. Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)

    Google Scholar 

  2. 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). doi:10.1007/978-3-642-35395-6_30

    Chapter  Google Scholar 

  3. Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., Amirat, Y.: Physical human activity recognition using wearable sensors. Sensors 15(12), 29858 (2015)

    Article  Google Scholar 

  4. de Azevedo, F.M., Brasil, L.M., de Oliveira, R.C.L.: Redes Neurais com aplições em Controle e em Sistemas Especialistas. Visual Books (2000)

    Google Scholar 

  5. Badawi, H., Saddik, A.E.: Towards a context-aware biofeedback activity recommendation mobile application for healthy lifestyle. Procedia Comput. Sci. 21, 382–389 (2013)

    Article  Google Scholar 

  6. Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer data from smartphones. Procedia Comput. Sci. 34, 450–457 (2014)

    Article  Google Scholar 

  7. Chen, K.H., Chen, P.C., Liu, K.C., Chan, C.T.: Wearable sensor-based rehabilitation exercise assessment for knee osteoarthritis. Sensors 15(2), 4193 (2015)

    Article  Google Scholar 

  8. Crammer, K., Singer, Y., Cristianini, N., Shawe-taylor, J., Williamson, B.: On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res. 2, 265–292 (2001)

    Google Scholar 

  9. Czabke, A., Marsch, S., Lueth, T.C.: Accelerometer based real-time activity analysis on a microcontroller. In: 5th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2011, Dublin, Ireland, 23–26, May 2011 (2011)

    Google Scholar 

  10. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Int. Res. 2(1), 263–286 (1995)

    MATH  Google Scholar 

  11. Duan, K.-B., Keerthi, S.S.: Which is the best multiclass SVM method? An empirical study. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 278–285. Springer, Heidelberg (2005). doi:10.1007/11494683_28

    Chapter  Google Scholar 

  12. Dubois, A., Charpillet, F.: Human activities recognition with RGB-depth camera using HMM. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4666–4669, July 2013

    Google Scholar 

  13. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Hoboken (2000)

    MATH  Google Scholar 

  14. Haykin, S.O.: Neural Networks and Learning Machines. Pearson Prentice Hall, Upper Saddle River (2008)

    Google Scholar 

  15. Ho, E.S., Chan, J.C., Chan, D.C., Shum, H.P., Cheung, Y.M., Yuen, P.C.: Improving posture classification accuracy for depth sensor-based human activity monitoring in smart environments. Comput. Vis. Image Underst. 148, 97–110 (2016)

    Article  Google Scholar 

  16. Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417 (1933)

    Article  MATH  Google Scholar 

  17. Jalal, A., Kamal, S., Kim, D.: A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments. Sensors 14(7), 11735 (2014)

    Article  Google Scholar 

  18. Lee, M.W., Khan, A.M., Kim, T.S.: A single tri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation. Pers. Ubiquit. Comput. 15(8), 887–898 (2011)

    Article  Google Scholar 

  19. Li, Q., Stankovic, J.A., Hanson, M.A., Barth, A.T., Lach, J., Zhou, G.: Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In: Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, pp. 138–143. IEEE Computer Society, Washington, DC (2009)

    Google Scholar 

  20. Mesquita, D.P.P., Gomes, J.P.P., Junior, A.H.S.: Ensemble of minimal learning machines for pattern classification. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2015. LNCS, vol. 9095, pp. 142–152. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19222-2_12

    Chapter  Google Scholar 

  21. Mohammadi, E., Khoshab, H., Kazemnejad, A.: Activities of daily living for patients with chronic heart failure: a partnership care model evaluation. Appl. Nurs. Res. 30, 261–267 (2016)

    Article  Google Scholar 

  22. Nunes, T.M., Coelho, A.L., Lima, C.A., Papa, J.P., de Albuquerque, V.H.C.: EEG signal classification for epilepsy diagnosis via optimum path forest a systematic assessment. Neurocomputing 136, 103–123 (2014)

    Article  Google Scholar 

  23. Olivares, A., Ramrez, J., Grriz, J.M., Olivares, G., Damas, M.: Detection of (in)activity periods in human body motion using inertial sensors: a comparative study. Sensors 12(5), 5791 (2012)

    Article  Google Scholar 

  24. Osmani, V., Balasubramaniam, S., Botvich, D.: Human activity recognition in pervasive health-care: supporting efficient remote collaboration. J. Netw. Comput. Appl. 31(4), 628–655 (2008)

    Article  Google Scholar 

  25. Papa, J.P., Falco, A.X., de Albuquerque, V.H.C., Tavares, J.M.R.: Efficient supervised optimum-path forest classification for large datasets. Pattern Recogn. 45(1), 512–520 (2012)

    Article  Google Scholar 

  26. Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin dags for multiclass classification. In: Advances in Neural Information Processing Systems, pp. 547–553. MIT Press (2000)

    Google Scholar 

  27. del Rosario, M.B., Redmond, S.J., Lovell, N.H.: Tracking the evolution of smartphone sensing for monitoring human movement. Sensors 15(8), 18901 (2015)

    Article  Google Scholar 

  28. Ruck, D.W., Rogers, S.K., Kabrisky, M., Oxley, M.E., Suter, B.W.: The multilayer perceptron as an approximation to a bayes optimal discriminant function. Trans. Neur. Netw. 1, 296–298 (1990)

    Article  Google Scholar 

  29. Shimonski, R., Zenir, J., Bishop, A.: Chapter 4 - mobile phone tracking. In: Bishop, R.S.Z. (ed.) Cyber Reconnaissance, Surveillance and Defense, pp. 113–143. Syngress, Boston (2015)

    Chapter  Google Scholar 

  30. Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.M.: Complex human activity recognition using smartphone and wrist-worn motion sensors. Sensors 16(4), 426 (2016)

    Article  Google Scholar 

  31. de Souza Júnior, A.H., Corona, F., Barreto, G.A., Miche, Y., Lendasse, A.: Minimal learning machine: a novel supervised distance-based approach for regression and classification. Neurocomputing 164, 34–44 (2015)

    Article  Google Scholar 

  32. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, Cambridge (2008)

    MATH  Google Scholar 

  33. Vapnik, V.N.: Statistical Learning Theory. Wiley, Hoboken (1998)

    MATH  Google Scholar 

  34. Wang, Y., Jiang, X., Cao, R., Wang, X.: Robust indoor human activity recognition using wireless signals. Sensors 15(7), 17195 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

The first author acknowledge the sponsorship from the Cearense Foundation for the Support of Scientific and Technological Development (FUNCAP) and the National Council for Research and Development (CNPq) by providing financial support.

Author information

Authors and Affiliations

  1. Programa de Pós-Graduação em Ciência da Computação, Instituto Federal do Ceará, Maracanaú, Ceará, Brazil

    Leandro B. Marinho, A. H. de Souza Junior & P. P. Rebouças Filho

Authors
  1. Leandro B. Marinho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. H. de Souza Junior
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. P. Rebouças Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. P. Rebouças Filho .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Marinho, L.B., de Souza Junior, A.H., Rebouças Filho, P.P. (2017). A New Approach to Human Activity Recognition Using Machine Learning Techniques. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-53480-0_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation