Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor

Yang, Xin; Doulah, Abul Barkat Mollah Sayeed Ud; Farooq, Muhammad; Parton, Jason; McCrory, Megan A.; Higgins, Janine; Sazonov, Edward

doi:10.1038/s41598-018-37161-x

Cited by 14 publications

(12 citation statements)

References 42 publications

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“…The same research group conducted another study using just the strain sensor [32]. In both studies, a piezoelectric strain sensor (LDT0-028K, Meas-Spec Inc.) was attached to the skin immediately below the earlobe using a medical adhesive (Hollister 7730).…”

Section: Strain Sensorsmentioning

confidence: 99%

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

Raju

Sazonov

2021

IEEE Sensors J.

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Food portion size estimation (FPSE) is critical in dietary assessment and energy intake estimation. Traditional methods such as visual estimation are now replaced by faster, more accurate sensor-based methods. This paper presents a comprehensive review of the use of sensor methodologies for portion size estimation. The review was conducted using the PRISMA guidelines and full texts of 67 scientific articles were reviewed. The contributions of this paper are threefold: i) A taxonomy for sensor-based (SB) FPSE methods was identified, classifying the sensors (as wearable, portable and stationary) and the methodology (as direct and indirect). ii) A novel comprehensive review of the state-of-theart SB-FPSE methods was conducted and 5 sensor modalities (Acoustic, Strain, Imaging, Weighing, and Motion sensors) were identified. iii) The accuracy of portion size estimation and the applicability to free-living conditions of these SB-FPSE methods were assessed. This article concludes with a discussion of challenges and future trends of SB-FPSE.

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Section: Strain Sensorsmentioning

confidence: 99%

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

Raju

Sazonov

2021

IEEE Sensors J.

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“…Although video observation and manual annotation is an accepted gold standard for these studies, the manual chew count can be inaccurate, time-consuming and a burden to the investigators when the data size is large. In [ 25 ], the authors used both manual annotation of videos and sensor-driven features to quantify energy intake. They mentioned that manual analysis is not realistic on a large scale.…”

Section: Introductionmentioning

confidence: 99%

Automatic Count of Bites and Chews From Videos of Eating Episodes

2020

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Methods for measuring of eating behavior (known as meal microstructure) often rely on manual annotation of bites, chews, and swallows on meal videos or wearable sensor signals. The manual annotation may be time consuming and erroneous, while wearable sensors may not capture every aspect of eating (e.g. chews only). The aim of this study is to develop a method to detect and count bites and chews automatically from meal videos. The method was developed on a dataset of 28 volunteers consuming unrestricted meals in the laboratory under video observation. First, the faces in the video (regions of interest, ROI) were detected using Faster R-CNN. Second, a pre-trained AlexNet was trained on the detected faces to classify images as a bite/no bite image. Third, the affine optical flow was applied in consecutively detected faces to find the rotational movement of the pixels in the ROIs. The number of chews in a meal video was counted by converting the 2-D images to a 1-D optical flow parameter and finding peaks. The developed bite and chew count algorithm was applied to 84 meal videos collected from 28 volunteers. A mean accuracy (±STD) of 85.4% (±6.3%) with respect to manual annotation was obtained for the number of bites and 88.9% (±7.4%) for the number of chews. The proposed method for an automatic bite and chew counting shows promising results that can be used as an alternative solution to manual annotation.

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“…The sensors can also determine the number of unique food items in a meal [7]- [9]. Energy intake may be estimated from sensor data, such as counts of chews and swallows [10], [11], or hand gestures [12], [13]. The sensors are also capable of monitoring behavioral metrics of food consumption, such as The associate editor coordinating the review of this manuscript and approving it for publication was Lin Wang .…”

Section: Introductionmentioning

confidence: 99%

Detection of Oil-Containing Dressing on Salad Leaves Using Multispectral Imaging

Raju

Sazonov

2020

IEEE Access

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There is a need for non-invasive methods for nutritional analysis of food that can address the drawbacks of current methods such as color photography, which cannot distinguish between energydense and zero calorie foods. This paper discusses a novel, multispectral approach for the identification of oil, particularly in salads, and defines a pseudo-reflectance term. A custom-made multispectral camera was used to collect a novel, publicly shared dataset of images of untreated lettuce leaves or leaves treated with vinegar, oil, or a combination of these. The camera captured image data at 10 wavelengths ∈[380nm,980nm] across the electromagnetic spectrum in the visible and NIR (near-infrared) regions. Imaging was done in a lab environment with the presence of ambient light. Mean spectra were extracted from the regions of interest in the multispectral cube and used to compute pseudo-reflectance. ANOVA (Analysis of Variance) was performed to look for variances in the pseudo-reflectance curves. ANOVA proved that the differences between group means of the four treatment groups (oil, vinegar, oil plus vinegar, control) were statistically significant. Pairs of groups showing the greatest significance were established using a Tukey post hoc test. Sequential Forward Selection (SFS) was used to determine 5 optimal feature wavelengths from the whole feature space (410 nm, 455 nm, 485 nm, 810 nm, and 850 nm). A combination of visible (VIS) and infrared (IR) wavelengths, selected using SFS, showed the greatest potential for discrimination between groups containing oil and groups that do not contain oil with a classification accuracy of 84.20%. The pseudo-reflectance values were statistically proven to be sensitive to the presence of oil as a dressing. This research has demonstrated the feasibility of implementing a multispectral imaging technique for identifying the presence of oil in salads and possibly an energy content detection system. INDEX TERMS Multispectral imaging, energy density, food detection, high calorie detection, dietary assessment.

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Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor

Cited by 14 publications

References 42 publications

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

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

Automatic Count of Bites and Chews From Videos of Eating Episodes

Detection of Oil-Containing Dressing on Salad Leaves Using Multispectral Imaging

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