TY  - JOUR
AU  - Chun, Minki
AU  - Yu, Ha-Jin
AU  - Jung, Hyunggu
PY  - 2024
DA  - 2024/7/3
TI  - A Deep Learning–Based Rotten Food Recognition App for Older Adults: Development and Usability Study
JO  - JMIR Form Res
SP  - e55342
VL  - 8
KW  - digital health
KW  - mobile health
KW  - mHealth
KW  - app
KW  - apps
KW  - application
KW  - applications
KW  - smartphone
KW  - smartphones
KW  - classification
KW  - digital sensor
KW  - deep learning
KW  - artificial intelligence
KW  - machine learning
KW  - food
KW  - foods
KW  - fruit
KW  - fruits
KW  - experience
KW  - experiences
KW  - attitude
KW  - attitudes
KW  - opinion
KW  - opinions
KW  - perception
KW  - perceptions
KW  - perspective
KW  - perspectives
KW  - acceptance
KW  - adoption
KW  - usability
KW  - gerontology
KW  - geriatric
KW  - geriatrics
KW  - older adult
KW  - older adults
KW  - elder
KW  - elderly
KW  - older person
KW  - older people
KW  - ageing
KW  - aging
KW  - aged
KW  - camera
KW  - image
KW  - imaging
KW  - photo
KW  - photos
KW  - photograph
KW  - photographs
KW  - recognition
KW  - picture
KW  - pictures
KW  - sensor
KW  - sensors
KW  - develop
KW  - development
KW  - design
AB  - Background: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items. Objective: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app. Methods: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants’ perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses. Results: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants’ perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory. Conclusions: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e55342
UR  - https://doi.org/10.2196/55342
DO  - 10.2196/55342
ID  - info:doi/10.2196/55342
ER  -