Human-Centric Security Design for Tracking Dietary Intake

The research study centers on the application and importance of a human-centric design for monitoring a person’s daily dietary intake patterns to maintain health at their fingertips. We humans have a concept implanted in our brains that just wants to witness technological advancements in any domain but in this marathon of development, we have forgotten the security failures due to the human mind’s negligence at the point of operation. We have implemented this idea in user-based applications and cloud deployment for data storage to make it not only user-friendly but ‘human-based’. The image detected through the application is processed using the YOLO model to derive all the nutritional information which is then stored in a cloud where data is encrypted and stored safely using the AES algorithm. The model has been trained by borrowing the Food 101 dataset from Kaggle. The AES algorithm is also used by our application for the same purpose. Based upon the biodata fed into the cloud by a user-filled survey form, the impact on health is determined by the consumption of a food item fed via its image and a message is prompted on the application accordingly. The YOLO model provides us an accuracy of 92.87% and the AES algorithm comes with the best category fulfillments which in total makes a complete architecture of a human-centered security design for tracking human dietary intake.