## Inspiration

Loneliness affects countless people and over time, it can have significant consequences on a person's mental health. One quarter of Canada's 65+ population live completely alone, which has been scientifically connected to very serious health risks. With the growing population of seniors, this problem only seems to be growing worse, and so we wanted to find a way to help both elderly citizens take care of themselves and their loved ones to take care of them.

## What it does

Claire is an AI chatbot with a UX designed specifically for the less tech-savvy elderly population. It helps seniors to journal and self-reflect, both proven to have mental health benefits, through a simulated social experience. At the same time, it allows caregivers to stay up-to-date on the emotional wellbeing of the elderly. This is all done with natural language processing, used to identify the emotions associated with each conversation session.

## How we built it

We used a React front-end served by a node.js back-end. Messages were sent to Google Cloud's natural language processing API, where we could identify emotions for recording and entities for enhancing the simulated conversation experience. Information on user activity and profiles are maintained in a Firebase database.

## Challenges we ran into

We wanted to use speech-to-text so as to reach an even broader seniors' market, but we ran into technical difficulties with streaming audio from the browser in a consistent way. As a result, we chose simply to have a text-based conversation.

## Accomplishments that we're proud of

Designing a convincing AI chatbot was the biggest challenge. We found that the bot would often miss contextual cues, and interpret responses incorrectly. Over the course of the project, we had to tweak how our bot responded and prompted conversation so that these lapses were minimized. Also, as developers, it was very difficult to design to the needs of a less-tech-saavy target audience. We had to make sure our application was intuitive enough for all users.

## What we learned

We learned how to work with natural language processing to follow a conversation and respond appropriately to human input. As well, we got to further practise our technical skills by applying React, node.js, and Firebase to build a full-stack application.

## What's next for claire

We want to implement an accurate speech-to-text and text-to-speech functionality. We think this is the natural next step to making our product more widely accessible.
## Inspiration

What is our first thought when we hear "health-care"? Is it an illness? Cancer? Disease? That is where we lose our focus from an exponential increasing crisis, especially in this post-COVID era. It is MENTAL HEALTH!

Studying at university, I have seen my friends suffer from depression and anxiety looking for someone to hear them out for once.

Statistically, an estimated 792 million individuals worldwide suffer from mental health diseases and concerns.
That's roughly one out of every ten persons on the planet.

In India, where I am from, the problem is even worse. Close to 14 per cent of India's population required active mental health interventions. Every year, about 2,00,000 Indians take their lives. The statistics are even higher if one starts to include the number of attempts to suicide.

The thought of being able to save even a fraction of this number is powerful enough to get me working this hard for it.

## What it does

Noor, TALKs because that's all it takes. She provides a comfortable environment to the user where they can share their thoughts very privately, and let that feelings out once and for all.

## How we built it

I built this app in certain steps:

1. Converting all the convolutional intents into a Machine Learning Model - Pytorch.
2. Building a framework where users can provide input and the model can output the best possible response that makes the most sense. Here, the threshold is set to 90% accuracy.
3. Building an elegant GUI.

## Challenges we ran into

Building Chatbots from scratch is extremely difficult. However, 36 hours were divided into sections where I could manage building a decent hack out of everything I got. Enhancing the bot's intelligence was challenging too. In the initial stages, I was experimenting with fewer intents, but with the addition of more intents, keeping track of the intents became difficult.

## Accomplishments that we're proud of

First, I built my own Chatbot for the first time!!! YAYYYYY! This is a really special project because of the fact that it is dealing with such a major issue in the world right now. Also, this was my first time making an entire hackathon project using Python and its frameworks only. Extremely new experience. I am proud of myself for pushing through the frustrating times when I felt like giving up.

## What we learned

Everything I made during this hackathon was something I had never done before. Legit, EVERYTHING! Let it be using NLP or Pytorch. Or even Tkinter for Graphic User Interface (GUI)! Honestly, may not be my best work ever but, definitely something that taught me the most!

## What's next for Noor

Switch from Tkinter to deploying my script into an application or web app. The only reason I went with Tkinter was to try learning something new. I'll be using flutter for app development and TFjs for a web-based application.

Discord: keivalya#8856
## 🤔 Problem Statement

* 55 million people worldwide struggle to engage with their past memories effectively (World Health Organization) and 40% of us will experience some form of memory loss (Alzhiemer's Society of Canada). This widespread struggle with nostalgia emphasizes the critical need for user-friendly solutions. Utilizing modern technology to support reminiscence therapy and enhance cognitive stimulation in this population is essential.

## 💡 Inspiration

* Alarming statistics from organizations like the Alzheimer's Society of Canada and the World Health Organization motivated us.
* Desire to create a solution to assist individuals experiencing memory loss and dementia.
* Urge to build a machine learning and computer vision project to test our skillsets.

## 🤖 What it does

* DementiaBuddy offers personalized support for individuals with dementia symptoms.
* Integrates machine learning, computer vision, and natural language processing technologies.
* Facilitates face recognition, memory recording, transcription, summarization, and conversation.
* Helps users stay grounded, recall memories, and manage symptoms effectively.

## 🧠 How we built it

* Backend developed using Python libraries including OpenCV, TensorFlow, and PyTorch.
* Integration with Supabase for data storage.
* Utilization of Cohere Summarize API for text summarization.
* Frontend built with Next.js, incorporating Voiceflow for chatbot functionality.

## 🧩 Challenges we ran into

* Limited team size with only two initial members.
* Late addition of two teammates on Saturday.
* Required efficient communication, task prioritization, and adaptability, especially with such unique circumstances for our team.
* Lack of experience in combining all these foreign sponsorship technology, as well as limited frontend and fullstack abilities.

## 🏆 Accomplishments that we're proud of

* Successful development of a functional prototype within the given timeframe.
* Implementation of key features including face recognition and memory recording.
* Integration of components into a cohesive system.

## 💻 What we learned

* Enhanced skills in machine learning, computer vision, and natural language processing.
* Improved project management, teamwork, and problem-solving abilities.
* Deepened understanding of dementia care and human-centered design principles.

## 🚀 What's next for DementiaBuddy

* Refining face recognition algorithm for improved accuracy and scalability.
* Expanding memory recording capabilities.
* Enhancing chatbot's conversational abilities.
* Collaborating with healthcare professionals for validation and tailoring to diverse needs.

## 📈 Why DementiaBuddy?

Asides from being considered for the Top 3 prizes, we worked really hard so that DementiaBuddy could be considered to win multiple sponsorship awards at this hackathon, including the Best Build with Co:Here, RBC's Retro-Revolution: Bridging Eras with Innovation Prize, Best Use of Auth0, Best Use of StarkNet, & Best .tech Domain Name. Our project stands out because we've successfully integrated multiple cutting-edge technologies to create a user-friendly and accessible platform for those with memory ailments. Here's how we've met each challenge:

* 💫 Best Build with Co:Here: Dementia Buddy should win the Best Build with Cohere award because it uses Cohere's Summarizing API to make remembering easier for people with memory issues. By summarizing long memories into shorter versions, it helps users connect with their past experiences better. This simple and effective use of Cohere's technology shows how well the project is made and how it focuses on helping users.
* 💫 RBC's Retro-Revolution - Bridging Eras with Innovation Prize: Dementia Buddy seamlessly combines nostalgia with modern technology, perfectly fitting the criteria of the RBC Bridging Eras prize. By updating the traditional photobook with dynamic video memories, it transforms the reminiscence experience, especially for individuals dealing with dementia and memory issues. Through leveraging advanced digital media tools, Dementia Buddy not only preserves cherished memories but also deepens emotional connections to the past. This innovative approach revitalizes traditional memory preservation methods, offering a valuable resource for stimulating cognitive function and improving overall well-being.
* 💫 Best Use of Auth0: We succesfully used Auth0's API within our Next.js frontend to help users login and ensure that our web app maintains a personalized experience for users.
* 💫 Best .tech Domain Name: AMachineLearningProjectToHelpYouTakeATripDownMemoryLane.tech, I can't think of a better domain name. It perfectly describes our project.