A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the best-performing methodologies that have been developed so far for automatic food recognition and volume estimation. Firstly, the paper presented the rationale of visual-based methods for food recognition. Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases. In this context, this study discusses the mobile applications that are implementing these methods for automatic food logging. Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition. Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest. Finally, this survey ends with a discussion of potential applications of food image analysis, existing research gaps, and open issues of this research area. Learning from unlabeled image datasets in an unsupervised manner, catastrophic forgetting during continual learning, and improving model transparency using explainable AI are potential areas of interest for future studies.

Younger Adults Are More Likely to Increase Fruit and Vegetable Consumption and Decrease Sugar Intake with the Application of Dietary Monitoring

Establishing healthy eating habits is considered to be a sustainable strategy for health maintenance, and mobile applications (apps) are expected to be highly effective among the young-aged population for healthy eating promotion. The purpose of this study was to investigate the effectiveness of a dietary monitoring app on younger adults’ nutrition knowledge and their dietary habits. A controlled-experimental study was performed with one experimental group having a three-hour nutrition seminar and 12 weeks of dietary monitoring with the app, and one control group receiving a three-hour nutrition seminar. Behavioral feedback delivered by the app was evaluated in facilitating the transfer of nutritional knowledge to nutrition behavior. A total of 305 younger adults aged from 19 to 31 were recruited. Baseline and post-intervention nutrition knowledge and dietary behavior were collected. All mean scores of post-GNKQ-R increased from baseline for both the control and the experimental groups. The mean differences of sugar intake, dietary fiber intake, and vitamin C intake for the experimental group were significantly more than those for the control group (all p < 0.001). In addition, the experimental group increased fruit and vegetable consumption significantly more than the control group (all p < 0.001). For those younger adults with a relatively large body size, they were more likely to increase fruit consumption with the application of dietary monitoring.