TY - GEN
T1 - Real-time food intake classification and energy expenditure estimation on a mobile device
AU - Ravi, Daniele
AU - Lo, Benny
AU - Yang, Guang Zhong
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2015/10/15
Y1 - 2015/10/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84961641018&partnerID=8YFLogxK
U2 - 10.1109/BSN.2015.7299410
DO - 10.1109/BSN.2015.7299410
M3 - Conference contribution
AN - SCOPUS:84961641018
T3 - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
BT - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
Y2 - 9 June 2015 through 12 June 2015
ER -