Alz-Aware

Inspiration

We have watched numerous people in the real work being impacted by Alzheimer’s hitting them so suddenly. That is why we decided to do research on if there are factors that can be used to take preventative care earlier, in order to put an end to the sudden spread of Alzheimer's disease!

What it does

This takes the user's audio recording and converts their recording to transcribed text. The transcribed text is then compared with the original text to detect inconsistencies in speech patterns, such as missing words, repeated words, and etc. These metrics are then plugged into a logistics-regressions-like model to determine a percentage for Alzheimer's likelihood!

How we built it

We built this using technologies like Python, Flask, Flask API, JavaScript, CSS, HTML, etc. VS Code allowed us to keep all of our components organized!

Challenges we ran into

We really struggled with connecting the frontend and backend together (we had them as two separate components), but Python's Flask API was extremely useful in app routing and combining all our individual, different components.

Accomplishments that we're proud of

We're really proud of the front-end, because it took us lots of time! We made sure to make it digitally accessible and user-friendly for elderly people (with bright colors, simple application flow, and simple navigation texts).

What we learned

We learned how to work with new API's like Google's speech-to-text API; it was very cool! It was a very enlightening experience to work with audio-related technologies and to learn how to use them; it is the first time either of us has worked with real-time audio data/files.

What's next for Alz-Aware

We hope to scale our product and deploy it so that millions of people can use this product and hopefully take first-step precautions against Alzheimer's disease in a simple way.