Skip to main content
SpringerLink
Log in

Human Activity Recognition on Mobile Devices Using Artificial Hydrocarbon Networks

  • Conference paper
  • First Online:
Advances in Soft Computing (MICAI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10632))

Included in the following conference series:

  • 399 Accesses

Abstract

Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. However, versatility of users, location of smartphones, battery, processing and storage limitations, among other issues have been identified. In that sense, this paper presents a human activity recognition system based on artificial hydrocarbon networks. This technique have been proved to be very effective on HAR systems using wearable sensors, so the present work proposes to use this learning method with the information provided by the in-sensors of mobile devices. Preliminary results proved that artificial hydrocarbon networks might be used as an alternative for human activity recognition on mobile devices. In addition, a real dataset created for this work has been published.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
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.

    HAR_Mobile dataset link: https://github.com/hiramponce/HAR_Mobile.

References

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

    Google Scholar 

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

    Article  Google Scholar 

  3. Chen, C., Jafari, R., Kehtarnavaz, N.: A survey of depth and inertial sensor fusion for human action recognition. Multimed. Tools Appl. 76(3), 4405–4425 (2017)

    Article  Google Scholar 

  4. Dohnálek, P., Gajdoš, P., Moravec, P., Peterek, T., SnáŠel, V.: Application and comparison of modified classifiers for human activity recognition. Przegląd Elektrotechniczny 89(11), 55–58 (2013)

    Google Scholar 

  5. Incel, O., Kose, M., Ersoy, C.: A review and taxonomy of activity recognition on mobile phones. BioNanoScience 3(2), 145–171 (2013)

    Article  Google Scholar 

  6. Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. In: Proceedings on the Fourth International Workshop on Knowledge Discovery from Sensor Data (2010)

    Google Scholar 

  7. Kwolek, B., Kepski, M.: Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput. Methods Programs Biomed. 117(3), 489–501 (2014)

    Article  Google Scholar 

  8. Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192–1209 (2013)

    Article  Google Scholar 

  9. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: Computer Vision and Pattern Recognition Workshops, pp. 9–14 (2010)

    Google Scholar 

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

    Google Scholar 

  11. Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Berkeley MHAD: a comprehensive multimodal human action database. In: IE Workshop on Applications of Computer Vision, pp. 53–60 (2013)

    Google Scholar 

  12. Ponce, H., Martínez-Villasenor, M.L., Miralles-Pechuán, L.: A novel wearable sensor-based human activity recognition approach using artificial hydrocarbon networks. Sensors 16(7), 1033 (2016)

    Article  Google Scholar 

  13. Ponce, H., Miralles-Pechuán, L., Martínez-Villasenor, M.L.: A flexible approach for human activity recognition using artificial hydrocarbon networks. Sensors 16(11), 1715 (2016)

    Article  Google Scholar 

  14. Ponce, H., Ponce, P.: Artificial organic networks. In: 2011 IEEE Conference on Electronics, Robotics and Automotive Mechanics, pp. 29–34. IEEE (2011)

    Google Scholar 

  15. Ponce, H., Ponce, P., Molina, A.: Adaptive noise filtering based on artificial hydrocarbon networks: an application to audio signals. Expert Syst. Appl. 41(14), 6512–6523 (2014)

    Article  Google Scholar 

  16. Ponce-Espinosa, H., Ponce-Cruz, P., Molina, A.: Artificial organic networks. Artificial Organic Networks. SCI, vol. 521, pp. 53–72. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02472-1_3

    Chapter  Google Scholar 

  17. Ponce, H., Ponce, P., Molina, A.: The development of an artificial organic networks toolkit for LabVIEW. J. Comput. Chem. 36(7), 478–492 (2015)

    Article  Google Scholar 

  18. Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors-a review of classification techniques. Physiol. Meas. 30(4), 1–33 (2009)

    Article  Google Scholar 

  19. Reiss, A.: Personalized mobile physical activity monitoring for everyday life. Ph.D. thesis, Technical University of Kaiserslautern (2014)

    Google Scholar 

  20. Roggen, D., et al.: OPPORTUNITY: towards opportunistic activity and context recognition systems. In: 3rd IEEE Workshop on Autononomic and Opportunistic Communications (2009)

    Google Scholar 

  21. Roggen, D., et al.: Collecting complex activity datasets in highly rich networked sensor environments. In: Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany, pp. 233–240. IEEE (2010)

    Google Scholar 

  22. Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.M.: A survey of online activity recognition using mobile phones. Sensors 15(1), 2059–2085 (2015)

    Article  Google Scholar 

  23. Su, X., Tong, H., Ji, P.: Activity recognition with smartphone sensors. Tsinghua Sci. Technol. 19(3), 235–249 (2014)

    Article  Google Scholar 

  24. Yang, A.Y., Jafari, R., Sastry, S.S., Bajcsy, R.: Distributed recognition of human actions using wearable motion sensor networks. J. Ambient. Intell. Smart Environ. 1(2), 103–115 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, 03920, Mexico City, Mexico

    Hiram Ponce, Guillermo González & Ma Lourdes Martínez-Villaseñor

  2. Centre for Applied Data Analytics Research (CeADAR), University College Dublin, Belfield, Dublin 4, Ireland

    Luis Miralles-Pechuán

Authors
  1. Hiram Ponce
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guillermo González
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luis Miralles-Pechuán
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ma Lourdes Martínez-Villaseñor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiram Ponce .

Editor information

Editors and Affiliations

  1. Universidad Autónoma del Estado de Hidalgo, Pachuca, Mexico

    Félix Castro

  2. INFOTEC Aguascalientes, Aguascalientes, Mexico

    Sabino Miranda-Jiménez

  3. Tecnológico de Monterrey, Atizapán de Zaragoza, Mexico

    Miguel González-Mendoza

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ponce, H., González, G., Miralles-Pechuán, L., Martínez-Villaseñor, M.L. (2018). Human Activity Recognition on Mobile Devices Using Artificial Hydrocarbon Networks. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Soft Computing. MICAI 2017. Lecture Notes in Computer Science(), vol 10632. Springer, Cham. https://doi.org/10.1007/978-3-030-02837-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02837-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02836-7

  • Online ISBN: 978-3-030-02837-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation