Enhancing Food Intake Tracking in Long-term Care With Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology: Validation and Feasibility Assessment

• Published in: JMIR aging Vol. 5; no. 4; p. e37590
• Main Authors: Pfisterer, Kaylen, Amelard, Robert, Boger, Jennifer, Keller, Heather, Chung, Audrey, Wong, Alexander
• Format: Journal Article
• Language: English
• Published: Toronto JMIR Publications 17.11.2022
• Subjects: Aging; Automation; Classification; Datasets; Dietary minerals; Ethics; Industrial research; Long term health care; Malnutrition; Nutrition research; Nutritional status; Older people; Original Paper; Recipes; Workshops; United States > US
• ISSN: 2561-7605; 2561-7605
• DOI: 10.2196/37590

Background Half of long-term care (LTC) residents are malnourished, leading to increased hospitalization, mortality, and morbidity, with low quality of life. Current tracking methods are subjective and time-consuming. Objective This paper presented the automated food imaging and nutrient intake tracking technology designed for LTC. Methods A needs assessment was conducted with 21 participating staff across 12 LTC and retirement homes. We created 2 simulated LTC intake data sets comprising modified (664/1039, 63.91% plates) and regular (375/1039, 36.09% plates) texture foods. Overhead red-green-blue-depth images of plated foods were acquired, and foods were segmented using a pretrained food segmentation network. We trained a novel convolutional autoencoder food feature extractor network using an augmented UNIMIB2016 food data set. A meal-specific food classifier was appended to the feature extractor and tested on our simulated LTC food intake data sets. Food intake (percentage) was estimated as the differential volume between classified full portion and leftover plates. Results The needs assessment yielded 13 nutrients of interest, requirement for objectivity and repeatability, and account for real-world environmental constraints. For 12 meal scenarios with up to 15 classes each, the top-1 classification accuracy was 88.9%, with mean intake error of −0.4 (SD 36.7) mL. Nutrient intake estimation by volume was strongly linearly correlated with nutrient estimates from mass (r2=0.92-0.99), with good agreement between methods (σ=−2.7 to −0.01; 0 within each of the limits of agreement). Conclusions The automated food imaging and nutrient intake tracking approach is a deep learning–powered computational nutrient sensing system that appears to be feasible (validated accuracy against gold-standard weighed food method, positive end user engagement) and may provide a novel means for more accurate and objective tracking of LTC residents’ food intake to support and prevent malnutrition tracking strategies.