Date of Award


Document Type


Degree Name

Master of Science (MS)

Legacy Department

Computer Engineering

Committee Chair/Advisor

Hoover, Adam W

Committee Member

Gowdy, John N

Committee Member

Muth, Eric R


Advances in body-worn sensors and mobile health technology have created new opportunities for empowering people to take a more active role in managing their health. Obesity has been recognized as a target of opportunity that could particularly benefit from this approach. Self-monitoring of dietary intake is critical for weight loss/management, but currently used tools such as
food diaries require users to manually estimate and record energy intake, making them subjective, prone to error, and difficult to use for long periods of time. Our group is developing a new tool called
the 'bite counter' that automates the monitoring of caloric intake. The device is worn like a watch and uses sensors to track wrist motion during a meal. Previous studies have shown that our method
accurately counts bites during controlled and uncontrolled meals in the lab. This thesis describes a study to evaluate the accuracy of the method in a cafeteria setting. A cafeteria booth that can
seat 1 to 4 people was instrumented with tethered wrist motion trackers, embedded scales, and video cameras, to enable recording of wrist motion, changes in food weight, and actual activities
during eating. A total of 276 subjects were recorded eating uncontrolled meals. The data was manually reviewed and the times of all actual bites taken were recorded as 'ground truth'. The
wrist motion data was then analyzed using the automated bite counting method to determine the times of automated bite detections. These were compared against the ground truth to evaluate the accuracy of the bite counting method. In total, 22,383 bites were evaluated, consisting of 380 different foods, eaten using 4 different utensils from 4 different containers, across a variety of subject demographics. Results show that the method varied in accuracy from 39 % (for ice cream cones) to 88% (for salad bar) across the 39 most commonly eaten foods (>=100 bite occurrences in the data set). The average accuracy found across all bites was 76% with a positive predictive value of 87%. A second test of the bite counting method using modified timing thresholds resulted in 82% accuracy with a 82% positive predictive value. These results indicate that the method works well across a wide variety of foods, utensils, containers, and subject demographics. The results also indicate that
eating rate may be the most important variable to consider in the search for improvements to the method.



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