## Inspiration

"Every day in Canada, more than 350 people develop dementia"

* Alzheimer Society of Canada
Dementia and memory loss is a worldwide problem and we witnessed the challenges faced by dementia patients and their caregivers on a daily basis. This drove us to create a solution that could enhance the quality of life for dementia patients while providing much-needed support to their caregivers.

## What it does

NURO is a comprehensive wearable assistant designed specifically for individuals living with dementia. It serves multiple functions to address various aspects of their daily life challenges. Firstly, NURO provides personalized reminders for medications, appointments, and daily routines, helping to mitigate memory lapses and maintain a sense of normalcy. Secondly, it offers real-time location tracking with geo-fencing capabilities to prevent dangerous wandering behaviors, ensuring the safety of the wearer. Additionally, NURO incorporates adaptive learning technology, which analyzes the wearer's behaviors, routines, and preferences to tailor its reminders and interactions over time, making it more effective and personalized. Last but definitely not the least, if the user forgets something, the AI voice will answer and tell the user the answer.

## How we built it

NURO was built using a combination of hardware and software components. For the hardware, we utilized a Raspberry Pi as the central processing unit, which was made wearable through a clip-on design. Additionally, we integrated a microphone for voice input and wireless speakers for audible reminders and communication. On the software side, we developed a frontend interface that allows users to manage reminders, appointments, and other settings. We also implemented algorithms for real-time location tracking, adaptive learning, and data analysis.

## Challenges we ran into

We ran into a few challenges during the development of this project. The first one being live speech diarization. Current online integrations do not support live speaker diarization, so we had to implement a pipeline with the help of FFMPEG.

## Accomplishments that we're proud of

We're very proud of our accomplishments such as integrating the Solace Event Broker into our application. Furthermore, we're proud of integrating hardware components within our application such as the Raspberry Pi and a microphone. We're very proud of speech-to-text conversion which was able to recognize different voices ( speech diarisation).

## What we learned

We learned about the process of multithreading in a Python script. In terms of hardware, we learned how to configure a Raspberry Pi and integrate this computer with other components. Additionally, we became proficient in using LLM models to manipulate our data and learned about the usage of a VectorDB database. Our team also learned about the best practices to integrate the Solace Event Broker into our application.

## What's next for Nuro

In the future, we could try to implement visual feed along with the auditory feed. This visual feed can be very useful when it comes to remembering where you put things. A lot of dementia patients forget where they last placed their object such as their keys. This visual feed can capture where the items were last placed and remind the patient where their item is. This visual feed can just be a camera in the patient's glasses.
## Inspiration

Social-distancing is hard, but little things always add up.

What if person X is standing too close to person Y in the c-mart, and then person Y ends up in the hospital for more than a month battling for their lives? Not finished, that c-mart gets shut down for contaminated merchandise.

All this happened because person X didn't step back.

These types of scenarios, and in hope of going back to normal lives, pushed me to create **Calluna**.

## What Calluna does

Calluna is aimed to be an apple watch application. On the application, you can check out all the notifications you've gotten that day as well as when you've got it and your settings.

When not on the app, you get pinged when your too close to someone who also has the app, making this a great feature for business workforces.

## How Calluna was built

Calluna was very simply built using Figma. I have linked below both design and a fully-fuctionally prototype!

## Challenges we ran into

I had some issues with ideation. I needed something that was useful, simple, and has growth potential. I also had some headaches on the first night that could possibly be due to sleep deprivation and too much coffee that ended up making me sleep till the next morning.

## Accomplishments that we're proud of

I love the design! I feel like this is a project that will be really helpful *especially* during the COVID-19 pandemic.

## What we learned

I learned how to incorporate fonts to accent the color and scene, as well as working with such small frames and how to make it look easy on the eyes!

## What's next for Calluna

I hope to create and publish the ios app with GPS integration, then possibly android too.
## Inspiration

Our inspiration was the inability of some dementia patients to even recognize their own family members. Izzie's grandpa had dementia, and he would at time mistake his wife for his sister, or forget his grandchildren. With that in mind, we wanted to create a device that would remind a dementia patient of who the person is that they are talking to.

## What it does

There are two code versions on the repository. One is a computer version that listens to 7 seconds of audio, where a person would say their name. It then converts that speech to text using rev.ai, and pulls up the person's name, their relationship with the dementia patient, and a picture of them together to jog the memory and recall previous emotions. The second version is for the hardware. It has most of the same functionality, but the speech input was not completed, so it currently runs using preinstalled sound files.

## How we built it

We programmed the raspberry pi with python via SSH connection. We also connected an arduino for the purpose of analog-to-digital conversion as we attempted to add the sound input to our hardware.

## Challenges we ran into

We ran into a lot of challenges with installing the necessary python packages, especially with numpy. Configuration issues with the raspberry pi we were using meant we spent hours trying to uninstall and reinstall packages instead of moving forward with our code.

## Accomplishments that we're proud of

We are proud of having a finished product as this was each member's first time at a hackathon.

## What we learned

We learned how to use APIs and extended our knowledge of linux and command prompt.

## What's next for María

The next step would to get the data from the onboard speaker transporting correctly to the raspberry pi. More importantly, however, we would need to create an easy way for users to upload text with information about the person in question and relevant photos to the program. In addition, we’d like to use a feature similar to “hey Siri” or “Alexa” works in that the device listens for someone’s name after you say “my name is” and pulls up the corresponding information to the screen.