Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data

William F. Fadel (), Jacek K. Urbanek, Steven R. Albertson, Xiaochun Li, Andrea K. Chomistek and Jaroslaw Harezlak
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William F. Fadel: Indiana University
Jacek K. Urbanek: Johns Hopkins University
Steven R. Albertson: Indiana University-Purdue University Indianapolis
Xiaochun Li: Indiana University
Andrea K. Chomistek: Indiana University Bloomington
Jaroslaw Harezlak: Indiana University Bloomington

Statistics in Biosciences, 2019, vol. 11, issue 2, No 7, 334-354

Abstract: 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.

Keywords: Classification trees; Signal processing; Accelerometer; Physical activity; Walking (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s12561-019-09241-7

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