Table A1.
Systematic review papers that were evaluated and used to define the scope of this study (see Section 1). Listed for all systematic reviews are the year of publication, the focus of the review, the years of inclusion, the number of papers included and the specific requirements for inclusion. Text in italic represents literal quotes.
| Reference | Year of Publication | Focus of Review | Years of Inclusion | Number of Papers Included | Specific Requirements for Inclusion |
|---|---|---|---|---|---|
| [143] | 2020 | In this review paper [they] provide an overview about automatic food intake monitoring, by focusing on technical aspects and Computer Vision works which solve the main involved tasks (i.e., classification, recognitions, segmentation, etc.). | 2010–2020 | 23 papers that present systems for automatic food intake monitoring + 46 papers that address Computer Vision tasks related to food images analysis | Method should apply computer vision techniques. |
| [138] | 2020 | [This] scoping review was conducted in order to: 1. catalog the current use of wearable devices and sensors that automatically detect eating activity (dietary intake and/or eating behavior) specifically in free-living research settings; 2. and identify the sample size, sensor types, ground-truth measures, eating outcomes, and evaluation metrics used to evaluate these sensors. |
Prior to 22 December 2019 | 33 |
I—description of any wearable device or sensor (i.e., worn on the body) that was used
to automatically (i.e., no required actions by the user) detect any form of eating (e.g., content of food consumed, quantity of food consumed, eating event, etc.). Proxies for “eating” measures, such as glucose levels or energy expenditure, were not included. II—“In-field” (non-lab) testing of the sensor(s), in which eating and activities were performed at-will with no restrictions (i.e., what, where, with whom, when, and how the user ate could not be restricted). III—At least one evaluation metric (e.g., Accuracy, Sensitivity, Precision, F1-score) that indicated the performance of the sensor on detecting its respective form of eating. |
| [144] | 2019 | The goal of this review was to identify unique technology-based tools for dietary intake assessment, including smartphone applications, those that captured digital images of foods and beverages for the purpose of dietary intake assessment, and dietary assessment tools available from the Web or that were accessed from a personal computer (PC). | January 2011–September 2017 | 43 |
(1) publications were in English,
(2) articles were published from January 2011 to September 2017, and (3) sufficient information was available to evaluate tool features, functions, and uses. |
| [145] | 2017 | This article reviews the most relevant and recent researches on automatic diet monitoring, discussing their strengths and weaknesses. In particular, the article reviews two approaches to this problem, accounting for most of the work in the area. The first approach is based on image analysis and aims at extracting information about food content automatically from food images. The second one relies on wearable sensors and has the detection of eating behaviours as its main goal. | Not specified | Not specified | n/a |
| [146] | 2019 | The aim of this review is to synthesise research to date that utilises upper limb motion tracking sensors, either individually or in combination with other technologies (e.g., cameras, microphones), to objectively assess eating behaviour. | 2005–2018 | 69 |
(1) used at least one wearable motion sensor,
(2) that was mounted to the wrist, lower arm, or upper arm (referred to as the upper limb in this review), (3) for eating behaviour assessment or human activity detection, where one of the classified activities is eating or drinking. We explicitly also included studies that additionally employed other sensors on other parts of the body (e.g., cameras, microphones, scales). |
| [147] | 2022 | This paper consists of a systematic review of sensors and machine learning approaches for detecting food intake episodes. […] The main questions of this systematic review were as follows: (RQ1) What sensors can be used to access food intake moments effectively? (RQ2) What can be done to integrate such sensors into daily lives seamlessly? (RQ3) What processing must be done to achieve good accuracy? | 2010–2021 | 30 |
(1) research work that performs food intake detection;
(2) research work that uses sensors to detect food with the help of sensors; (3) research work that presents some processing of food detection to propose diet; (4) research work that use wearable biosensors to detect food intake; (5) research work that use the methodology of deep learning, Support Vector Machines or Convolutional Neural Networks related to food intake; (6) research work that is not directly related to image processing techniques; (7) research work that is original; (8) papers published between 2010 and 2021; and (9) papers written in English |
| [148] | 2021 |
This article presents a comprehensive review of the use of sensor methodologies for portion size estimation. […] Three research questions were chosen to guide this systematic review:
RQ1) What are the available state-of-the-art SB-FPSE methodologies? […] RQ2) What methods are employed for portion size estimation from sensor data and how accurate are these methods? […] RQ3) Which sensor modalities are more suitable for use in the free-living conditions? |
Since 2000 | 67 | Articles published in peer-reviewed venues; […] Papers that describe methods for estimation of portion size; FPSE methods that are either automatic or semi-automatic; written in English. |
| [135] | 2022 | [They] reviewed the current methods to automatically detect eating behavior events from video recordings. | 2010–2021 | 13 |
Original research articles […] published in the English language and containing findings on video analysis for human eating behavior from January 2010 to December 2021. […] Conference papers were included. […] Articles concerning non-human studies were excluded. We excluded research articles on eating behavior with video electroencephalogram monitoring, verbal interaction analysis, or sensors, as well
as research studies not focusing on automated measures as they are beyond the scope of video analysis. |
| [139] | 2022 | The aim of this study was to identify and collate sensor-based technologies that are feasible for dietitians to use to assist with performing dietary assessments in real-world practice settings. | 2016–2021 | 54 |
Any scientific paper published between January 2016 and December 2021 that used sensor-based devices to passively detect and record the initiation of eating in real-time. Studies were further excluded during the full text screening stage if they did not evaluate device performance or if the same research group conducted a more recent study describing a device that superseded previous studies of the same device.
Studies evaluating a device that did not have the capacity to detect and record the start time of food intake, did not use sensors, were not applicable for use in free-living settings, or were discontinued at the time of the search were also excluded. |
| [149] | 2021 | This paper reviews the most recent solutions to automatic fluid intake monitoring both commercially and in the literature. The available technologies are divided into four categories: wearables, surfaces with embedded sensors, vision- and environmental-based solutions, and smart containers. | 2010–2020 | 115 | Papers that did not study liquid intake and only studied food intake or other unrelated activities were excluded. Since this review is focused on the elderly population, in the wearable section, we only included literature that used wristbands and textile technology which could be easily worn without affecting the normal daily activity of the subjects. We have excluded devices that were not watch/band or textile based such as throat and ear microphones or ear inertial devices as they are not practical for everyday use. […] Although this review is focused on the elderly population, studies that used adult subjects were not excluded, as there are too few that only used seniors. |
| [150] | 2022 | The purpose of this review is to analyze the effectiveness of mHealth and wearable sensors to manage Alcohol Use Disorders, compared with the outcomes of the same conditions under traditional, face-to-face (in person) treatment. | 2012–2022 | 25 | Articles for analysis were published in the last 10 years in peer-reviewed academic journals, and published in the English language. They must include participants who are adults (18 years of age or older). Preferred methods were true experiments (RCT, etc.), but quasi-experimental, non-experimental, and qualitative studies were also accepted. Other systematic reviews were not accepted so as not to confound the results. Works that did not mention wearable sensors or mHealth to treat AUD were excluded. Studies with participants under age 18 were excluded. Studies that did not report results were excluded. |
| [151] | 2023 | This paper reviews the existing work […] 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. | Not specified | 253 |
(1) at least one kind of vision-based
technology (e.g., RGB-D camera or wearable camera) was used in the paper; (2) eating or drinking activities or both identified in the paper; (3) the paper used human participants data; (4) at least one of the evaluation criteria (e.g., F1-score) was used for assessing the performance of the design |