Skip to main content
SpringerLink
Log in

Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data

  • Published:
Statistics in Biosciences Aims and scope Submit manuscript

Abstract

Wearable accelerometers provide an objective measure of human physical activity. They record high-frequency unlabeled three-dimensional time series data. We extract meaningful features from the raw accelerometry data and based on them develop and evaluate a classification method for the detection of walking and its subclasses, i.e., level walking, descending stairs, and ascending stairs. Our methodology is tested on a sample of 32 middle-aged subjects for whom we extracted features based on the Fourier and wavelet transforms. We build subject-specific and group-level classification models utilizing a tree-based methodology. We evaluate the effects of sensor location and tuning parameters on the classification accuracy of the tree models. In the group-level classification setting, we propose a robust feature inter-subject normalization and evaluate its performance compared to unnormalized data. The overall classification accuracy for the three activities at the subject-specific level was on average 87.6%, with the ankle-worn accelerometers showing the best performance with an average accuracy 90.5%. At the group-level, the average overall classification accuracy for the three activities using the normalized features was 80.2% compared to 72.3% for the unnormalized features. In summary, a framework is provided for better use and feature extraction from raw accelerometry data to differentiate among different walking modalities as well as considerations for study design.

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
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Bai J, Goldsmith J, Caffo B, Glass TA, Crainiceanu CM (2012) Movelets: a dictionary of movement. Electron J Stat 6:559

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  3. Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: International Conference on Pervasive Computing, Springer, pp 1–17

  4. Campbell KL, Crocker P, McKenzie DC (2002) Field evaluation of energy expenditure in women using tritrac accelerometers. Med Sci Sports Exerc 34(10):1667–1674

    Article  Google Scholar 

  5. Ellis K, Kerr J, Godbole S, Staudenmayer J, Lanckriet G (2016) Hip and wrist accelerometer algorithms for free-living behavior classification. Med Sci Sports Exerc 48(5):933–940

    Article  Google Scholar 

  6. Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG (2011) Validation of the genea accelerometer. Med Sci Sports Exerc 43(6):1085–1093

    Article  Google Scholar 

  7. Harris FJ (1978) On the use of windows for harmonic analysis with the discrete fourier transform. Proc IEEE 66(1):51–83

    Article  Google Scholar 

  8. He B, Bai J, Zipunnikov VV, Koster A, Caserotti P, Lange-Maia B, Glynn NW, Harris TB, Crainiceanu CM (2014) Predicting human movement with multiple accelerometers using movelets. Med Sci Sports Exerc 46(9):1859–1866

    Article  Google Scholar 

  9. Kwapisz JR, Weiss GM, Moore SA (2011) Activity recognition using cell phone accelerometers. ACM SigKDD Explor Newsl 12(2):74–82

    Article  Google Scholar 

  10. Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W (2013) Activity recognition using a single accelerometer placed at the wrist or ankle. Med Sci Sports Exerc 45(11):2193

    Article  Google Scholar 

  11. Ohtaki Y, Susumago M, Suzuki A, Sagawa K, Nagatomi R, Inooka H (2005) Automatic classification of ambulatory movements and evaluation of energy consumptions utilizing accelerometers and a barometer. Microsyst Technol 11(8–10):1034–1040

    Article  Google Scholar 

  12. Pober DM, Staudenmayer J, Raphael C, Freedson PS et al (2006) Development of novel techniques to classify physical activity mode using accelerometers. Med Sci Sports Exerc 38(9):1626

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. RStudio Team (2015) RStudio: integrated development environment for R. RStudio, Inc., Boston, MA. http://www.rstudio.com/

  15. Sejdić E, Djurović I, Jiang J (2009) Time-frequency feature representation using energy concentration: an overview of recent advances. Digit Signal Proces 19(1):153–183

    Article  Google Scholar 

  16. Straczkiewicz M, Urbanek J, Fadel W, Crainiceanu C, Harezlak J (2016) Automatic car driving detection using raw accelerometry data. Physiol Meas 37(10):1757

    Article  Google Scholar 

  17. Therneau T, Atkinson B (2015) An introduction to recursive partitioning using the rpart routines. Mayo Foundation, Rochester, MN

    Google Scholar 

  18. Therneau T, Atkinson B, Ripley B (2015) rpart: recursive partitioning and regression trees. http://CRAN.R-project.org/package=rpart, r package version 4.1-10

  19. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M et al (2008) Physical activity in the united states measured by accelerometer. Med Sci Sports Exerc 40(1):181

    Article  Google Scholar 

  20. Urbanek JK, Zipunnikov V, Harris T, Fadel W, Glynn N, Koster A, Caserotti P, Crainiceanu C, Harezlak J (2018) Prediction of sustained harmonic walking in the free-living environment using raw accelerometry data. Physiol Meas 39(2):02NT02

    Article  Google Scholar 

  21. Veltink PH, Bussmann HJ, De Vries W, Martens WJ, Van Lummel RC (1996) Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans Rehabil Eng 4(4):375–385

    Article  Google Scholar 

  22. Xiao L, He B, Koster A, Caserotti P, Lange-Maia B, Glynn NW, Harris TB, Crainiceanu CM (2016) Movement prediction using accelerometers in a human population. Biometrics 72(2):513–524

    Article  MathSciNet  MATH  Google Scholar 

  23. Zhang S, Rowlands AV, Murray P, Hurst TL, et al (2012) Physical activity classification using the genea wrist-worn accelerometer. PhD thesis, Lippincott Williams and Wilkins

Download references

Acknowledgements

This paper was made possible, in part, with support from the Indiana Clinical and Translational Sciences Institute Design and Biostatistics Pilot Grant funded, in part by Grant UL1TR001108, from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award. Jaroslaw Harezlak has received funding from the National Institute of Mental Health research Grant R01MH108467.

Author information

Authors and Affiliations

  1. Department of Biostatistics, Richard M. Fairbanks School of Public Health & School of Medicine, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, 46202, USA

    William F. Fadel & Xiaochun Li

  2. Division of Geriatric Medicine and Gerontology, Department of Medicine, School of Medicine, Johns Hopkins University, 2024 E. Monument Street, Suite 2-700, Baltimore, MD, 21205, USA

    Jacek K. Urbanek

  3. Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, 723 W. Michigan St., SL280, Indianapolis, IN, 46202, USA

    Steven R. Albertson

  4. Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th Street, Bloomington, IN, 47405, USA

    Andrea K. Chomistek

  5. Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th Street, Suite C107, Bloomington, IN, 47405, USA

    Jaroslaw Harezlak

Authors
  1. William F. Fadel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacek K. Urbanek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Steven R. Albertson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaochun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrea K. Chomistek
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jaroslaw Harezlak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William F. Fadel.

Supplemental Results Tables

Supplemental Results Tables

See Appendix Tables 3, 4, 5, 6.

Table 3 LS means of sensitivity for sensor location by activity for the subject-level classifiers
Full size table
Table 4 LS means of sensitivity for window length by activity for the subject-level classifiers
Full size table
Table 5 LS means of overall classification accuracy for sensor location for the subject-level classifiers
Full size table
Table 6 LS means of overall classification accuracy for window length for the subject-level classifiers
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fadel, W.F., Urbanek, J.K., Albertson, S.R. et al. Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data. Stat Biosci 11, 334–354 (2019). https://doi.org/10.1007/s12561-019-09241-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12561-019-09241-7

Keywords

Search

Navigation