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
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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
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
Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, 723 W. Michigan St., SL280, Indianapolis, IN, 46202, USA
Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th Street, Bloomington, IN, 47405, USA
Statistics in Biosciences