Food Recognition and Nutritional Apps

Food recognition and nutritional apps are trending technologies that may revolutionise the way people with diabetes manage their diet. Such apps can monitor food intake as a digital diary and even employ artificial intelligence to assess the diet automatically. Although these apps offer a promising solution for managing diabetes, they are rarely used by patients. This chapter aims to provide an in-depth assessment of the current status of apps for food recognition and nutrition, to identify factors that may inhibit or facilitate their use, while it is accompanied by an outline of relevant research and development.Comment: This book chapter: Food Recognition and Nutritional Apps is set to appear in the book: "Diabetes Digital Health, Telehealth, and Artificial Intelligence

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

Dietary assessment and obesity aviodance system based on vision: A review

Using technology for food objects recognition and estimation of its calories is very useful to spread food culture and awareness among people in the age of obesity due to the bad habits of food consumption and wide range of inappropriate food products.Image based sensing of such system is very promising with the large expanding of camera embedded portable devices such as smartphones, PC tablets, and laptops.In the past decade, researchers have been working on developing a reliable image based system for food recognition and calories estimation.Different approaches have tackled the system from different aspects.This paper reviews the state of the art of this interesting application, and presents its experimental results.Future work of research is presented in order to guide new researchers toward potential tracks to create more maturity and reliability to this application

食事画像からの自動カロリー量推定システムの実現

近年では, 健康的至高の高まりにより食事記録を付ける人が増えてきている. それに伴い食事記録支援システムが多く公開され始めているが, 既存のシステムのほとんどが正確にカロリー量を推定することができない. そこで本論文では食品を基準物体と撮影することで, 食品の認識を行い, さらに大きさを推定することでその食品のカロリー量を推定するシステムを提案する. システムはユーザーの携帯性や利便性を考えスマートフォンアプリという形での実装を行う. システムは画像中より食品領域及び基準物体領域を抽出し, その大きさを比較する. 基準物体は事前に面積がわかっていること以外には制約はなく, ユーザーが各々常に携帯しているものを使用することが出来る. 食品認識部分の手法には高精度な認識が可能なディープラーニングを用いた. 一般にディープラーニングによる画像認識は計算量が多くモバイルでの利用は難しいが, パラメータ数が少ないネットワークを選択したりなどの工夫により, サーバを介さずモバイル上での実行ながら約0.2 秒程度での実行速度で高精度な認識を可能にした. 実験ではカロリー量推定実験とユーザー評価実験の2 つを行い結果としてカロリー量推定実験での誤差の平均は52.231kcal, 相対誤差の平均は0.213 となった.ユーザー評価実験でも既存システムよりも記録を取りやすいという評価を得た. このことから提案システムの有効性が確認できた.電気通信大学201

ACCURACY OF A BITE-COUNT BASED CALORIE ESTIMATE COMPARED TO HUMAN ESTIMATES WITH AND WITHOUT CALORIE INFORMATION AVAILABLE

Obesity is an increasing health problem in the US, associated with such dangerous health risks as heart disease and diabetes. Self-monitoring in the form of calorie counting is a critical aspect of successful weight loss. However, calorie estimations are subject to several perceptual and cognitive biases, and there are limited tools available to assist these estimations. The present study seeks to assess the accuracy of participants\u27 estimations of the calorie content of meals in the presence or absence of calorie information, and to compare their accuracy with calorie estimations based on bite count. Data were analyzed for 87 participants from a study in which participants were allowed to select from a wide variety of meals in a cafeteria setting, which they consumed while wearing a device designed to count bites of food. They were asked to estimate the number of calories they consumed either with or without calorie information available. True calorie intake and a calorie intake estimation based on bite count were calculated for each participant. A 2x2 Mixed-Design ANOVA revealed a significant main effect for estimation method (F(1, 83) = 14.381, p \u3c .001), a marginally significant effect for the presence of calorie information (F(1, 83) = 3.835, p = .054), and a significant interaction between estimation method and the presence of calorie information (F(1, 83) = 6.384, p \u3c .05). Post-hoc tests revealed that errors in human calorie estimations were significantly improved by the presence of calorie information (t(45.89) = -2.731 p \u3c .01). Calorie estimations based on bite count were significantly more accurate than human estimates without the aid of calorie information (t(32) = -3.578, p \u3c .005), but there was no significant difference between estimations based on bite count and human estimates with the aid of calorie information (t(52) = -1.116, p = .270). The results suggest that bite count may aid individuals with calorie estimation when other aids are unavailable or be a less burdensome alternative to certain calorie estimation aids

Grocery Shopping Assistant Using OpenCV

In this paper we present an android mobile application that allows user to keep track of food products and grocery items bought during each grocery shopping along with its nutrient information. This application allows user to get nutrient information of products and grocery by just taking a photo. Product matching is performed using SURF feature detection followed by FLANN feature matching. We extract the table from the nutrient fact table image using concepts of erosion, dilation and contour detection. Classifying the grocery is done using Object Categorization through the concepts of Bag of Words (BOW) and SVM machine learning. This application includes three main subsystems: client (Android), server (Node.js) and image processing (OpenCV)