Speaker: Professor Bart Vanrumste, Catholic University of Leuven

Time and Date: 10:00-11:00 am, April 1, 2019

Place: Room 206 of Genetics Building, Handan Campus, Fudan University

Abstract:

Food intake monitoring can play an important role in the prevention of malnutrition among older adults. Traditional monitoring methods typically involve the use of pen-and-paper food diaries or questionnaires. While digital alternatives exist, these tools rely on manual data entry, often multiple times a day. Two sensor systems and corresponding algorithms are proposed in this presentation.

The first is an accelerometer based wearable system, with the accelerometer mounted on the eyeglasses of the user. The eyeglasses are used as a platform to mount the accelerometer in close proximity to the head. The ears of the glasses are able to transmit the vibrations and movements of mastication muscles in the skull during chewing to the accelerometer, where it can be converted into an acceleration signal and used for the detection of chewing activity. A machine learning algorithm is proposed to automatically detect the chewing activity.

Furthermore, a smart plate system is presented. A prototype was designed and developed consisting of a custom embedded system and sensors. The system consists of a base station and an off-the-shelf polymer plate that is mounted on top of the base. Weight sensors in the base accurately measure the weight of consumed food from the plate. The novelty of this system is the ability to measure the location of individual bites on the plate. In combination with a compartmentalised plate, the system can estimate from which compartment a bite was taken, without any sensors or electronics embedded into the plate itself or physical changes to the plate. With prior knowledge of which food type was served in which compartment, this can allow for an accurate estimation of the total amount of ingested calories. For the bite localisation to work, an accurate detection of the individual bites is required. For this, a novel bite detection algorithm is presented.