scholarly journals Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1556
Author(s):  
Zhengeng Yang ◽  
Hongshan Yu ◽  
Shunxin Cao ◽  
Qi Xu ◽  
Ding Yuan ◽  
...  

It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system “mentally” gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an “intelligent guess”, implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results.

2020 ◽  
Author(s):  
zhengeng yang ◽  
Hongshan Yu ◽  
Shunxin Cao ◽  
Wenyan Jia ◽  
Qi Xu ◽  
...  

Abstract Background: It is well-known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed that allows assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage overweight, obesity and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of objective and passive dietary assessment with much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms.Method : A novel Artificial Intelligent (AI) system is proposed to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system "mentally" gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an "intelligent guess", implemented by an inner product between the probability vector and the reference volume vector.Dataset: The datasets utilized for model validation include: 1) two virtual food datasets produced by computer simulation, and 2) two real-world food datasets collected by us.Results: The average relative volumetric errors of our AI method were less than 9% on both virtual datasets, and 11.7% and 20.1% , respectively, on the two real-world food datasets.Discussion: We discuss: 1) the use of AI to estimate the "relative volume" of food in a plate, 2) the case of multiple foods in a plate, and 3) the potential of AI in advancing nutrition science.Conclusion: Our AI system is able to use the same food volume estimation strategy as the human uses.


1970 ◽  
Vol 11 (1) ◽  
Author(s):  
Norm Campbell CM, MD, FRCPC ◽  
Michel Sauvé MD FRCP FACP FCCP MSc

Chronic diseases including cardiovascular disease and cancer are the leading causes of disability and death in Canada.1,2 The majority of chronic diseases are caused by physical inactivity, tobacco use, excess alcohol consumption and unhealthy diet.3-6 In particular, unhealthy diet is the leading risk factor for death and disability in Canada resulting in an estimated 64,000 deaths and over 1 million years of disability (DALYs) in 2010 alone.7 Worldwide, a staggering 11 million deaths and over 200 million DALYs were attributed to unhealthy eating in 2010.


2015 ◽  
Vol 76 (3) ◽  
pp. 103-108 ◽  
Author(s):  
Sophie Desroches ◽  
Annie Lapointe ◽  
Sarah-Maude Deschênes ◽  
Véronique Bissonnette-Maheux ◽  
Karine Gravel ◽  
...  

Purpose: To assess dietitians’ perspectives on the importance and applicability of interventions to enhance adherence to dietary advice for preventing and managing chronic diseases in adults in the Canadian context. Methods: Based on a Cochrane systematic review, we identified 8 promising interventions for enhancing adherence to dietary advice: behavioural contracts, exchange lists, feedback based on self-monitoring, individualized menu suggestions, multiple interventions, portion size awareness, telephone follow-up, and videos. Thirty-two dietitians then completed a 3-round Delphi study by responding to an electronic questionnaire asking them to rate the importance and applicability in their practice of the 8 interventions on a 7-point Likert scale. Results: Using a ≥75% level of agreement, 4 interventions showed strong consensus: multiple interventions, feedback based on self-monitoring, portion size awareness, and videos. Among these, the most significant were (means ± SD for importance and applicability, respectively) feedback based on self-monitoring (6.97 ± 0.18 and 6.72 ± 0.46), portion size awareness (6.69 ± 0.54 and 6.75 ± 0.51), and multiple interventions (6.94 ± 0.25 and 6.81 ± 0.40). Conclusions: These findings can guide the development of educational training sessions for dietitians to help them provide practice-relevant interventions that will increase the likelihood that patients adhere to their advice regarding prevention and management of chronic diseases.


Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 564 ◽  
Author(s):  
Sepehr Makhsous ◽  
Hashem M. Mohammad ◽  
Jeannette M. Schenk ◽  
Alexander V. Mamishev ◽  
Alan R. Kristal

. Over the past ten years, diabetes has rapidly become more prevalent in all age demographics and especially in children. Improved dietary assessment techniques are necessary for epidemiological studies that investigate the relationship between diet and disease. Current nutritional research is hindered by the low accuracy of traditional dietary intake estimation methods used for portion size assessment. This paper presents the development and validation of a novel instrumentation system for measuring accurate dietary intake for diabetic patients. This instrument uses a mobile Structured Light System (SLS), which measures the food volume and portion size of a patient’s diet in daily living conditions. The SLS allows for the accurate determination of the volume and portion size of a scanned food item. Once the volume of a food item is calculated, the nutritional content of the item can be estimated using existing nutritional databases. The system design includes a volume estimation algorithm and a hardware add-on that consists of a laser module and a diffraction lens. The experimental results demonstrate an improvement of around 40% in the accuracy of the volume or portion size measurement when compared to manual calculation. The limitations and shortcomings of the system are discussed in this manuscript.


2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yifan Yang ◽  
Wenyan Jia ◽  
Tamara Bucher ◽  
Hong Zhang ◽  
Mingui Sun

AbstractObjectiveCurrent approaches to food volume estimation require the person to carry a fiducial marker (e.g. a checkerboard card), to be placed next to the food before taking a picture. This procedure is inconvenient and post-processing of the food picture is time-consuming and sometimes inaccurate. These problems keep people from using the smartphone for self-administered dietary assessment. The current bioengineering study presents a novel smartphone-based imaging approach to table-side estimation of food volume which overcomes current limitations.DesignWe present a new method for food volume estimation without a fiducial marker. Our mathematical model indicates that, using a special picture-taking strategy, the smartphone-based imaging system can be calibrated adequately if the physical length of the smartphone and the output of the motion sensor within the device are known. We also present and test a new virtual reality method for food volume estimation using the International Food Unit™ and a training process for error control.ResultsOur pilot study, with sixty-nine participants and fifteen foods, indicates that the fiducial-marker-free approach is valid and that the training improves estimation accuracy significantly (P<0·05) for all but one food (egg, P>0·05).ConclusionsElimination of a fiducial marker and application of virtual reality, the International Food Unit™ and an automated training allowed quick food volume estimation and control of the estimation error. The estimated volume could be used to search a nutrient database and determine energy and nutrients in the diet.


Author(s):  
K. Chien ◽  
R.C. Heusser ◽  
M.L. Jones ◽  
R.L. Van de Velde

Silver impregnation techniques have been used for the demonstration of the complex carbohydrates in electron microscopy. However, the silver stains were believed to be technically sensitive and time consumming to perform. Currently, due to the need to more specifically evaluate immune complex for localization in certain renal diseases, a simplified procedure in conjunction with the use of the microwave has been developed and applied to renal and other biopsies. The procedure is as follows:Preparation of silver methenamine solution:1. 15ml graduated, clear polystyrene centrifuge tube (Falcon, No. 2099) was rinsed once with distilled water.2. 3% hexamethylene tetramine (methenamine) was added into the centrifuge tube to the 6ml mark.3. 3% silver nitrate was added slowly to the methenamine to the 7ml mark while agitating. (Solution will instantly turn milky in color and then clear rapidly by mixing. No precipitate should be formed).4. 2% sodium borate was added to the solution to the 8ml mark, mixed and centrifuged before use.


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