Smartphone-based Calorie Estimation From Food Image Using Distance Information

Personal assistive systems for diet control can play a vital role to combat obesity. As smartphones have become inseparable companions for a large number of people around the world, designing smartphone-based system is perhaps the best choice at the moment. Using this system people can take an image of their food right before eating, know the calorie content based on the food items on the plate. In this paper, we propose a simple method that ensures both user flexibility and high accuracy at the same time. The proposed system employs capturing food images with a fixed posture and estimating the volume of the food using simple geometry. The real world experiments on different food items chosen arbitrarily show that the proposed system can work well for both regular and liquid food items

Smartphone as a Personal, Pervasive Health Informatics Services Platform: Literature Review

Objectives: The article provides an overview of current trends in personal sensor, signal and imaging informatics, that are based on emerging mobile computing and communications technologies enclosed in a smartphone and enabling the provision of personal, pervasive health informatics services. Methods: The article reviews examples of these trends from the PubMed and Google scholar literature search engines, which, by no means claim to be complete, as the field is evolving and some recent advances may not be documented yet. Results: There exist critical technological advances in the surveyed smartphone technologies, employed in provision and improvement of diagnosis, acute and chronic treatment and rehabilitation health services, as well as in education and training of healthcare practitioners. However, the most emerging trend relates to a routine application of these technologies in a prevention/wellness sector, helping its users in self-care to stay healthy. Conclusions: Smartphone-based personal health informatics services exist, but still have a long way to go to become an everyday, personalized healthcare-provisioning tool in the medical field and in a clinical practice. Key main challenge for their widespread adoption involve lack of user acceptance striving from variable credibility and reliability of applications and solutions as they a) lack evidence-based approach; b) have low levels of medical professional involvement in their design and content; c) are provided in an unreliable way, influencing negatively its usability; and, in some cases, d) being industry-driven, hence exposing bias in information provided, for example towards particular types of treatment or intervention procedures

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

Volume estimation using food specific shape templates in mobile image-based dietary assessment

As obesity concerns mount, dietary assessment methods for prevention and intervention are being developed. These methods include recording, cataloging and analyzing daily dietary records to monitor energy and nutrient intakes. Given the ubiquity of mobile devices with built-in cameras, one possible means of improving dietary assessment is through photographing foods and inputting these images into a system that can determine the nutrient content of foods in the images. One of the critical issues in such the image-based dietary assessment tool is the accurate and consistent estimation of food portion sizes. The objective of our study is to automatically estimate food volumes through the use of food specific shape templates. In our system, users capture food images using a mobile phone camera. Based on information (i.e., food name and code) determined through food segmentation and classification of the food images, our system choose a particular food template shape corresponding to each segmented food. Finally, our system reconstructs the three-dimensional properties of the food shape from a single image by extracting feature points in order to size the food shape template. By employing this template-based approach, our system automatically estimates food portion size, providing a consistent method for estimation food volume

Measuring the healthfulness of children's diets and the role of the home food environment

Although diet is well recognized as key to good health in children, methods for assessing diet and an understanding of the environmental factors that influence children's diets are limited.. One widely used method to evaluate diet quality is HEI-2010, but the utility of this method has not been previously examined in children. In this study, we found that children with higher HEI-2010 scores were more likely to meet micronutrient requirements (mean micronutrient adequacy ratio 82.4±1.9 vs. 60.8±1.6) and less likely to over-consume energy (+2.1±4.7 % vs. +17.8±3.2%) compared to children with lower HEI scores. However, HEI-2010 did not adequately assess some components of diet in young children (ages 2 to 12). For example, children who received the maximum HEI score for the dairy component often consumed less than the recommended level of for calcium (-21%), vitamin D (-3%) and vitamin A (-11%) compared to children who met the dairy Dietary Guideline. Overall, HEI-2010 was an effective tool for assessing nutrient quality in the diets of older children, but had important flaws when used in younger children. These findings led us to use the Dietary Guidelines rather than HEI-2010 to measure the association between the availability of foods in the home and child diet. We found that parents of African American children were less likely to report always having fruit (percent difference from reference,-12%) and low-fat milk(-10%), and more likely to report dark greens(+10%) in their homes compared to white children. Children who always vs. rarely, had a food in their homes were more likely to meet the dietary guideline for that food: OR (95% CI); Fruit: 2.61(1.01, 6.75), Dark greens: 3.33(0.76, 14.40), and low-fat milk: 1.44(1.04, 2.00). Children who always, compared to rarely, had soft drinks available were more likely to exceed the recommended empty calorie limit from calories in soft drinks: 1.92(1.34, 2.74). Many of the current dietary methods were first developed for adults and later applied to children. By thoroughly examining the utility of these tools for children, and, when necessary, developing child specific versions of tools, we can uncover important intervention targets, such as increasing the availability of healthy foods in the home.Doctor of Philosoph

La aplicación de la inteligencia artificial en la nutrición personalizada

El desarrollo tecnológico ha repercutido en diversas áreas del conocimiento y la actividad humana. La irrupción de las tecnologías como big data, machine learning o inteligencia artificial están revolucionando las relaciones humanas; dichas tecnologías, actualmente, se utilizan en diversas actividades y ámbitos. En este contexto, la nutrición se emplea para promover alimentación de calidad, crear alimentos nutritivos, establecer patrones de consumo saludables, evitar el desperdicio de alimentos, generar seguridad alimentaria, nutrición personalizada, entre otros. De ahí que en esta investigación se realiza una revisión documental de las principales investigaciones sobre las diversas aplicaciones de la inteligencia artificial en el campo de la nutrición personalizada. De tal modo que la formulación del problema reza así: ¿Cómo se aplica la inteligencia artificial en la nutrición personalizada? El objetivo fue: analizar la aplicación de la inteligencia artificial en la nutrición personalizada. La metodología consistió en (i) enfoque: cualitativo, (ii) tipo de investigación: descriptivo-exploratorio, (iii) métodos: descriptivo y observación, (iv) técnicas: análisis documental y análisis de contenido y (v) instrumentos: ficha de resumen, ficha de análisis documental y ficha de análisis bibliográfico. Finalmente, los resultados y conclusiones arribados consisten en: (i) la inteligencia artificial se aplica en la nutrición personalizada a través de aplicativos móviles y otros, (ii) la inteligencia artificial contribuye en la nutrición personalizada y (iii) el uso inadecuado de la inteligencia artificial podría originar riesgos en la nutrición personalizada

Making the best use of new technologies in the National Diet and Nutrition Survey: a review

.Background Dietary assessment is of paramount importance for public health monitoring. Currently in the UK, the population’s diets are examined by the National Diet and Nutrition Survey Rolling Programme (NDNS RP). In the survey, diet is assessed by a four-day paper-based dietary diary, with accompanying interviews, anthropometric measurements and blood and urine sampling. However, there is growing interest worldwide in the potential for new technologies to assist in data collection for assessment of dietary intake. Published literature reviews have identified the potential of new technologies to improve accuracy, reduce costs, and reduce respondent and researcher burden by automating data capture and the nutritional coding process. However, this is a fast-moving field of research, with technologies developing at a rapid pace, and an updated review of the potential application of new technologies in dietary assessment is warranted. This review was commissioned to identify the new technologies employed in dietary assessment and critically appraise their strengths and limitations in order to recommend which technologies, if any, might be suitable to develop for use in the NDNS RP and other UK population surveys. Objectives The overall aim of the project was to inform the Department of Health of the range of new technologies currently available and in development internationally that have potential to improve, complement or replace the methods used in the NDNS RP. The specific aims were: to generate an itinerary of new and emerging technologies that may be suitable; to systematically review the literature and critically appraise new technologies; and to recommend which of these new technologies, if any, would be appropriate for future use in the NDNS RP. To meet these aims, the project comprised two main facets, a literature review and qualitative research. Literature review data sources The literature review incorporated an extensive search of peer-reviewed and grey literature. The following sources were searched: Cochrane Database of Systematic Reviews (CDSR), Database of Abstracts of Reviews of Effectiveness (DARE), Web of Science Core Collection, Ovid MEDLINE, Ovid MEDLINE In-Process, Embase, NHS EED (Economic Evaluation Database), National Cancer Institute (NCI) Dietary Assessment Calibration/Validation Register, OpenGrey, EPPI Centre (TRoPHI), conference proceedings (ICDAM 2012, ISBNPA 2013, IEEE Xplore, Nutrition Society Irish Section and Summer Meetings 2014), recent issues of journals (Journal of Medical Internet Research, International Journal of Medical Informatics), grants registries (ClinicalTrials.gov, BBSRC, report), national surveys, and mobile phone application stores. In addition, hand-searching of relevant citations was performed. The search also included solicitation of key authors in the field to enquire about Making the best use of new technologies in the NDNS: a review 4 as-yet unpublished articles or reports, and a Bristol Online Survey publicised via social media, society newsletters and meetings. Literature review eligibility criteria Records were screened for eligibility using a three-stage process. Firstly, keyword searches identified obviously irrelevant titles. Secondly, titles and abstracts were screened against the eligibility criteria, following which full-text copies of papers were obtained and, in the third stage of screening, examined against the criteria. Two independent reviewers screened each record at each stage, with discrepancies referred to a third reviewer. Eligibility criteria were pre-specified and agreed by the project Steering Group (Section 1.6). Eligible records included: studies involving technologies, new to the NDNS RP, which can be used to automate or assist the collection of food consumption data and the coding of foods and portion sizes, currently available or beta versions, public domain or commercial; studies that address the development, features, or evaluation of new technology; technologies appropriate for the requirements of the NDNS RP in terms of nutritional analysis, with capacity to collect quantifiable consumption data at the food level; primary sources of information on a particular technology; and journal articles published since the year 2000 or grey literature available from 2011 onwards. The literature search was not limited to Englishlanguage publications, which are included in the itinerary, although data were not extracted from non-English studies. Literature synthesis and appraisal New technologies were categorised into eleven types of technology, and an itinerary was generated of tools falling under each category type. Due to the volume of eligible studies identified by the literature searches, data extraction was limited to the literature focussing on selected exemplar tools of five technology categories (web-based diet diary, web-based 24- hour recall, handheld devices (personal digital assistants and mobile phones), nonautomated cameras to complement traditional methods, and non-automated cameras to replace traditional methods). For each category, at least two exemplars were chosen, and all studies involving the exemplar were included in data extraction and synthesis. Exemplars were selected on the basis of breadth of evidence available, using pre-specified criteria agreed by the Steering Group. Data were extracted by a single reviewer and an evidence summary collated for each exemplar. A quality appraisal checklist was developed to assess the quality of validation studies. The checklist was piloted and applied by two independent reviewers. Studies were not excluded on the basis of quality, but study quality was taken into account when judging the strength of evidence. Due to the heterogeneity of the literature, meta-analyses were not performed. References were managed and screened using the EPPI Reviewer 4 systematic review software. EPPI Reviewer was also used to extract data

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

Examining the eating patterns of young adults

This thesis explored the determinates of young adult (18-30 years) eating patterns. Qualitative and quantitative analysis was conducted to examine what influences young adults to skip meals and snack throughout the day. Findings suggest young adults are highly influenced by a lack of time, availability and their peers.<br /