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Pfisterer K, Amelard R, Boger J, Keller H, Chung A, Wong A
Enhancing Food Intake Tracking in Long-term Care With Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology: Validation and Feasibility Assessment
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology
Kaylen Pfisterer;
Robert Amelard;
Jennifer Boger;
Heather Keller;
Audrey Chung;
Alexander Wong
ABSTRACT
Background:
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.
Objective:
This paper presents the automated food imaging and nutrient intake tracking (AFINI-T) technology designed for LTC.
Methods:
We propose a novel convolutional autoencoder for food classification, trained on an augmented UNIMIB2016 dataset and tested on our simulated LTC food intake dataset.
Results:
On 12 meal scenarios with up to 15 classes each, the top-1 classification accuracy was 88.9% with a mean intake error of -0.4 mL±36.7 mL. Nutrient intake estimation by volume was strongly linearly correlated with nutrient estimates from mass (r2 0.92 to 0.99) with good agreement between methods (σ= −2.7 to −0.01; zero within each of the limits of agreement).
Conclusions:
The AFINI-T approach is a deep-learning powered computational nutrient sensing system that may provide a novel means for more accurately and objectively tracking LTC resident food intake to support and prevent malnutrition tracking strategies.
Citation
Please cite as:
Pfisterer K, Amelard R, Boger J, Keller H, Chung A, Wong A
Enhancing Food Intake Tracking in Long-term Care With Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology: Validation and Feasibility Assessment
