Imperial College London

DrBennyLo

Faculty of MedicineDepartment of Surgery & Cancer

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
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Location

 

B414BBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Rosa:2019:10.1109/BSN.2019.8771070,
author = {Rosa, BG and Anastasova-Ivanova, S and Lo, B and Yang, GZ},
doi = {10.1109/BSN.2019.8771070},
publisher = {IEEE},
title = {Towards a fully automatic food intake recognition system using acoustic, image capturing and glucose measurements},
url = {http://dx.doi.org/10.1109/BSN.2019.8771070},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - 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.
AU - Rosa,BG
AU - Anastasova-Ivanova,S
AU - Lo,B
AU - Yang,GZ
DO - 10.1109/BSN.2019.8771070
PB - IEEE
PY - 2019///
SN - 2376-8886
TI - Towards a fully automatic food intake recognition system using acoustic, image capturing and glucose measurements
UR - http://dx.doi.org/10.1109/BSN.2019.8771070
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000492872400012&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://ieeexplore.ieee.org/document/8771070
UR - http://hdl.handle.net/10044/1/75192
ER -