Skip to main content
SpringerLink
Log in

A revised framework of machine learning application for optimal activity recognition

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Data science augments manual data understanding with machine learning for potential performance increase. In this paper, data science methodology is examined to enhance machine learning application in smartphone based automatic human activity recognition (HAR). Eventually, a modified feature engineering and a novel post-learning data engineering are proposed in the machine learning framework as the alternate of data understanding for an effective HAR. The proposed framework is examined on two different HAR data sets demonstrating a possibility of data-driven machine learning for near an optimal classification of activities. The proposed framework exhibited effectiveness and efficiency when compared with the existing methods. The modified feature engineering resulted in 42% fewer features required by support vector machine to yield 97.3% correct recognition of human physical activities. However, the addition of post-learning data engineering further improved the model to perform 99% accurate classification, which is an almost optimal performance.

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

Similar content being viewed by others

References

  1. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, Bruges, Belgium (2013)

  2. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Hervs, R., Rodrguez, M. (eds.) Ambient Assisted Living and Home Care, 4th IWAAL, Lecture Notes in Computer Science, vol. 7657, pp. 216–223. Springer, Berlin (2012)

    Google Scholar 

  3. HUYNH, D.T.G.: Human activity recognition with wearable sensors. Ph.D. dissertation, Technische Univ., Darmstadt (2008)

  4. Chan, M., Estève, D., Fourniols, J.-Y., Escriba, C., Campo, E.: Smart wearable systems: current status and future challenges. Artif. Intell. Med. 56(3), 137–156 (2012)

    Article  Google Scholar 

  5. Ryder, J., Longstaff, B., Reddy, S., Estrin, D.: Ambulation: a tool for monitoring mobility patterns over time using mobile phones. In: International Conference on Computer Science Engineering, Vancouver, pp. 927–931 (2009)

  6. Consolvo, S., McDonald, D.W., Toscos, T., Chen, M.Y., Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., Smith, I., Landay, J.A.: Activity sensing in the wild: A field trial of ubifit garden. In: Proceedings of SIGCHI Conference on Human Factors in Computing System, Florence, Italy, pp. 1797–1806 (2008)

  7. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.: Training computationally efficient smartphonebased human activity recognition models. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A., Appollini, B., Kasabov, N. (eds.) Artificial Neural Networks and Temporal Difference Learning, ICANN 2013, Lecture Notes Computer Science, vol. 8131, pp. 426–433. Springer, Berlin (2013)

    Chapter  Google Scholar 

  8. Kästner, M., Strickert, M., Villmann, T.: A sparse kernelized matrix learning vector quantization model for human activity recognition. In: 21st European Symposium on Artificial Neural Networks, ESANN 2013, Bruges, Belgium, April 24–26 (2013)

  9. Preece, S., Goulermas, J., Kenney, L., Howard, D.: A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans. Biomed. Eng. 56(3), 871–879 (2009)

    Article  Google Scholar 

  10. Lovell, N., Wang, N., Ambikairajah, E., Celler, B.: Accelerometry based classification of walking patterns using time-frequency analysis. In: Proceedings of the 29th IEEE International Conference of the Engineering in Medicine and Biology Society, pp. 4899–4902 (2007)

  11. Nyan, M., Tay, F., Seah, K., Sitoh, Y.: Classification of gait patterns in the time-frequency domain. J. Biomech. 39(14), 2647–2656 (2006)

    Article  Google Scholar 

  12. 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 

  13. Barshan, B., Yuksek, M.C.: Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. Comput. J. 57(11), 1649–1667 (2014)

    Article  Google Scholar 

  14. Cai, D.: Spectral regression: a regression framework for efficient regularized subspace learning. Ph.D. dissertation, Department of Computer Science, University of Illinois at Urbana–Champaign (2009)

  15. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1) (2009)

    Article  Google Scholar 

  16. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

    MATH  Google Scholar 

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

    Google Scholar 

  18. Rish, I.: An empirical study of the naive bayes classifier. In: IJCAI workshop “Empirical Methods in AI”, vol. 3, no. 22, pp. 41–46. IBM, New York (2001)

  19. Maurer, U., Smailagic, A., Siewiorek, D., Deisher, M.: Activity recognition and monitoring using multiple sensors on different body positions. In: IEEE IEEE International Workshop on Wearable and Implantable Body Sensor Network, pp. 113–116 (2006)

  20. Allen, F., Ambikairajah, E., Lovell, N., Celler, B.: An adapted gaussian mixture model approach to accelerometry-based movement classification using time-domain features. In: Proceedings of 28th International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3600–3603 (2006)

  21. Khan, A., Lee, Y.-K., Lee, S., 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 

  22. Arif, M., Bilal, M., Kattan, A., Ahmed, S.: Better physical activity classification using smartphone acceleration sensor. J. Med. Syst. 38(95) (2014)

  23. Capela, N.A., Lemaire, E.D., Baddour, N., Rudolf, M., Goljar, N., Burger, H.: Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants. J. Neuroeng. Rehabil. 13(1) (2016)

  24. Reyes-Ortiz, J.-L., Oneto, L., Ghio, A., Sama, A., Anguita, D., Parra, X.: Human activity recognition on smartphones with awareness of basic activities and postural transitions. In: Artificial Neural Networks and Machine Learning—ICANN 2014, Lecture Notes Computer Science, vol. 8681, pp. 177–184. Springer, Berlin (2014)

    Chapter  Google Scholar 

  25. Wu, Z., Zhang, A., Zhang, C.: Human activity recognition using wearable devices sensor data (2015). http://cs229.stanford.edu/proj2015/107_report.pdf

  26. Su, X., Tong, H., Ji, P.: Accelerometer-based Activity Recognition on Smartphone. In: Proceedings of 23rd ACM International Conference on Information & Knowledge Management, NY, USA, pp. 2021–2023 (2014)

  27. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16(1), 321–35 (2002)

    Article  Google Scholar 

  28. Zhang, S., McCullagh, P., Zhang, J., Yu, T.: A smartphone based real-time daily activity monitoring system. Cluster Comput. 17(1), 711–721 (2014)

    Article  Google Scholar 

  29. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)

    Article  Google Scholar 

  30. Hoque, N., Bhattacharyya, D.K., Kalita, J.K.: MIFS-ND: a mutual information-based feature selection method. Expert. Syst. Appl. 41(14), 6371–6385 (2014)

    Article  Google Scholar 

  31. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.: Feature selection for SVMs. In: Advances in Neural Information Processing Systems, vol. 13, pp. 668–674. MIT Press, Cambridge (2000)

Download references

Author information

Authors and Affiliations

  1. College of Computer and Information Systems, Umm Al Qrua University, Makkah, Saudi Arabia

    Mohsin Bilal & Muhammad Arif

  2. Department of Telecommunication Engineering, Mehran University of Engineering and Technology, Jamshoro, 76062, Pakistan

    Faisal K. Shaikh

  3. School of Engineering and Computing, National University, San Diego, USA

    Mudasser F. Wyne

Authors
  1. Mohsin Bilal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Faisal K. Shaikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Arif
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mudasser F. Wyne
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohsin Bilal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bilal, M., Shaikh, F.K., Arif, M. et al. A revised framework of machine learning application for optimal activity recognition. Cluster Comput 22 (Suppl 3), 7257–7273 (2019). https://doi.org/10.1007/s10586-017-1212-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1212-x

Keywords

Search

Navigation