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research
Real-time food intake classification and energy expenditure estimation on a mobile device
Authors
B Lo
D Ravi
G Yang
Publication date
15 April 2015
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
Abstract
© 2015 IEEE.Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for realtime mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for realtime feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment
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info:doi/10.1109%2Fbsn.2015.72...
Last time updated on 01/04/2019
Supporting member
Spiral - Imperial College Digital Repository
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oai:spiral.imperial.ac.uk:1004...
Last time updated on 17/02/2017