Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user\u27s memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors

Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

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Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods

Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis

The aim of this thesis is to develop automated methods for the analysis of the spatial patterns, and the functional behaviour of endothelial cells, viewed under microscopy, with applications to the understanding of atherosclerosis. Initially, a radial search approach to segmentation was attempted in order to trace the cell and nuclei boundaries using a maximum likelihood algorithm; it was found inadequate to detect the weak cell boundaries present in the available data. A parametric cell shape model was then introduced to fit an equivalent ellipse to the cell boundary by matching phase-invariant orientation fields of the image and a candidate cell shape. This approach succeeded on good quality images, but failed on images with weak cell boundaries. Finally, a support vector machines based method, relying on a rich set of visual features, and a small but high quality training dataset, was found to work well on large numbers of cells even in the presence of strong intensity variations and imaging noise. Using the segmentation results, several standard shear-stress dependent parameters of cell morphology were studied, and evidence for similar behaviour in some cell shape parameters was obtained in in-vivo cells and their nuclei. Nuclear and cell orientations around immature and mature aortas were broadly similar, suggesting that the pattern of flow direction near the wall stayed approximately constant with age. The relation was less strong for the cell and nuclear length-to-width ratios. Two novel shape analysis approaches were attempted to find other properties of cell shape which could be used to annotate or characterise patterns, since a wide variability in cell and nuclear shapes was observed which did not appear to fit the standard parameterisations. Although no firm conclusions can yet be drawn, the work lays the foundation for future studies of cell morphology. To draw inferences about patterns in the functional response of cells to flow, which may play a role in the progression of disease, single-cell analysis was performed using calcium sensitive florescence probes. Calcium transient rates were found to change with flow, but more importantly, local patterns of synchronisation in multi-cellular groups were discernable and appear to change with flow. The patterns suggest a new functional mechanism in flow-mediation of cell-cell calcium signalling

Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multi-spectral perception algorithms and aerial and ground-based systems developed for monitoring crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment, as well as the modules for collaborative mapping. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and to perform selective spraying without human intervention.Comment: Published in IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp. 29-49, Sept. 202

Does weight management research for adults with severe obesity represent them? Analysis of systematic review data

Acknowledgments We thank the members of the REBALANCE Project and Advisory Groups for their contributions to the REBALANCE Project. We thank Shaun Treweek and Heidi Gardner, Health Services Research Unit, University of Aberdeen, for helpful discussions on trial generalisability and inclusion of underserved groups. Funding National Institute for Health Research Health Technology Assessment Programme (project number: 15/09/04).Peer reviewedPublisher PD

A Deep learning based food recognition system for lifelog images

In this paper, we propose a deep learning based system for food recognition from personal life archive im- ages. The system first identifies the eating moments based on multi-modal information, then tries to focus and enhance the food images available in these moments, and finally, exploits GoogleNet as the core of the learning process to recognise the food category of the images. Preliminary results, experimenting on the food recognition module of the proposed system, show that the proposed system achieves 95.97% classification accuracy on the food images taken from the personal life archive from several lifeloggers, which potentially can be extended and applied in broader scenarios and for different types of food categories

Summary report on sensory-related socio-economic and sensory science literature about organic food products

Organic food’s initial attraction to the public was that it was perceived to be healthier and tastier, but scientists and policy makers have mainly stressed the benefits to the environment of organic and sustainable farming. Scientific support for marketing actions addressed to those who want to be healthier and who want to enjoy better taste, and are willing to pay more for these benefits is scarce. Past research has produced little clear evidence about the importance of sensory characteristics such as taste, smell, appearance etc in consumers’ preferences with regard to organic food. The Ecropolis project, funded by the E.U., was set up with the aim of investigating the role of the senses in consumers’ preferences regarding organic food, and leading to research into how best to satisfy those preferences. This deliverable is aimed at providing a solid basis for such research with an in-depth review of, and two reports on, the relevant scientific literature. The first report (Annex I) regards what consumers expect from organic products in terms of taste, smell, appearance, etc and how these expectations are (or are not) met; the second is about the science of the senses (Annex II). The first project tasks included creating and agreeing on a glossary of terms, deciding on search criteria (key words, etc.), setting up a bibliographical data base, preparing then circulating the above-mentioned reports, and finally preparing a summary of the reports. The report on consumers expectations highlights the suggestion that while organic food has traditionally been marketed through specialized retailers, its market share will only grow significantly if it is promoted by multiple retailers. Research literature from all over the world seems to agree in indicating that consumers’ choices are largely motivated by health, the environment, price and social status. Other considerations include ethics, the localness of the product and lifestyle choices. The literature also indicates that the organic market will expand significantly only if consumers are more willing, and able, to recognize quality, but this presents serious issues. When buying the product they cannot personally verify its quality and genuineness and thus must rely on regulation and inspection bodies. The recognition of quality can also be encouraged by effective communication by producers and retailers through appropriate branding, labelling and presentation. There are connections between this information and questions of sense perception, but researchers disagree about how important the latter is in influencing the customer, and in which ways it does so. The following report focuses, in fact, on the science of the senses, which tries to analyze in detail people’s responses to food, despite the many potential pitfalls in carrying out the research which might influence the reliability of the results. There is broad agreement on two points: - there is no proof that organic food is more nutritious or safer, and - most studies that have compared the taste and organoleptic quality of organic andconventional foods report no consistent or significant differences between organic and conventional produce. Therefore, claiming that all organic food tastes different from all conventional food would not be correct. However, among the well-designed studies with respect to fruits and vegetables that have found differences, the vast majority favour organic produce. Organic produce tends to store better and has longer shelf life, probably because of lower levels of nitrates and higher average levels of antioxidants. The former can accelerate food spoilage, while antioxidants help preserve the integrity of cells and some are natural antibiotics. The first conclusion may, however, depend on factors not directly connected to organic farming, such as harvesting and storage methods and the type of land used for growing the food. About the second finding it must be considered that measuring organoleptic quality is difficult and inherently subjective and evaluations may be clouded by the influence of numerous factors on the consumer’s perceptions of the food and not just its appearance and taste. Experimental research indicates that the information that a food is organic confers upon it a “halo effect” (making it seem better sense-wise simply because it is organic) which might make consumers like it more. Ecropolis researchers will analyze in detail which senses are indeed impacted on, and how, and try to match them to consumer needs and expectations in order to be able to offer suggestions for future policy, including how the food is stored, transported and presented, which is also essential for maintaining sensory properties. The workpackage WP1 has also produced a specific report on how organic food sensory aspects are regulated. International standards, with some important exceptions, are largely in line with European ones. Differences in standards usually regard whether there is orientation towards freshness “per se” as opposed to increasing shelf-life, or quality standardization as opposed to quality differentiation. Differences in regulations regard such aspects as ingredients, additives, processing aids and methods, packaging, storage and transport. The lack of harmony among the different regulatory systems often reflects different traditions and market conditions, however, more complicated compliance procedures result in higher costs for importers. Greater homogeneity would not only reduce such costs but would also increase consumer confidence in international standards. Ecropolis will also investigate the effect of different regulations on how people perceive organic goods sense-wise. The work done to date is seen as a starting point for future research aimed at producing practical results in the organic food market. Ecropolis will try to bring together separate strands of research concerning how organic goods are regulated and marketed with regard to taste, appearance, etc., and how consumers themselves are affected by such factors. The aim is to find optimal matches between the two, and thus to greatly increase organic food’s share of the food market

Productivity Measurement for Home Health Care Registered Nurses

The purpose of this study is to develop a productivity measurement applicable to home health registered nurses (RNs) by identifying and quantifying the knowledge and ability variables that define productive nurse practice. A preliminary set of knowledge and ability variables was identified based on content analysis of interviews with local nurse managers and round I of a three round Delphi procedure, using a purposive sample of nurse managers from nationally preeminent agencies. A randomized national sample of 337 nurse managers was then surveyed to determine the relative value and rank of the knowledge and ability variables. These variables were refined during Delphi round II and III. Based on the three Delphi rounds, the interviews and the responses to the national survey, a profile was developed, using factor analysis, consisting of 35 important knowledge and ability variables. These variables clustered into seven constructs: Practice Management, Knowledge/Skill Maintenance, Written Documentation, Home Health Care Knowledge, Communication, Nursing Process, and Client/Family Management. Within these seven constructs, the following individual variables were considered most important: skill in health assessment and hands on technical skill, documentation, independent decision making, communication, organizational ability, and a foundation in teaching/learning principles and home care rules and regulations. Qualitatively identified associations among variables were statistically supported. Nonparametric tests, including the Kruskal-Wallis and Mann-Whitney U test, were used to identify differences in the importance of specific knowledge and ability variables among governmental, hospital based, proprietary, and VNA agencies, and between hospice and non-hospice agencies. No significant differences were found among agency types. However, among agencies considered preeminent, intellectual skills appeared to be of greater importance to productive practice than direct care skills. Results of this study suggest a profile of productivity dimensions which provides (1) a theoretical basis for understanding the knowledge and ability variables associated with RN productivity in the home health setting, (2) a description of nurse inputs in a home health services productivity model, and (3) a reality based measurement tool that has utility in understanding and managing RN productivity in home health care