Project Title: Make it Count
URL of Project: https://codepad.app/edit/n409cmf9
Video: https://youtu.be/l-MLWhcYEfE
Write-Up: Pre-diabetes is a silent epidemic—according to the Centers for Disease Control and Prevention, it affects more than 1 in 3 adults in the United States. It is imperative for people in the pre-diabetic community to take care of their bodies through healthy foods and exercise, not only to combat their diagnosis but also for their overall well-being. However, many recipes for pre-diabetic-safe foods are inaccessible, difficult to search for, or lack good taste. Many might believe that delicious, pre-diabetic meals just don’t exist for people to cook at home in their own kitchens because of the stereotype that “all glucose needs to be cut out.” This is where our project comes in.
Make it Count addresses limited access to delicious meal recipes that contain low-glycemic, fiber-rich, and whole-grain foods among pre-diabetics, signifying to those with pre-diabetes that their diagnosis does not have to control their enjoyment. Our project also includes an exercise and movement portion that promotes simple, achievable, and accessible workouts. This aids pre-diabetic folks in living a physically healthy and happy life with workouts alongside tasty diets.
Our project comprises a website that gives users easy access to countless recipes that are both safe for their bodies and minds—plus, they taste really good. The user can guarantee that these recipes are a blend of ingredients that will help prevent spikes in blood sugar levels and slow down digestion. Using HTML, CSS, and JavaScript, we added a feature that allows users to scan their food and fathom how quickly that item will raise their blood sugar levels. The GI (Glycemic Index) Scanner runs on a scale of 1-100 and can be categorized into low GI (55 and below), moderate GI (56-69), and high GI (70 and above). The website also provides users an exercise journal with points, featuring multiple different types of exercises—walking (+10XP; +5 happiness), yoga (+15XP; +5 happiness), run (+20XP; +8 happiness), workout (+25XP; +3 happiness)—alongside a workout pet to augment motivation. The exercise section remains interactive through “pet levels,” and users are able to catalog exercises in a journal that can be viewed at any time. This section includes two mental health activities as well—play time (+10 happiness) and rest time (+15 happiness)—for balance is as important as working hard.
While collaborating on this project, we learned about the difficulties pre-diabetics face while searching for easy-to-make, appetizing food—innumerable recipes either offered little nutritional value or appeared unappetizing. Hence, we needed to conduct extensive research on the factors differentiating diabetic-friendly and non-diabetic-friendly recipes. Furthermore, coding itself called for persistence, collaboration, and creativity. In retrospect, we had to learn to adapt because coding takes incredible trial and error. If anything is disorganized, it is incredibly difficult to find errors and understand the code when revising. One of the biggest challenges we faced was working with new coding languages. Those of us with less coding experience had trouble with text and graphics—font, size, positioning, and more. For instance, programming the pet faces was extremely challenging, for we implemented only CSS (not pictures) to draw them. It took a great amount of time to properly align the eyes, ears, and noses and design facial expressions to symbolize happiness shifts. Dozens of other considerations—XP gains, journal entries, saved progress—furthered the project’s difficulty, demanding that we keep track of dozens of tasks at once. We scanned thousands of lines of code to adjust small buttons and animations, requiring countless hours of work.
Still, we relied on one another, allocating tasks based on our diverse skillset, to figure out the designs. To get through our gap in knowledge, we heavily relied on learning through YouTube and learned a valuable lesson: anything is possible if you watch a YouTube tutorial.
Our project shed light on the many ways we can help those who do not receive enough support, including the pre-diabetic community. Creating a functional website to help others was more than enough of a reward, but immensely improved coding skills were a plus. Coding was frustrating at times, but witnessing a pet blink or webcam function made us really proud.
To create the food scanner, we utilized Teachable Machine AI. Teachable Machine is a free, web-based Google tool with which users can train a machine learning model to recognize images, sounds, or poses. Using hundreds of images of dozens of fruits, vegetables, grains, and other typical household ingredients, we trained an AI model to determine the GI of food objects, encouraging users to select diabetic-friendly ingredients when cooking.
