## 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

There are approximately **10 million** Americans who suffer from visual impairment, and over **5 million Americans** suffer from Alzheimer's dementia. This weekend our team decided to help those who were not as fortunate. We wanted to utilize technology to create a positive impact on their quality of life.

## What it does

We utilized a smartphone camera to analyze the surrounding and warn visually impaired people about obstacles that were in their way. Additionally, we took it a step further and used the **Azure Face API** to detect the faces of people that the user interacted with and we stored their name and facial attributes that can be recalled later. An Alzheimer's patient can utilize the face recognition software to remind the patient of who the person is, and when they last saw him.

## How we built it

We built our app around **Azure's APIs**, we created a **Custom Vision** network that identified different objects and learned from the data that we collected from the hacking space. The UI of the iOS app was created to be simple and useful for the visually impaired, so that they could operate it without having to look at it.

## Challenges we ran into

Through the process of coding and developing our idea we ran into several technical difficulties. Our first challenge was to design a simple UI, so that the visually impaired people could effectively use it without getting confused. The next challenge we ran into was attempting to grab visual feed from the camera, and running them fast enough through the Azure services to get a quick response. Another challenging task that we had to accomplish was to create and train our own neural network with relevant data.

## Accomplishments that we're proud of

We are proud of several accomplishments throughout our app. First, we are especially proud of setting up a clean UI with two gestures, and voice control with speech recognition for the visually impaired. Additionally, we are proud of having set up our own neural network, that was capable of identifying faces and objects.

## What we learned

We learned how to implement **Azure Custom Vision and Azure Face APIs** into **iOS**, and we learned how to use a live camera feed to grab frames and analyze them. Additionally, not all of us had worked with a neural network before, making it interesting for the rest of us to learn about neural networking.

## What's next for BlindSpot

In the future, we want to make the app hands-free for the visually impaired, by developing it for headsets like the Microsoft HoloLens, Google Glass, or any other wearable camera device.
## Inspiration

We realized how visually-impaired people find it difficult to perceive the objects coming near to them, or when they are either out on road, or when they are inside a building. They encounter potholes and stairs and things get really hard for them. We decided to tackle the issue of accessibility to support the Government of Canada's initiative to make work and public places completely accessible!

## What it does

This is an IoT device which is designed to be something wearable or that can be attached to any visual aid being used. What it does is that it uses depth perception to perform obstacle detection, as well as integrates Google Assistant for outdoor navigation and all the other "smart activities" that the assistant can do. The assistant will provide voice directions (which can be geared towards using bluetooth devices easily) and the sensors will help in avoiding obstacles which helps in increasing self-awareness. Another beta-feature was to identify moving obstacles and play sounds so the person can recognize those moving objects (eg. barking sounds for a dog etc.)

## How we built it

Its a raspberry-pi based device and we integrated Google Cloud SDK to be able to use the vision API and the Assistant and all the other features offered by GCP. We have sensors for depth perception and buzzers to play alert sounds as well as camera and microphone.

## Challenges we ran into

It was hard for us to set up raspberry-pi, having had no background with it. We had to learn how to integrate cloud platforms with embedded systems and understand how micro controllers work, especially not being from the engineering background and 2 members being high school students. Also, multi-threading was a challenge for us in embedded architecture

## Accomplishments that we're proud of

After hours of grinding, we were able to get the raspberry-pi working, as well as implementing depth perception and location tracking using Google Assistant, as well as object recognition.

## What we learned

Working with hardware is tough, even though you could see what is happening, it was hard to interface software and hardware.

## What's next for i4Noi

We want to explore more ways where i4Noi can help make things more accessible for blind people. Since we already have Google Cloud integration, we could integrate our another feature where we play sounds of living obstacles so special care can be done, for example when a dog comes in front, we produce barking sounds to alert the person. We would also like to implement multi-threading for our two processes and make this device as wearable as possible, so it can make a difference to the lives of the people.