Research Study Abstract

Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data

  • Published on May 10, 2019

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.


  • William F. Fadel 1
  • Jacek K. Urbanek 2
  • Steven R. Albertson 3
  • Xiaochun Li 1
  • Andrea K. Chomistek 4
  • Jaroslaw Harezlak 4


  • 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

  • 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

  • 3

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

  • 4

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


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