Personalized Dietary Self-Management Using Mobile Vision-Based Assistance
Lecture notes in computer sciencePublished 1 January 2017
Georg Waltner, Michael Schwarz, Stefan Ladstätter, Anna Weber, Patrick Luley, Meinrad Lindschinger
Citations38
SJR quartileQ2
SJR score0.35
SNIP0.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This work presents a highly personalized mobile application that assists the user in appropriate food choices during grocery shopping, while simultaneously incorporating a personalized dietary recommender system.
Abstract
Daily appropriate decision making on nutrition requires application of knowledge where it matters, and being adjusted to the individual requirements. We present a highly personalized mobile application that assists the user in appropriate food choices during grocery shopping, while simultaneously incorporating a personalized dietary recommender system. The application can be used in video based augmented reality mode, where a computer vision algorithm recognizes presented food items and thus replaces tedious search within the food database. The recognition system employs a shallow Convolutional Neural Network (CNN) based classifier running at 10 fps. An innovative user study demonstrates the high usability and user experience of the application. The vision classifier is evaluated on a newly introduced reference image database containing 81 grocery foods (vegetables, fruits).
Keywords
Computer ScienceMedicine
2009 IEEE Conference on Computer Vision and Pattern RecognitionImageNet: A large-scale hierarchical image database
61,841 Citations2009Jia Deng, Wei Dong +4 more
A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
SUS: A 'Quick and Dirty' Usability Scale
8,122 Citations1996john Brooke
Large-Scale Video Classification with Convolutional Neural Networks
6,282 Citations2014Andrej Karpathy, George Toderici +4 more
This work studies multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggests a multiresolution, foveated architecture as a promising way of speeding up the training.
Lecture notes in computer scienceFood-101 – Mining Discriminative Components with Random Forests
1,963 Citations2014Lukas Bossard, Matthieu Guillaumin +1 more
A novel method to mine discriminative parts using Random Forests (rf), which allows us to mine for parts simultaneously for all classes and to share knowledge among them, and compares nicely to other s-o-a component-based classification methods.
Proceedings of the National Academy of SciencesA Biometric Study of Human Basal Metabolism
1,955 Citations1918Jacqueline Harris, Fräncis G. Benedict
The American Journal of PsychologyUser Centered System Design: New Perspectives on Human-Computer Interaction
1,849 Citations1988Roy Pea, Donald A. Norman +1 more
International Journal of Industrial ErgonomicsComputer interface evaluation using eye movements: methods and constructs
1,033 Citations1999Joseph H. Goldberg, Xerxes P. Kotval
Data obtained from eye movements can significantly enhance the observation of users' strategies while using computer interfaces, which can subsequently improve the precision of computer interface evaluations.
Recognition of Multiple-Food Images by Detecting Candidate Regions
369 Citations2012Yuji Matsuda, Hajime Hoashi +1 more
A feature-fusion-based food recognition method for bounding boxes of the candidate regions with various kinds of visual features including bag-of-features of SIFT and CSIFT with spatial pyramid, histogram of oriented gradient (HoG), and Gabor texture features is applied.
Food Detection and Recognition Using Convolutional Neural Network
299 Citations2014Hokuto Kagaya, Kiyoharu Aizawa +1 more
This paper constructed a dataset of the most frequent food items in a publicly available food-logging system, and used it to evaluate recognition performance, and found that the convolution kernels show that color dominates the feature extraction process.
Lecture notes in computer scienceAutomatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation
278 Citations2015Yoshiyuki Kawano, Keiji Yanai
This paper proposes to leverage existing knowledge on food of other cultures by a generic “foodness” classifier and domain adaptation to enable not only to built other-cultured food datasets based on an original food image dataset automatically, but also to save as much crowd-sourcing costs as possible.
PFID: Pittsburgh fast-food image dataset
252 Citations2009Mei Chen, Kapil Dev Dhingra +4 more
The first visual dataset of fast foods is introduced with a total of 4,545 still images, 606 stereo pairs, 303 360° videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers to stimulate research on fast food recognition for dietary assessment.
Wide-Slice Residual Networks for Food Recognition
228 Citations2018Niki Martinel, Gian Luca Foresti +1 more
This work introduces a new deep scheme that is designed to handle the food structure, and first introduces a slice convolution block to capture the vertical food traits that are common to a large number of categories.
Food image recognition with deep convolutional features
207 Citations2014Yoshiyuki Kawano, Keiji Yanai
The feature obtained from the Deep Convolutional Neural Network boosts food recognition accuracy greatly by integrating it with conventional hand-crafted image features, Fisher Vectors with HoG and Color patches.
Food Image Recognition Using Very Deep Convolutional Networks
199 Citations2016Hamid Hassannejad, Guido Matrella +4 more
The effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception appears to be at least closer to the requirements for mobile systems.
Computers in Biology and MedicineRetrieval and classification of food images
110 Citations2016Giovanni Maria Farinella, Dario Allegra +3 more
A new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification is proposed.
VeggieVision: a produce recognition system
87 Citations2002Ruud M. Bolle, Jonathan H. Connell +3 more
The authors present an automatic product 1D system ("VeggieVision"), intended to ease the produce checkout process, which consists of an integrated scale and imaging system with a user-friendly interface.
Lecture notes in computer scienceA Benchmark Dataset to Study the Representation of Food Images
81 Citations2015Giovanni Maria Farinella, Dario Allegra +1 more
The UNICT-FD889 dataset is introduced, the first food image dataset composed by over \(800\) distinct plates of food which can be used as benchmark to design and compare representation models of food images, and results confirm that both textures and colors are fundamental properties in food representation.
Classifying food images represented as Bag of Textons
71 Citations2014Giovanni Maria Farinella, Marco Moltisanti +1 more
It is pointed out, through a set of experiments, that textures are fundamental to properly recognize different food items and are more accurate than existing (and more complex) approaches in classifying the 61 classes of the Pittsburgh Fast-Food Image Dataset.
Food log by analyzing food images
71 Citations2008Keigo Kitamura, Toshihiko Yamasaki +1 more
A food-logging system that can distinguish food images from other images, analyze the food balance, and visualize the log is presented, based on feature vectors consisting of color histograms, DCT coefficients, detected image patterns and so forth.
Segmentation and recognition of multi-food meal images for carbohydrate counting
58 Citations2013Marios Anthimopoulos, Joachim Dehais +2 more
The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients and a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem.
Real-time mobile recipe recommendation system using food ingredient recognition
44 Citations2012Takuma Maruyama, Yoshiyuki Kawano +1 more
The proposed system carries out object recognition on food ingredients in a real-time way on an Android-based smartphone, and recommends cooking recipes related to the recognized food ingredients, and achieves the 83.93% recognition rate within the top six candidates.
Lecture notes in computer scienceMANGO - Mobile Augmented Reality with Functional Eating Guidance and Food Awareness
38 Citations2015Georg Waltner, Michael Schwarz +7 more
First results of the Austrian project (MANGO) which develops mobile assistance for instant, situated information access via Augmented Reality (AR) functionality to support the user during everyday grocery shopping are presented.
Smartphone eye tracking toolbox
36 Citations2014Lucas Paletta, Helmut Neuschmied +5 more
An integrated software package, i.e., the Smartphone Eye Track-ing Toolbox (SMET) that enables accurate gaze recovery on mobile displays with heat mapping of recent attention and shows results from eye movement analysis, such as, ROI dwell time and statistics on eye gaze event.
Clinical Chemistry and Laboratory Medicine (CCLM)Oxidative stress: potential of distinct peroxide determination systems
35 Citations2004Meinrad Lindschinger, Karl Nadlinger +6 more
The Pox-Act assay seems to be a valuable tool for the determination of total peroxides, while the results from the d-ROMS kit should be considered with caution.