Measuring and localizing individual bites using a sensor augmented plate during unrestricted eating for the aging population.

Food intake monitoring can play an important role in the prevention of malnutrition in the aging population, but traditional tools may not be adequate for use in this target group. Traditional tools typically involve the use of questionnaires or food diaries that require manual data entry. Due to their time consuming nature, they are often incomplete, contain mistakes or not used at all. An alternative to self-reporting tools, in the form of a plate system that automatically measures the consumed food during the meal, is presented in this work. Furthermore, the system can estimate the location where each bite was taken on the plate. The system is compatible with an off-the-shelf plate that is mounted on top of a base station. Weight sensors need not be integrated in the plate itself. Localization of the bites is done by looking at the movement of the center of mass during eating. When used with a compartmentalized plate, the amount of consumed food per compartment can be estimated. With prior knowledge of the type of food in each compartment, this can give an indication of the amount of calories and nutritional intake. We present a bite detection algorithm using a random forest decision tree classifier. Data from 24 aging adults (ages 52-95) eating a single meal with chopsticks was used to train and evaluate the model. Out of a total of 836 true annotated bites, the algorithm detected 602 with a precision and recall of 0.78 and 0.76, respectively. By summing the weights of detected bites from each compartment, the algorithm was able to estimate the amount of food taken per compartment with an average error of (8 ±8)% of the portion size.

Jaar van publicatie:2020

BOF-keylabel:ja

IOF-keylabel:ja

BOF-publication weight:2

CSS-citation score:1

Auteurs:International

Authors from:Higher Education

Toegankelijkheid:Open