A Multi-Task Learning Approach for Meal Assessment

Key role in the prevention of diet-related chronic diseases plays the balanced nutrition together with a proper diet. The conventional dietary assessment methods are time-consuming, expensive and prone to errors. New technology-based methods that provide reliable and convenient dietary assessment, have emerged during the last decade. The advances in the field of computer vision permitted the use of meal image to assess the nutrient content usually through three steps: food segmentation, recognition and volume estimation. In this paper, we propose a use one RGB meal image as input to a multi-task learning based Convolutional Neural Network (CNN). The proposed approach achieved outstanding performance, while a comparison with state-of-the-art methods indicated that the proposed approach exhibits clear advantage in accuracy, along with a massive reduction of processing time

Image-based food classification and volume estimation for dietary assessment: a review.

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

Point2Volume: A vision-based dietary assessment approach using view synthesis

Dietary assessment is an important tool for nutritional epidemiology studies. To assess the dietary intake, the common approach is to carry out 24-h dietary recall (24HR), a structured interview conducted by experienced dietitians. Due to the unconscious biases in such self-reporting methods, many research works have proposed the use of vision-based approaches to provide accurate and objective assessments. In this article, a novel vision-based method based on real-time three-dimensional (3-D) reconstruction and deep learning view synthesis is proposed to enable accurate portion size estimation of food items consumed. A point completion neural network is developed to complete partial point cloud of food items based on a single depth image or video captured from any convenient viewing position. Once 3-D models of food items are reconstructed, the food volume can be estimated through meshing. Compared to previous methods, our method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation

A Systematic Literature Review With Bibliometric Meta-Analysis Of Deep Learning And 3D Reconstruction Methods In Image Based Food Volume Estimation Using Scopus, Web Of Science And IEEE Database

Purpose- Estimation of food portions is necessary in image based dietary monitoring techniques. The purpose of this systematic survey is to identify peer reviewed literature in image-based food volume estimation methods in Scopus, Web of Science and IEEE database. It further analyzes bibliometric survey of image-based food volume estimation methods with 3D reconstruction and deep learning techniques. Design/methodology/approach- Scopus, Web of Science and IEEE citation databases are used to gather the data. Using advanced keyword search and PRISMA approach, relevant papers were extracted, selected and analyzed. The bibliographic data of the articles published in the journals over the past twenty years were extracted. A deeper analysis was performed using bibliometric indicators and applications with Microsoft Excel and VOS viewer. A comparative analysis of the most cited works in deep learning and 3D reconstruction methods is performed. Findings: This review summarizes the results from the extracted literature. It traces research directions in the food volume estimation methods. Bibliometric analysis and PRISMA search results suggest a broader taxonomy of the image-based methods to estimate food volume in dietary management systems and projects. Deep learning and 3D reconstruction methods show better accuracy in the estimations over other approaches. The work also discusses importance of diverse and robust image datasets for training accurate learning models in food volume estimation. Practical implications- Bibliometric analysis and systematic review gives insights to researchers, dieticians and practitioners with the research trends in estimation of food portions and their accuracy. It also discusses the challenges in building food volume estimator model using deep learning and opens new research directions. Originality/value- This study represents an overview of the research in the food volume estimation methods using deep learning and 3D reconstruction methods using works from 1995 to 2020. The findings present the five different popular methods which have been used in the image based food volume estimation and also shows the research trends with the emerging 3D reconstruction and deep learning methodologies. Additionally, the work emphasizes the challenges in the use of these approaches and need of developing more diverse, benchmark image data sets for food volume estimation including raw food, cooked food in all states and served with different containers

Collaborative design and feasibility assessment of computational nutrient sensing for simulated food-intake tracking in a healthcare environment

One in four older adults (65 years and over) are living with some form of malnutrition. This increases their odds of hospitalization four-fold and is associated with decreased quality of life and increased mortality. In long-term care (LTC), residents have more complex care needs and the proportion affected is a staggering 54% primarily due to low intake. Tracking intake is important for monitoring whether residents are meeting their nutritional needs however current methods are time-consuming, subjective, and prone to large margins of error. This reduces the utility of tracked data and makes it challenging to identify individuals at-risk in a timely fashion. While technologies exist for tracking food-intake, they have not been designed for use within the LTC context and require a large time burden by the user. Especially in light of the machine learning boom, there is great opportunity to harness learnings from this domain and apply it to the field of nutrition for enhanced food-intake tracking. Additionally, current approaches to monitoring food-intake tracking are limited by the nutritional database to which they are linked making generalizability a challenge. Drawing inspiration from current methods, the desires of end-users (primary users: personal support workers, registered staff, dietitians), and machine learning approaches suitable for this context in which there is limited data available, we investigated novel methods for assessing needs in this environment and imagine an alternative approach. We leveraged image processing and machine learning to remove subjectivity while increasing accuracy and precision to support higher-quality food-intake tracking. This thesis presents the ideation, design, development and evaluation of a collaboratively designed, and feasibility assessment, of computational nutrient sensing for simulated food-intake tracking in the LTC environment. We sought to remove potential barriers to uptake through collaborative design and ongoing end user engagement for developing solution concepts for a novel Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) system while implementing the technology in parallel. More specifically, we demonstrated the effectiveness of applying a modified participatory iterative design process modeled from the Google Sprint framework in the LTC context which identified priority areas and established functional criteria for usability and feasibility. Concurrently, we developed the novel AFINI-T system through the co-integration of image processing and machine learning and guided by the application of food-intake tracking in LTC to address three questions: (1) where is there food? (i.e., food segmentation), (2) how much food was consumed? (i.e., volume estimation) using a fully automatic imaging system for quantifying food-intake. We proposed a novel deep convolutional encoder-decoder food network with depth-refinement (EDFN-D) using an RGB-D camera for quantifying a plate’s remaining food volume relative to reference portions in whole and modified texture foods. To determine (3) what foods are present (i.e., feature extraction and classification), we developed a convolutional autoencoder to learn meaningful food-specific features and developed classifiers which leverage a priori information about when certain foods would be offered and the level of texture modification prescribed to apply real-world constraints of LTC. We sought to address real-world complexity by assessing a wide variety of food items through the construction of a simulated food-intake dataset emulating various degrees of food-intake and modified textures (regular, minced, puréed). To ensure feasibility-related barriers to uptake were mitigated, we employed a feasibility assessment using the collaboratively designed prototype. Finally, this thesis explores the feasibility of applying biophotonic principles to food as a first step to enhancing food database estimates. Motivated by a theoretical optical dilution model, a novel deep neural network (DNN) was evaluated for estimating relative nutrient density of commercially prepared purées. For deeper analysis we describe the link between color and two optically active nutrients, vitamin A, and anthocyanins, and suggest it may be feasible to utilize optical properties of foods to enhance nutritional estimation. This research demonstrates a transdisciplinary approach to designing and implementing a novel food-intake tracking system which addresses several shortcomings of the current method. Upon translation, this system may provide additional insights for supporting more timely nutritional interventions through enhanced monitoring of nutritional intake status among LTC residents