Date of Award:

2017

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Advisor/Chair:

Xiaojun Qi

Abstract

In this study, we designed a system that recognizes a person’s physical activity by analyzing data read from a device that he or she wears. In order to reduce the system’s demands on the device’s computational capacity and memory space, we designed a series of strategies such as making accurate analysis based on only a small amount of data in the memory, extracting only the most useful features from the data, cutting unnecessary branches of the classification system, etc. We also implemented a strategy to correct certain types of misclassifications, in order to improve the performance of the system.

We categorized a person’s daily activities into three activity states, including stationary, walking, and running. Based on data collected from five subjects, we trained a classification system that provides an activity state feedback every second and yields a classification accuracy of 94.82%. Our experiments also demonstrated that the strategies applied to reduce system size and improve system performance worked well.

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