Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review

Amugongo, Lameck Mbangula; Kriebitz, Alexander; Boch, Auxane; Lütge, Christoph

doi:10.3390/healthcare11010059

Cited by 24 publications

(11 citation statements)

References 50 publications

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“…Recent advancements in computer vision and deep learning show potential for replacing traditional input methods [27,28]. This study integrates automatic image recognition technology [29][30][31] into an mHealth app which originally used voice-based inputs, seeking to improve accuracy and time efficiency in meal reporting.…”

Section: Challenges In Dietary Intake Inputmentioning

confidence: 99%

“…While automatic image recognition is increasingly integrated into mobile meal reporting [28,38,39], few comparative studies have examined how this technology creates additional value for existing apps. The results of the present study indicate that combining automatic image and voice recognition is not only feasible but also provides improvements over voice-only versions in terms of accuracy and time-efficiency.…”

Section: Principal Findingsmentioning

confidence: 99%

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Automatic Image Recognition Meal Reporting among Young Adults: A Randomized Controlled Trial (Preprint)

Sahoo,

Chiu,

Lin

et al. 2024

Preprint

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BACKGROUND Advances in artificial intelligence (AI) technology have raised new possibilities for the effective evaluation of daily dietary intake, but more empirical study is needed for the use of such technologies under realistic meal scenarios. This study developed an automated food recognition technology, which was then integrated into its previous design to improve usability for meal reporting. The newly developed app allowed for the automatic detection and recognition of multiple dishes within a single real-time food image as input. Application performance was tested using young adults in authentic dining conditions. OBJECTIVE A two-group comparative study was conducted to assess app performance using metrics including accuracy, efficiency, and user perception. The experimental group, named Automatic Image-based Reporting (AIR) group, was compared against a control group using the previous version, named the Voice Input Reporting (VIR) group. Each application is primarily designed to facilitate a distinct method to food intake reporting. AIR users capture and upload images of their selected dishes, supplemented with voice commands where appropriate. VIR users supplement the uploaded image with verbal inputs for food names and attributes. METHODS The two mobile apps were subjected to a head-to-head parallel randomized evaluation. A cohort of 42 young adults aged 20-25 years (9 male and 34 female) was recruited from a university in Taiwan and randomly assigned to two groups, i.e., AIR (n=22) and VIR (n=20). Both groups were assessed using the same menu of seventeen dishes. Each meal was designed to represent a typical lunch or dinner setting, with one stable, one main course, and three side dishes. All participants used the app on the same type of smartphone, with the interfaces of both using uniform user interactions, icons, and layouts. Analysis of the gathered data focused on assessing reporting accuracy, time efficiency, and user perception. RESULTS For the AIR group, 86% dishes were correctly identified, whereas 68% dishes were accurately reported. The AIR group exhibited a significantly higher degree of identification accuracy compared to the VIR group (p<.001). The AIR group also required significantly less time to complete food reporting (p<.001). SUS scores showed both apps were perceived as having high usability and learnability (P=.20). CONCLUSIONS The AIR group outperformed the VIR group in terms of accuracy and time efficiency for overall dish reporting within the meal testing scenario. While further technological enhancement may be required, the integration of AI vision technology into existing mobile applications holds promise. Our results provide an evidence-based contributing for the integration of automatic image recognition technology into existing apps in terms of user interaction efficacy and overall ease of use. Further empirical work is required including full-scale randomized controlled trials and assessments of user perception under a range of dining conditions. CLINICALTRIAL International Standard Randomized Trial Registry ISRCTN27511195; https://doi.org/10.1186/ISRCTN27511195

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Section: Challenges In Dietary Intake Inputmentioning

confidence: 99%

Section: Principal Findingsmentioning

confidence: 99%

Automatic Image Recognition Meal Reporting among Young Adults: A Randomized Controlled Trial (Preprint)

Sahoo,

Chiu,

Lin

et al. 2024

Preprint

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“…This will improve the process of matching donors with recipient organisations and individuals. Moreover, computer vision can assist in recognizing food, establishing quantities, and accurately sensing and measuring key parameters that determine the safety of the food donated as well as its quality [115]. For example, computer vision technology can identify perishable items that need to be distributed quickly and identify food items that food banks commonly request.…”

Section: Ai-powered Food Redistribution Systemsmentioning

confidence: 99%

Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

Onyeaka,

Tamasiga,

Nwauzoma

et al. 2023

Sustainability

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Food waste is a global issue with significant economic, social, and environmental impacts. Addressing this problem requires a multifaceted approach; one promising avenue is using artificial intelligence (AI) technologies. This article explores the potential for AI to tackle food waste and enhance the circular economy and discusses the current state of food waste and the circular economy, highlighting specific ways that AI can be used to monitor and optimise food production and supply chains, redistribute excess food to those in need, and support circular economy initiatives. As a result, we can maximise resource efficiency and minimise environmental impact with these applications, ultimately creating a more sustainable and equitable food system.

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“…On the other hand, image-based approaches rely primarily on captured images as the main source of information on dietary intake ( 17 ). Smartphone apps are used in image-based approaches for food recognition and volume and energy estimation, simplifying the recording of dietary intake for both researchers and study participants ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, several apps for image-based dietary assessment are available ( 18 ), and their use has become feasible due to the widespread ownership of smartphones. A novel image-based food-recognition app, SNAQ, utilizes depth-sensing hardware and computer vision to quantify the macronutrient content and quantity of photographed dietary items within the app ( 20 ).…”

Section: Introductionmentioning

confidence: 99%

Assessing daily energy intake in adult women: validity of a food-recognition mobile application compared to doubly labelled water

Serra,

Alceste,

Hauser

et al. 2023

Front. Nutr.

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Accurate dietary assessment is crucial for nutrition and health research. Traditional methods, such as food records, food frequency questionnaires, and 24-hour dietary recalls (24HR), have limitations, such as the need for trained interviewers, time-consuming procedures, and inaccuracies in estimations. Novel technologies, such as image-based dietary assessment apps, have been developed to overcome these limitations. SNAQ is a novel image-based food-recognition app which, based on computer vision, assesses food type and volume, and provides nutritional information about dietary intake. This cross-sectional observational study aimed to investigate the validity of SNAQ as a dietary assessment tool for measuring energy and macronutrient intake in adult women with normal body weight (n = 30), compared to doubly labeled water (DLW), a reference method for total daily energy expenditure (TDEE). Energy intake was also estimated using a one-day 24HR for direct comparison. Bland–Altman plots, paired difference tests, and Pearson’s correlation coefficient were used to assess agreement and relationships between the methods. SNAQ showed a slightly higher agreement (bias = −329.6 kcal/day) with DLW for total daily energy intake (TDEI) compared to 24HR (bias = −543.0 kcal/day). While both SNAQ and 24HR tended to underestimate TDEI, only 24HR significantly differed from DLW in this regard (p < 0.001). There was no significant relationship between estimated TDEI and TDEE using SNAQ (R2 = 27%, p = 0.50) or 24HR (R2 = 34%, p = 0.20) and there were no significant differences in energy and macronutrient intake estimates between SNAQ and 24HR (Δ = 213.4 kcal/day). In conclusion, these results indicate that SNAQ provides a closer representation of energy intake in adult women with normal body weight than 24HR when compared to DLW, but no relationship was found between the energy estimates of DLW and of the two dietary assessment tools. Further research is needed to determine the clinical relevance and support the implementation of SNAQ in research and clinical settings.Clinical trial registration: This study is registered on ClinicalTrials.gov with the unique identifier NCT04600596 (https://clinicaltrials.gov/ct2/show/NCT04600596).

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Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review

Cited by 24 publications

References 50 publications

Automatic Image Recognition Meal Reporting among Young Adults: A Randomized Controlled Trial (Preprint)

Automatic Image Recognition Meal Reporting among Young Adults: A Randomized Controlled Trial (Preprint)

Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

Assessing daily energy intake in adult women: validity of a food-recognition mobile application compared to doubly labelled water

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