Browsing by Author "Chomistek, Andrea K."

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    Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data
    (Springer, 2019-05-10) Fadel, William F.; Urbanek, Jacek K.; Albertson, Steven R.; Li, Xiaochun; Chomistek, Andrea K.; Harezlak, Jaroslaw; Biostatistics, School of Public Health
    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 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.
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    Statistical methods for extracting information from the raw accelerometry data and their applications in public health research
    (2017-01-19) Fadel, William Farris; Harezlak, Jaroslaw; Yiannoutsos, Constantin T.; Li, Xiaochun; Chomistek, Andrea K.
    Various methods exist to measure physical activity (PA). Subjective methods, such as diaries and surveys are relatively inexpensive ways of measuring one’s PA; how ever, they are riddled with measurement error and bias due to self-report. Wearable accelerometers offer a noninvasive and objective measure of subjects’ PA and are now widely used in observational and clinical studies. Accelerometers record high frequency data and produce an unlabeled time series at the sub-second level. An important activity to identify from such data is walking, since it is often the only form of exercise for certain populations. While much work has been done to advance the use of accelerometers in public health research, methodology is needed for quan tifying the physical characteristics of different types of PA from the raw signal. In my dissertation, I advance the accelerometry research methodology in a three-paper sequence. The first paper is a novel application of functional linear models to model the physical characteristics of walking. We emphasize the signal processing used to prepare the data for analyses, and we apply the methods to a motivating dataset collected in an elder population. The second paper addresses the classification of PA. We designed an experiment and collected the data with the purpose of extracting useful and interpretable features for differentiating among walking, descending stairs, and ascending stairs. We build subject-specific classification models utilizing a tree based classifier. We evaluate the effects of sensor location and tuning parameters on the classification rate of these models. The third paper addresses the classification of walking types at the population level. We propose a robust normalization of features extracted for each subject and compare the model classification results to evaluate the effect of feature normalization. In summary, this work provides a framework for better use of accelerometers in the study of physical activity.