## Inspiration

Our biggest inspiration came from our grandparents, who often felt lonely and struggled to find help. Specifically, one of us have a grandpa with dementia. He lives alone and finds it hard to receive help since most of his relatives live far away and he has reduced motor skills. Knowing this, we were determined to create a product -- and a friend -- that would be able to help the elderly with their health while also being fun to be around! Ted makes this dream a reality, transforming lives and promoting better welfare.

## What it does

Ted is able to...

* be a little cutie pie
* chat with speaker, reactive movements based on conversation (waves at you when greeting, idle bobbing)
* read heart rate, determine health levels, provide help accordingly
* Drives towards a person in need through using the RC car, utilizing object detection and speech recognition
* dance to Michael Jackson

## How we built it

* popsicle sticks, cardboards, tons of hot glue etc.
* sacrifice of my fingers
* Play.HT and Claude 3.5 Sonnet
* YOLOv8
* AssemblyAI
* Selenium
* Arudino, servos, and many sound sensors to determine direction of speaker

## Challenges we ran into

Some challenges we ran into during development was making sure every part was secure. With limited materials, we found that materials would often shift or move out of place after a few test runs, which was frustrating to keep fixing. However, instead of trying the same techniques again, we persevered by trying new methods of appliances, which eventually led a successful solution!

Having 2 speech-to-text models open at the same time showed some issue (and I still didn't fix it yet...). Creating reactive movements was difficult too but achieved it through the use of keywords and a long list of preset moves.

## Accomplishments that we're proud of

* Fluid head and arm movements of Ted
* Very pretty design on the car, poster board
* Very snappy response times with realistic voice

## What we learned

* power of friendship
* don't be afraid to try new things!

## What's next for Ted

* integrating more features to enhance Ted's ability to aid peoples' needs --> ex. ability to measure blood pressure
## Inspiration

When you think of the word “nostalgia”, what’s the first thing you think of? Maybe it’s the first time you tried ice cream, or the last concert you’ve been to. It could even be the first time you’ve left the country. Although these seem vastly different and constitute unique experiences, all of these events tie to one key component: memory. Can you imagine losing access to not only your memory, but your experiences and personal history? Currently, more than 55 million people world-wide suffer from dementia with 10 million new cases every year. Many therapies have been devised to combat dementia, such as reminiscence therapy, which uses various stimuli to help patients recall distant memories. Inspired by this, we created Remi, a tool to help dementia victims remember what’s important to them.

## What it does

We give users the option to sign up as a dementia patient, or as an individual signing up on behalf of a patient. Contributors to a patient's profile (friends and family) can then add to the profile descriptions of any memory they have relating to the patient. After this, we use Cohere API to piece together a personalized narration of the memory; this helps patients remember all of their most heartening past memories. We then use an old-fashioned styled answering machine, created with Arduino, and with the click of a button patients can listen to their past memories read to them by a loved one.

## How we built it

Our back-end was created using Flask, where we utilized Kintone API to store and retrieve data from our Kintone database. It also processes the prompts from the front-end using Cohere API in order to generate our personalized memory message; as well, it takes in button inputs from an Arduino UNO to play the text-to-speech. Lastly, our front-end was built using ReactJS and TailwindCSS for seamless user interaction.

## Challenges we ran into

As it was our first time setting up a Flask back-end and using Kintone, we ran into a couple of issues. With our back-end, we had trouble setting up our endpoints to successfully POST and GET data from our database. We also ran into troubles setting up the API token with Kintone to integrate it into our back-end. There were also several hardware limitations with the Arduino UNO, which was probably not the most suitable board for this project. The inability to receive wire-less data or handle large audio files were drawbacks, but we found workarounds that would allow us to demo properly, such-as using USB communication.

## Accomplishments that we're proud of

* Utilizing frontend, backend, hardware, and machine learning libraries in our project
* Delegating tasks and working as a team to put our project together
* Learning a lot of new tech and going to many workshops!

## What we learned

We learned a lot about setting up databases and web frameworks!

## What's next for Remi

Since there were many drawbacks with the hardware, future developments in the project would most likely switch to a board with higher bandwidth and wifi capabilities, such as a Raspberry Pi. We also wanted to add in a feature where contributors to a patient's memory library could record their voices, and our Text-to-Speech AI would mimic them when narrating a story. Reminiscence therapy involves more than narrating memories, so we wanted to add ore sensory features, such as visuals, to invoke memories and nostalgia for the patient as effectively as possible. For example, contributors could submit a picture of their memories along with their description, which could be displayed on an LCD attached to the answering machine design. On the business-end, we hope to collaborate with health professionals to see how we can further help dementia victims.
## **Inspiration**

1/5 elderly Canadians feel isolated as a result of their environment. In an everchanging world, society often forgets about the people that built the present world we live in. Elderly people constantly battle between the desire to connect with the changing society around them whilst worrying that they are being a bother to their children and grandchildren.

Having something that always has time to understand and talk to you would be great for loneliness and mental health issues in elderly people. This is where BloomBuddy comes in. Like a human, the plant lives alongside the user, undergoing all the environmental changes and basic necessities it would normally need. As such, it understands the environment that elderly people live in and is able to be a present and effective companion for elderly people. BloomBuddy not only analyzes the words coming from the user but also the environment and climate surrounding the user that could impact their mental health.

## **What it does**

Using machine learning, BloomBuddy is able to analyze the environment both the plant and the user is living in and generate a personality based off of those metrics. Through the unique personality created through the shared environment between the user and BloomBuddy, the plant is able to provide realistic and relatable responses to the user. BloomBuddy lives alongside the human and generates outputs based off of whether it needs resources (eg. water, more light...), it is self caring and automatically notifies the user on it's metrics using sensors. The user is able to communicate with their unique BloomBuddy through a screen and typed input, effectively communicating with the personalized ML model as they would a human. Each BloomBuddy is unique in both personality and digital presence, with a individualized NFT generated per plant built on the **Flow** blockchain network.

*The plant's metrics are easily viewable through the 7 inch display or through \*\*Kintone*\* where the user can see further data analysis of measured metrics throughout a period of time.

## **How we built it**

Some of the sensors we used includes a photoresistor, a humidity sensor, a temperature sensor and a moisture sensor to collect the large variety of data. Arduino IDE was used to program the Arduino and ESP32 respectively, while also programming the communication between the two with UART protocol to store the data on the ESP32 to display on the screen. We built the webpage with HTML for the template, CSS for the styling, and JavaScript for the functionality, while also integrating OpenAI API into the webpage. The NFT was created on the Flow blockchain network using Cadence and Javascript to create the smartcontracts and environment to validate the NFT.

## **Challenges we ran into**

It was our first time working with a lot of the technologies and we namely had several issues with hardware.

Our initial approach with the Raspberry Pi failed due to a faulty SD card reader. After hours of long work to debug and resolve the issue, we pivoted to an Arduino instead to fulfill the role as the central computing unit of our project. However, the Arduino still had some issues on communicating with the ESP32 that needed to be solved.

When implementing Flow into our project, we were unfamiliar with the environment and it was our first time setting a Web3 environment up. We encountered many issues getting the environment, smart contracts and scripts setup.

Nevertheless, our team learned a lot by working with new software and languages. It took all of 24+ hours with no sleep and many Red Bulls but we'd like to think it was worth it :).

## **Accomplishments that we're proud of**

We were able to support our hardware system with a strong, multi-aspect software system consisting of tech ranging from Web3, web and app development, data processing and more. The entire tech stack that we learned was geared towards the one goal of providing the most realistic interactable machine to provide companionship to elderly people and we'd like to say we've accomplished that.

As a team, we used our time efficiently by delegating responsibilities well based on skills and experience. Despite issues with hardware and software, our team pushed through it and learned lots in the process.

The issue we tackled hit home for our entire team as we've seen first hand how elderly people get neglected on accident in our fast-paced society. We're proud that within 24 hours, we were able to construct an MVP of a promising solution that we all truly believe in.

## **What we learned**

Although we had many setbacks, we overcame most of them and learned from our experiences. We quickly learned how to implement OpenAPI using JavaScript within a microcontroller such as the Arduino. Furthermore, as we had limited experience with NFTs, we learned how use Flow for Web3 implementation with NFTs. Through the hackathon, we were able to deploy a webpage on the ESP32 with the help of the mentor panel, as without them, we wouldn't have been able to get past this step.

Most of all, we've learned that any technology is learnable given a strong passion for the project and a team that's motivated to learn. As a collective team, we can confidently say we've learned way more then we expected coming into MakeUofT.

## **What's next for: BloomBuddy**

The next step is scaling the project by adding more functionalities to
1) Increase accessibility for elderly people with disabilities
2) Generate more impact by personalizing the plant more to tailor to individual mental illnesses
3) Further develop Web3 functionality to enhance NFT collections and reward users with FLOW tokens.

and more...

BloomBuddy is just the start of what personifying our hobbies could look like. Whether we realize it or not, every living being shows their behavior in some way. BloomBuddy is proof of concept that personifying the things we care about will enhance the growth of others as well as ourselves to
feasibly make a larger impact in the world with the help of technologies like AI and Web3.