Measuring and Localizing Individual Bites Using a Sensor Augmented Plate During Unrestricted Eating for the Aging Population

Abstract

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. These 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 are integrated in the base, allowing for easy removal and cleaning of the plate. Localization of 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 measured. With prior knowledge of the type of food in each compartment, this can give an indication 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.

A Systematic Review of Sensor-Based Methodologies for Food Portion Size Estimation

“…The ubiquity of cameras in portable devices such as mobile phones has been the main motivation for researchers to use imaging extensively. Mandometer [101] and the smart plate [99], [100] are two portable-weighing sensors.…”

“…The authors reported an average error (mean ± standard deviation) of 8 ± 8 % in portion size weight estimation in lab conditions. They extended their work in [100] by evaluating a newer bite detection algorithm on multiple measurements with varying food types. The extension also included more realistic eating conditions and a larger dataset.…”

“…State of the art devices/systems embedded with weighing sensors for FPSE: (a) The Mandometer V.5[101]; (b) Instrumented table with the OHAUS Scout Pro SP4001 scale[98]; (c) Smart Tablecloth-fabric-based pressure matrix[97]; (d) Plate system employed in[99],[100]: base station and plate. Plate contains three compartments with 3 load cells.…”

Quantifying Eating Behavior With a Smart Plate in Patients With Arm Impairment After Stroke

“…The model we previously presented in [11] is used to detect bites. It uses a Random Forest classifier to detect bites and was trained with data from 18 healthy middle aged to older adults, each consuming a single meal.…”

“…In previous work, we presented a variation on the table embedded scale in standalone format: a smart plate with integrated weight sensors that can automatically measure food intake [10], [11]. In addition to the total food intake, the system can detect and measure individual bites, which can give a better insight into ingestive behavior by extracting information such as bite size and time spent per bite.…”