Date of Award
Master of Science (MS)
Gowdy , John
Muth , Eric
We introduce a new algorithm of bite detection during an eating activity based on template matching. The algorithm uses a template to model the motion of the wrist over a 6-second window centered on the time when a person takes a bite. We also determine if diﬀerent types of bites (for example food vs. drink, or using diﬀerent types of utensils) have diﬀerent wrist motion templates. This method is implemented on 22,383 bites and 5 diﬀerent types of templates are built. We then describe a method to recognize diﬀerent types of bites using the set of templates. The obtained accuracy was 46%. Finally, we describe a method to detect bites using the set of templates and compare its accuracy to the original threshold-based algorithm. We get positive predictive value of 75 % and true positive rate of 47% found across all bites.
Eskandari, Soheila, "Bite detection and differentiation using templates of wrist motion" (2013). All Theses. 1818.