A Systematic Review of Sensor-Based Methodologies for Food Portion Size Estimation

Raju, Viprav B.; Sazonov, Edward

doi:10.1109/jsen.2020.3041023

Cited by 10 publications

(7 citation statements)

References 102 publications

(111 reference statements)

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“…Thus, reducing any error is desirable. Also with increasing availability of wearable sensors (see [19]) there are opportunities for hybrid approaches combining direct (based on physical properties of food) and indirect (based on food intake activity) measures to reduce overall error. Future work will focus on improving the accuracy of the automated aspects of the approach, for example the accurate identification of corner markers that is critical to accurate 3D projection, and determining the impact of increased training [12] for improvements in the manual aspects, i.e.…”

Section: Discussionmentioning

confidence: 99%

Food Volume Estimation by Integrating 3D Image Projection and Manual Wire Mesh Transformations

et al. 2022

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2D images can be used to capture food intake data in nutrition studies. Estimates of food volume from these images are required for nutrient analysis. Although 3D image capture is possible, it is not commonplace. Additionally, nutrition studies often require multiple food images taken by non-expert users, typically collected using mobile phones, due to their convenience. Current 2D image to 3D volume approaches are restricted by the need for prescribed camera placement, image metadata analysis and/or significant computational resources. A new method is presented combining 2D image capture and automated 3D scene projection with manual placement and resizing of wire mesh objects. 2D images, with a reference object, are taken on low specification mobile phones. 3D scene projection is calculated by twinning a cuboid in 3D space to the reference object in the 2D image. A manually selected 3D wire mesh object is then positioned over the target food item and manually transformed to improve accuracy. The virtual wire mesh object is then projected into the 3D scene and the volume of the target food item calculated. The whole process is computationally light, and runs in real-time as an app on a standard Apple iPad. Based on a user study with 60 participants, experimental evaluations of volume estimates over regular shape and ground truth food items demonstrate that this approach provides acceptable accuracy. We demonstrate that the accuracy of estimates can be increased by combining multiple independent estimates.

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Section: Discussionmentioning

confidence: 99%

Food Volume Estimation by Integrating 3D Image Projection and Manual Wire Mesh Transformations

et al. 2022

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“…The abovementioned manual methods are cumbersome, subject to memory (and therefore prone to error), and are not accurate compared to the much recent automatic assessment methods. A previous review [ 13 ] identified some of the existing image-based food portion size estimation (FPSE) methods that are automatic. It was seen that food portion size can be estimated automatically using food images captured during the meal [ 14 ], [ 15 ], [ 16 ], [ 17 ], [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Plate and Bowl Dimensions for Food Portion Size Assessment in a Wearable Sensor System

2023

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Automatic food portion size estimation (FPSE) with minimal user burden is a challenging task. Most of the existing FPSE methods use fiducial markers and/or virtual models as dimensional references.An alternative approach is to estimate the dimensions of the eating containers prior to estimating the portion size.In this paper, we propose a wearable sensor system (the Automatic Ingestion Monitor integrated with a ranging sensor) and a related method for the estimation of dimensions of plates and bowls. The contributions of this study are: (1) The model eliminates the need for fiducial markers. (2) The camera system (AIM-2) is not restricted in terms of positioning relative to the food item. (3) Our model accounts for radial lens distortion caused due to lens aberrations. (4) A ranging sensor directly gives the distance between the sensor and the eating surface. (5) The model is not restricted to circular plates. (6) The proposed system implements a passive method that can be used for assessment of container dimensions with minimum user interaction. The error rates (mean ± std. dev) for dimension estimation were 2.01 ± 4.10 % for plate widths/diameters, 2.75 ± 38.11 % for bowl heights, and 4.58 ± 6.78 % for bowl diameters.

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“…To monitor Sensors 2022, 22, 1493 2 of 17 the intake of energy and nutrients accurately, each food in the image must be identified and its volume estimated. Although food recognition has been extensively studied using deep learning techniques [11][12][13][14][15][16][17], estimating food volume from images remains a challenging problem [9][10][11]18]. Several sensor-based approaches have been reported [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…Although food recognition has been extensively studied using deep learning techniques [11][12][13][14][15][16][17], estimating food volume from images remains a challenging problem [9][10][11]18]. Several sensor-based approaches have been reported [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. A special imaging sensor called a depth sensor has been used to produce depth on a per-pixel basis from which food volume can be estimated [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…Due to these limitations, the volume of food in a 2D image can only be estimated roughly and an estimation error, sometimes considerably large, must be tolerated [35,36]. Several estimation methods have been reported [10,17,18,35,[37][38][39][40][41][42][43][44][45][46][47][48][49]. A set of computer-generated three-dimensional wireframes is used to conform to food surfaces and compute food volumes [37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation

Jia

Ren

et al. 2022

Sensors

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Knowing the amounts of energy and nutrients in an individual’s diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.

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A Systematic Review of Sensor-Based Methodologies for Food Portion Size Estimation

Cited by 10 publications

References 102 publications

Food Volume Estimation by Integrating 3D Image Projection and Manual Wire Mesh Transformations

Food Volume Estimation by Integrating 3D Image Projection and Manual Wire Mesh Transformations

Estimation of Plate and Bowl Dimensions for Food Portion Size Assessment in a Wearable Sensor System

A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation

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