Fadel, William F.Urbanek, Jacek K.Albertson, Steven R.Li, XiaochunChomistek, Andrea K.Harezlak, Jaroslaw2021-04-262021-04-262019-05-10Fadel, W. F., Urbanek, J. K., Albertson, S. R., Li, X., Chomistek, A. K., & Harezlak, J. (2019). Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data. Statistics in Biosciences, 11(2), 334–354. https://doi.org/10.1007/s12561-019-09241-71867-1772https://hdl.handle.net/1805/25758Wearable 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 sub-classes, 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.en-USClassification TreesSignal processingAccelerometerPhysical activityWalkingArticle