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A Deep Learning-Based Rotten Food Recognition App for Older Adults: Development and Usability Study.

Minki Chun1, Ha-Jin Yu1,2, Hyunggu Jung1,2

  • 1Department of Computer Science and Engineering, University of Seoul, Seoul, Republic of Korea.

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|July 3, 2024
PubMed
Summary

This study developed a smartphone app to help older adults detect rotten fruit, finding it easy to use and visually satisfactory. Further development is needed to identify more food types.

Keywords:
acceptanceadoptionagedageingagingappapplicationapplicationsappsartificial intelligenceattitudeattitudescameraclassificationdeep learningdesigndevelopdevelopmentdigital healthdigital sensorelderelderlyexperienceexperiencesfoodfoodsfruitfruitsgeriatricgeriatricsgerontologyimageimagingmHealthmachine learningmobile healtholder adultolder adultsolder peopleolder personopinionopinionsperceptionperceptionsperspectiveperspectivesphotophotographphotographsphotospicturepicturesrecognitionsensorsensorssmartphonesmartphonesusability

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Area of Science:

  • Gerontology
  • Computer Science
  • Food Science

Background:

  • Cognitive decline in older adults increases the risk of food poisoning from consuming rotten produce.
  • Existing tools for detecting rotten food lack specific applications for older adults.
  • A need exists for user-friendly technology to support older adults in identifying unsafe food items.

Purpose of the Study:

  • To develop a smartphone application for older adults to identify rotten fruits via image classification.
  • To assess the usability and user perceptions of the developed food freshness detection app among older adults.

Main Methods:

  • A smartphone app was created utilizing residual deep networks for fruit freshness analysis.
  • Healthy older adults (aged 65+) participated in usability testing and provided feedback through surveys and interviews.
  • App performance was evaluated using after-scenario questionnaires and qualitative interview data.

Main Results:

  • Participants found the app simple, easy to use, and visually appealing for determining fruit freshness.
  • Older adults were satisfied with the app's efficiency in identifying fresh fruit but hesitant towards a paid version.
  • Qualitative feedback indicated no difficulties in capturing fruit images with the app.

Conclusions:

  • The developed app demonstrates potential for effectively assisting older adults in identifying rotten food.
  • Future research should expand the app's capability to detect the freshness of a wider variety of food items.