Skip to main content
SpringerLink
Log in

Subject Recognition Using Wrist-Worn Triaxial Accelerometer Data

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Big Data (MOD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10710))

Included in the following conference series:

Abstract

This study demonstrates how a subject can be identified by the means of accelerometer data generated through wrist-worn devices in the context of clinical trials where data integrity is of utmost importance. A custom vector of features extracted from the daily accelerometer time series is defined. Feature selection is adapted to take account of the sequential structure in features. Several classifiers are compared within three different learning frameworks: binary, multi-class and one-class. A simple algorithm like logistic regression shows excellent performance in the binary and multi-class frameworks.

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 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

Institutional subscriptions

Notes

  1. 1.

    For further information see: http://www.actigraphcorp.com/.

  2. 2.

    All classifiers use Scikit-Learn [11].

References

  1. Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W., Pollak, C.P.: The role of actigraphy in the study of sleep and circadian rhythms. Sleep 26(3), 342–392 (2003)

    Article  Google Scholar 

  2. Cao, V.L., Nicolau, M., McDermott, J.: One-class classification for anomaly detection with kernel density estimation and genetic programming. In: Heywood, M.I., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds.) EuroGP 2016. LNCS, vol. 9594, pp. 3–18. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30668-1_1

    Chapter  Google Scholar 

  3. Fernandez-Lopez, P., Liu-Jimenez, J., Sanchez-Redondo, C., Sanchez-Reillo, R.: Gait recognition using smartphone. In: 2016 IEEE International Carnahan Conference on Security Technology (ICCST), pp. 1–7. IEEE (2016)

    Google Scholar 

  4. Fulcher, B.D., Jones, N.S.: Highly comparative feature-based time-series classification. IEEE Trans. Knowl. Data Eng. 26(12), 3026–3037 (2014)

    Article  Google Scholar 

  5. George, S.L.: Research misconduct and data fraud in clinical trials: prevalence and causal factors. Int. J. Clin. Oncol. 21(1), 15–21 (2016)

    Article  Google Scholar 

  6. Iglewicz, B., Hoaglin, D.C.: How to Detect and Handle Outliers, vol. 16. ASQ Press, Milwaukee (1993)

    Google Scholar 

  7. Kelly, L.A., McMillan, D.G., Anderson, A., Fippinger, M., Fillerup, G., Rider, J.: Validity of actigraphs uniaxial and triaxial accelerometers for assessment of physical activity in adults in laboratory conditions. BMC Med. Phys. 13(1), 5 (2013)

    Article  Google Scholar 

  8. Khan, S.S., Madden, M.G.: A survey of recent trends in one class classification. In: Coyle, L., Freyne, J. (eds.) AICS 2009. LNCS (LNAI), vol. 6206, pp. 188–197. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17080-5_21

    Chapter  Google Scholar 

  9. Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 413–422. IEEE (2008)

    Google Scholar 

  10. Loughran, R., Agapitos, A., Kattan, A., Brabazon, A., O’Neill, M.: Speaker verification on unbalanced data with genetic programming. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9597, pp. 737–753. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31204-0_47

    Chapter  Google Scholar 

  11. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  12. Rajna, P., Szomszed, A.: Actigraphy: a valuable diagnostic tool or a luxury investigation? (Neuropsychiatric aspects). Ideggyogy. Sz. 62(9–10), 308–316 (2009)

    Google Scholar 

  13. Teskey, W.J., Elhabiby, M., El-Sheimy, N.: Inertial sensing to determine movement disorder motion present before and after treatment. Sensors 12(3), 3512–3527 (2012)

    Article  Google Scholar 

  14. Trost, S.G., McIver, K.L., Pate, R.R.: Conducting accelerometer-based activity assessments in field-based research. Med. Sci. Sports Exerc. 37(11), S531 (2005)

    Article  Google Scholar 

  15. Wang, L., Ning, H., Tan, T., Hu, W.: Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans. Circuits Syst. Video Technol. 14(2), 149–158 (2004)

    Article  Google Scholar 

  16. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)

    Article  Google Scholar 

Download references

Acknowledgements

This research is funded by ICON plc.

Author information

Authors and Affiliations

  1. Natural Computing Research and Applications Group, School of Business, University College Dublin, Dublin, Ireland

    Stefano Mauceri, Louis Smith, James Sweeney & James McDermott

  2. ICON Plc, Dublin, Ireland

    Stefano Mauceri, Louis Smith, James Sweeney & James McDermott

Authors
  1. Stefano Mauceri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Louis Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. James Sweeney
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James McDermott
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefano Mauceri .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mauceri, S., Smith, L., Sweeney, J., McDermott, J. (2018). Subject Recognition Using Wrist-Worn Triaxial Accelerometer Data. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-72926-8_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation