title: Improving the recognition of eating gestures using intergesture sequential dependencies.
abstract: This paper considers the problem of recognizing eating gestures by tracking wrist motion. Eating gestures are activities commonly undertaken during the consumption of a meal, such as sipping a drink of liquid or using utensils to cut food. Each of these gestures causes a pattern of wrist motion that can be tracked to automatically identify the activity. Previous works have studied this problem at the level of a single gesture. In this paper, we demonstrate that individual gestures have sequential dependence. To study this, three types of classifiers were built: 1) a K-nearest neighbor classifier which uses no sequential context, 2) a hidden Markov model (HMM) which captures the sequential context of subgesture motions, and 3) HMMs that model intergesture sequential dependencies. We built first-order to sixth-order HMMs to evaluate the usefulness of increasing amounts of sequential dependence to aid recognition. On a dataset of 25 meals, we found that the baseline accuracies for the KNN and the subgesture HMM classifiers were 75.8% and 84.3%, respectively. Using HMMs that model intergesture sequential dependencies, we were able to increase accuracy to up to 96.5%. These results demonstrate that sequential dependencies exist between eating gestures and that they can be exploited to improve recognition accuracy.
authors.forename: Raul I
authors.initials: RI
authors.lastname: Ramos-Garcia
authors.forename: Eric R
authors.initials: ER
authors.lastname: Muth
authors.forename: John N
authors.initials: JN
authors.lastname: Gowdy
authors.forename: Adam W
authors.initials: AW
authors.lastname: Hoover
mesh_terms: D000203:Activities of Daily Living; D000465:Algorithms; D004435:Eating / Q000502:physiology*; D005868:Gestures; D006801:Humans; D008390:Markov Chains; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D017216:Telemedicine; D014953:Wrist / Q000502:physiology