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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

Aim/outline

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.

URLs/references

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)