Indiana University-Purdue University Indianapolis (IUPUI) / 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. / 2 years
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/13393 |
| Date | 19 January 2017 |
| Creators | Fadel, William Farris |
| Contributors | Harezlak, Jaroslaw, Yiannoutsos, Constantin T., Li, Xiaochun, Chomistek, Andrea K. |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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