Abstract
A daily dietary assessment method named 24-hour dietary recall has commonly been used in nutritional epidemiology studies to capture detailed information of the food eaten by the participants to help understand their dietary behaviour. However, in this self-reporting technique, the food types and the portion size reported highly depends on users' subjective judgement which may lead to a biased and inaccurate dietary analysis result. As a result, a variety of visual-based dietary assessment approaches have been proposed recently. While these methods show promises in tackling issues in nutritional epidemiology studies, several challenges and forthcoming opportunities, as detailed in this study, still exist. This study provides an overview of computing algorithms, mathematical models and methodologies used in the field of image-based dietary assessment. It also provides a comprehensive comparison of the state of the art approaches in food recognition and volume/weight estimation in terms of their processing speed, model accuracy, efficiency and constraints. It will be followed by a discussion on deep learning method and its efficacy in dietary assessment. After a comprehensive exploration, we found that integrated dietary assessment systems combining with different approaches could be the potential solution to tackling the challenges in accurate dietary intake assessment.
Highlights
A RECENT National Health Service (NHS) survey [1] in England reported that the proportion of adults who were obese or overweight was 26% and 36% respectively in 2016
It starts from generating descriptors based on Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT)
Further research works to examine these hypothesis and additional validation studies are required. This is the first review which investigates the underlying algorithms and mathematical models used in the field of dietary assessment especially on food recognition and volume estimation
Summary
A RECENT National Health Service (NHS) survey [1] in England reported that the proportion of adults who were obese or overweight was 26% and 36% respectively in 2016. Since the procedure of the manual data collection is not carried out directly by experienced dietitians, the consumed portion size can only be estimated by users based on their visual perceptions (e.g., 1 bowl of rice, 1 cup of juice) instead of using weight scales, and the portion reported highly depends on their judgement which may lead to a biased and inaccurate dietary analysis result To address this inaccuracy in dietary assessments, increasing numbers of automatic dietary assessment devices/systems with various sensing modalities, ranging from acoustic sensing approach [4], inertial sensing approach [5] to physiological measurement approach [6], have been studied in the past decade. This study, on the other hand, provides an extensive review with the focus on the underlying computing algorithms, mathematical models and methodologies applied in image-based approaches and they are compared and assessed in technical aspects such as processing speed and efficiency, food recognition and volume estimation accuracy and constraints.
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