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The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App.

Maria F VasiloglouKlazine van der HorstThomai StathopoulouMichael P JaeggiGiulia S TeddeYa LuStavroula G Mougiakakou
Published in: JMIR mHealth and uHealth (2021)
No other study has focused on the principal problems in the use of automatic apps for assessing nutritional intake. This study shows that it is important to provide study participants with detailed instructions if high-quality data are to be obtained. Future developments could focus on making it easier to recognize food on various plates from its color or shape and on exploring alternatives to using fiducial markers. It is also essential for future studies to understand the training needed by the participants as well as to enhance the app's user-friendliness and to develop automatic image checks based on participant feedback.
Keyphrases
  • deep learning
  • machine learning
  • endothelial cells
  • risk assessment
  • current status
  • climate change
  • weight loss
  • big data