Date of Award


Document Type


Degree Name

Master of Science (MS)

Legacy Department

Electrical Engineering

Committee Chair/Advisor

Dr. Adam W. Hoover

Committee Member

Dr. Richard E. Groff

Committee Member

Dr. Eric R. Muth


This thesis considers the problem of detecting the eating activities (e.g. meals, snacks) of people by tracking their wrist motion. Our goal is to automatically de- tect the start and end times of eating activities during free-living. It builds upon previous work done by our research group [6] [7] [8] [21]. The detection is done by segmenting data into segments then classifying the segments as EA (eating activity) and NonEA (non-eating activity) using a Bayesian classier. Previous features used in the classier developed by our group included the sum of acceleration, the amount of manipulation, the amount of wrist roll and the regularity of wrist roll [6] [7] [8]. Additional features studied include a frequency analysis of manipulation, the time since the last EA, and the cumulative time spent eating in a day [21]. In this thesis we study two new features: the autocorrelation of the manipulation and the o-line analysis of the time since the last EA. The autocorrelation feature enables the study of patterns of manipulation that may not be precisely regular, and facilitates frequency analysis through the transform of the manipulation signal into something more sinu- soidal. The o-line analysis of the time since the last EA allows for the calculation of every possible combination of segment classications throughout the day, so that an early incorrect classication of a segment as an EA does not always inadvertently aect the classication of subsequent segments. This thesis further discusses these concepts and then tests them under the framework developed by our group. The overall accuracy of regularity of autocorrelation of manipulation along with the orig- inal 4 features is 70%. The overall accuracy of trying every combination along with the original 4 features is 65%. Finally, we compare the results to the previous work and discuss the results based on our findings.



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