Name
Title | ||
|---|---|---|
Towards a Fully Automatic Food Intake Recognition System Using Acoustic, Image Capturing and Glucose Measurements |
Abstract | ||
|---|---|---|
Food intake is a major healthcare issue in developed countries that has become an economic and social burden across all sectors of society. Bad food intake habits lead to increased risk for development of obesity in children, young people and adults, with the latter more prone to suffer from health diseases such as diabetes, shortening the life expectancy. Environmental, cultural and behavioural factors have been appointed to be responsible for altering the balance between energy intake and expenditure, resulting in excess body weight. Methods to counteract the food intake problem are vast and include self-reported food questionnaires, body-worn sensors that record the sound, pressure or movements in the mouth and GI tract or image-based approaches that recognize the different types of food being ingested. In this paper we present an ear-worn device to track food intake habits by recording the acoustic signal produced by the chewing movements as well as the glucose level amperiometrically. Combined with a small camera on a future version of the device, we hope to deliver a complete system to control dietary habits with caloric intake estimation during satiation and deficit during satiety periods, which can be adapted to the physiology of each user. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BSN.2019.8771070 | 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN) |
Keywords | Field | DocType |
fully automatic food intake recognition system,healthcare issue,developed countries,economic burden,social burden,bad food intake habits,young people,health diseases,life expectancy,environmental factors,cultural factors,behavioural factors,energy intake,expenditure,excess body weight,food intake problem,food questionnaires,body-worn sensors,image-based approaches,ear-worn device,acoustic signal,glucose level,dietary habits,caloric intake estimation | Computer vision,Recognition system,Computer science,Caloric theory,Obesity,Artificial intelligence,Life expectancy,Body weight,Environmental health,Glucose Measurement | Conference |
ISSN | ISBN | Citations |
2376-8886 | 978-1-7281-0804-9 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruno Gil Rosa | 1 | 2 | 1.72 |
| Salzitsa Anastasova-Ivanova | 2 | 0 | 0.34 |
| Benny Lo | 3 | 403 | 37.89 |
| Guang-Zhong Yang | 4 | 2812 | 297.66 |