Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review

Xin Chen; Ernest Kamavuako

doi:10.3390/s23136137

Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review

Xin Chen, Ernest Kamavuako^*

^*Corresponding author for this work

Engineering

Research output: Contribution to journal › Review article › peer-review

1 Citation (Scopus)

Abstract

Food and fluid intake monitoring are essential for reducing the risk of dehydration, malnutrition, and obesity. The existing research has been preponderantly focused on dietary monitoring, while fluid intake monitoring, on the other hand, is often neglected. Food and fluid intake monitoring can be based on wearable sensors, environmental sensors, smart containers, and the collaborative use of multiple sensors. Vision-based intake monitoring methods have been widely exploited with the development of visual devices and computer vision algorithms. Vision-based methods provide non-intrusive solutions for monitoring. They have shown promising performance in food/beverage recognition and segmentation, human intake action detection and classification, and food volume/fluid amount estimation. However, occlusion, privacy, computational efficiency, and practicality pose significant challenges. This paper reviews the existing work (253 articles) on vision-based intake (food and fluid) monitoring methods to assess the size and scope of the available literature and identify the current challenges and research gaps. This paper uses tables and graphs to depict the patterns of device selection, viewing angle, tasks, algorithms, experimental settings, and performance of the existing monitoring systems.

Original language	English
Article number	6137
Journal	SENSORS
Volume	23
Issue number	13
DOIs	https://doi.org/10.3390/s23136137
Publication status	Published - 4 Jul 2023

Keywords

intake monitoring
drinking action detection
dietary monitoring
vision-based methods

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review",

abstract = "Food and fluid intake monitoring are essential for reducing the risk of dehydration, malnutrition, and obesity. The existing research has been preponderantly focused on dietary monitoring, while fluid intake monitoring, on the other hand, is often neglected. Food and fluid intake monitoring can be based on wearable sensors, environmental sensors, smart containers, and the collaborative use of multiple sensors. Vision-based intake monitoring methods have been widely exploited with the development of visual devices and computer vision algorithms. Vision-based methods provide non-intrusive solutions for monitoring. They have shown promising performance in food/beverage recognition and segmentation, human intake action detection and classification, and food volume/fluid amount estimation. However, occlusion, privacy, computational efficiency, and practicality pose significant challenges. This paper reviews the existing work (253 articles) on vision-based intake (food and fluid) monitoring methods to assess the size and scope of the available literature and identify the current challenges and research gaps. This paper uses tables and graphs to depict the patterns of device selection, viewing angle, tasks, algorithms, experimental settings, and performance of the existing monitoring systems.",

keywords = "intake monitoring, drinking action detection, dietary monitoring, vision-based methods",

author = "Xin Chen and Ernest Kamavuako",

note = "Funding Information: This research was funded by the China Scholarship Council and King{\textquoteright}s College London, grant number 202106350041. Publisher Copyright: {\textcopyright} 2023 by the authors.",

year = "2023",

month = jul,

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doi = "10.3390/s23136137",

language = "English",

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AU - Chen, Xin

AU - Kamavuako, Ernest

PY - 2023/7/4

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N2 - Food and fluid intake monitoring are essential for reducing the risk of dehydration, malnutrition, and obesity. The existing research has been preponderantly focused on dietary monitoring, while fluid intake monitoring, on the other hand, is often neglected. Food and fluid intake monitoring can be based on wearable sensors, environmental sensors, smart containers, and the collaborative use of multiple sensors. Vision-based intake monitoring methods have been widely exploited with the development of visual devices and computer vision algorithms. Vision-based methods provide non-intrusive solutions for monitoring. They have shown promising performance in food/beverage recognition and segmentation, human intake action detection and classification, and food volume/fluid amount estimation. However, occlusion, privacy, computational efficiency, and practicality pose significant challenges. This paper reviews the existing work (253 articles) on vision-based intake (food and fluid) monitoring methods to assess the size and scope of the available literature and identify the current challenges and research gaps. This paper uses tables and graphs to depict the patterns of device selection, viewing angle, tasks, algorithms, experimental settings, and performance of the existing monitoring systems.

AB - Food and fluid intake monitoring are essential for reducing the risk of dehydration, malnutrition, and obesity. The existing research has been preponderantly focused on dietary monitoring, while fluid intake monitoring, on the other hand, is often neglected. Food and fluid intake monitoring can be based on wearable sensors, environmental sensors, smart containers, and the collaborative use of multiple sensors. Vision-based intake monitoring methods have been widely exploited with the development of visual devices and computer vision algorithms. Vision-based methods provide non-intrusive solutions for monitoring. They have shown promising performance in food/beverage recognition and segmentation, human intake action detection and classification, and food volume/fluid amount estimation. However, occlusion, privacy, computational efficiency, and practicality pose significant challenges. This paper reviews the existing work (253 articles) on vision-based intake (food and fluid) monitoring methods to assess the size and scope of the available literature and identify the current challenges and research gaps. This paper uses tables and graphs to depict the patterns of device selection, viewing angle, tasks, algorithms, experimental settings, and performance of the existing monitoring systems.

KW - intake monitoring

KW - drinking action detection

KW - dietary monitoring

KW - vision-based methods

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DO - 10.3390/s23136137

M3 - Review article

SN - 1424-8220

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JO - SENSORS

JF - SENSORS

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M1 - 6137

ER -

Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review

Abstract

Keywords

UN SDGs

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