Assessment of food intake has a wide range of
applications in public health and life-style related chronic dis-
ease 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 real-
time 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 real-
time feedback. Detailed user studies have also been performed to
demonstrate the practical value of the software environment.