Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology

  title={Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology},
  author={Kaylen J. Pfisterer and Robert Amelard and Jennifer Boger and Audrey G. Chung and Heather H. Keller and Alexander Wong},
Half of long-term care (LTC) residents are malnourished increasing hospitalization, mortality, morbidity, with lower quality of life. Current tracking methods are subjective and time consuming. This paper presents the automated food imaging and nutrient intake tracking (AFINI-T) technology designed for LTC. We propose a novel convolutional autoencoder for food classification, trained on an augmented UNIMIB2016 dataset and tested on our simulated LTC food intake dataset (12 meal scenarios; up to… 

Figures and Tables from this paper



Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes

This system provides improved transparency, approximates human assessors with enhanced objectivity, accuracy, and precision while avoiding hefty semi-automatic method time requirements, and may help address short-comings currently limiting utility of automated early malnutrition detection in resource-constrained LTC and hospital settings.

Prototyping the Automated Food Imaging and Nutrient Intake Tracking System: Modified Participatory Iterative Design Sprint

The AFINI-T concept system appears to have good practice relevance as a tool for an intelligent food and fluid intake tracking system in LTC and gives tangible examples of how the sprint method can be adapted and applied to the development of novel needs-based application-driven technology.

Validation of a novel image-weighed technique for monitoring food intake and estimation of portion size in hospital settings: a pilot study

Considering the huge benefits associated with routine monitoring, technological advances have made it possible to develop a novel, easy-to-use DIMS that, according to the findings, is a valid alternative for use in hospital settings.

Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study

Although estimation accuracy was not affected by the viewing angle, the type of meal mattered, with slightly worse performance for cooked meals than for breakfasts and snacks, highlighting its usability.

Point2Volume: A Vision-Based Dietary Assessment Approach Using View Synthesis

Compared to previous methods, this method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation.

A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques

A novel system to automatically estimate food attributes such as ingredients and nutritional value by classifying the input image of food by employing different deep learning models for accurate food identification is proposed.

Volume estimation using food specific shape templates in mobile image-based dietary assessment

The objective of this study is to automatically estimate food volumes through the use of food specific shape templates, providing a consistent method for estimation food volume.

Image-Based Food Classification and Volume Estimation for Dietary Assessment: A Review

After a comprehensive exploration, it is found that integrated dietary assessment systems combining with different approaches could be the potential solution to tackling the challenges in accurate dietary intake assessment.

Food calorie measurement using deep learning neural network

This paper proposes an assistive calorie measurement system, which allows the user to take a picture of the food and measure the amount of calorie intake automatically and shows that the accuracy of the method for food recognition of single food portions is 99%.

Recognition and volume estimation of food intake using a mobile device

This paper combines several vision techniques (visual recognition and 3D reconstruction) to achieve quantitative food intake estimation and presents a system that improves accuracy of food intake assessment using computer vision techniques.