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Recording and Tracking Food Intake in diabetes self-management apps

Primary supervisor

Pari Delir Haghighi

Poor food choices could highly contribute to the development of the current epidemic of diabetes. The consumption of foods that result in a large increase in blood glucose after consumption does lead to a progressive decline in beta cell function as well as contributing to obesity and insulin resistance leading to type 2 diabetes. Mobile apps could provide useful features for automatically identify, recording and monitoring of food intake, using food item detection methods. 


Student cohort

Single Semester
Double Semester


This study aims to investigate the current literature on mobile food item detection approaches and provide a comparative evaluation of existing models and their feasibility for integration into diabetes self-management apps. It will also aim to improve the results by considering additional contextual information.


Potter E, Burstein F, Flynn D, Hwang ID, Dinh T, Goh TY, Mohammad Ebrahim M, Gilfillan C Physician-Authored Feedback in a Type 2 Diabetes Self-management App: Acceptability Study, JMIR Form Res 2022;6(5):e31736

Required knowledge

  • Mobile app development
  • Deep learning and object detection models (knowledge of Tensorflow and Python)