As a team, we enjoyed the focus on ethics this week. We enjoyed the pod discussions and began to consider things that we would have never thought of. A big issue we identified with our product involved both body image concerns as well as privacy issues. We want our product to promote a healthy lifestyle, but we do not want our users to develop eating disorders and other personal issues. Likewise, we do not want private data on the consumption habits of our users to be leaked to other users as well as companies who participate in targeted advertising. As a team, we were able to discuss fixes to these issues including database security design and user-friendly/encouraging dialogue on the front end. Despite being done with discussions on ethics, we plan to keep all these principles in mind as we further our design.
We found it interesting in the pod discussion that two other groups had a product of similar functionality to ours. While one group anticipated projecting ingredients and recipes onto a tabletop using a projector at a calculated angle, the other is using AI to generate recipes on a phone app from ingredients added to their food inventory. We were able to clearly address that the MVP for our system would only be able to classify canned foods and certain types of fruits as opposed to cooked foods or ingredients in a bowl. The biggest difference between our product and these is that ours is used as a calorie tracking device and more focused on physical wellness, so more ethical concerns arise with this focal point. This will definitely be a greater consideration of ours while working on user interface integration and user experience with our web application.
The parts for scale integration have steadily come in through the ECE delivery center, so Surya began work on understanding the hardware layout of the scale and assessing the 2 main approaches to writing scale measurements to the database. He is waiting on the RPIs to assess the camera approach to read scale values. Another option could be to start on this with the Arduino chip that came in the mail already, but typically, Arduinos are not great choices for image processing because of limited on-board RAM and limited functionality with other cameras (RPIs are fantastic for such applications and several resources exist online for subsequent support). Additionally, he plans on working with Steven and Grace in sculpting a presentation strategy for the rapidly approaching interim demo.
In the meantime, he has also learned more about how the load cell amplifier works and the wiring topology. An important thing to do when working with a functional scale is to ensure that the correct wires are snipped and soldered; a wiring schematic can be found below for the reader’s convenience:
Steven did a lot of work patching up the ML infrastructure of the project. He optimized the accuracy of the various components. The first optimization was done to the classification algorithm for canned foods and fruit using the AdaBoost algorithm to combine multiple decision boundaries. The second one involved classification within the groups of fruit using k-nearest neighbors. This was combined with GoogLeNet and OpenCV to produce better results more specific to our project. Lastly, the ChatGPT API does not need optimization, but Steven worked with integration into the front-end. He plans to work alongside Grace in the upcoming weeks to test the basic functionality and syntax of the API to perform label reading and classification, if needed.