## Problem Statement

As the number of the elderly population is constantly growing, there is an increasing demand for home care. In fact, the market for safety and security solutions in the healthcare sector is estimated to reach $40.1 billion by 2025.
The elderly, disabled, and vulnerable people face a constant risk of falls and other accidents, especially in environments like hospitals, nursing homes, and home care environments, where they require constant supervision. However, traditional monitoring methods, such as human caregivers or surveillance cameras, are often not enough to provide prompt and effective responses in emergency situations. This potentially has serious consequences, including injury, prolonged recovery, and increased healthcare costs.

## Solution

The proposed app aims to address this problem by providing real-time monitoring and alert system, using a camera and cloud-based machine learning algorithms to detect any signs of injury or danger, and immediately notify designated emergency contacts, such as healthcare professionals, with information about the user's condition and collected personal data.
We believe that the app has the potential to revolutionize the way vulnerable individuals are monitored and protected, by providing a safer and more secure environment in designated institutions.

## Developing Process

Prior to development, our designer used Figma to create a prototype which was used as a reference point when the developers were building the platform in HTML, CSS, and ReactJs.
For the cloud-based machine learning algorithms, we used Computer Vision, Open CV, Numpy, and Flask to train the model on a dataset of various poses and movements and to detect any signs of injury or danger in real time.
Because of limited resources, we decided to use our phones as an analogue to cameras to do the live streams for the real-time monitoring.

## Impact

* **Improved safety:** The real-time monitoring and alert system provided by the app helps to reduce the risk of falls and other accidents, keeping vulnerable individuals safer and reducing the likelihood of serious injury.
* **Faster response time:** The app triggers an alert and sends notifications to designated emergency contacts in case of any danger or injury, which allows for a faster response time and more effective response.
* **Increased efficiency:** Using cloud-based machine learning algorithms and computer vision techniques allow the app to analyze the user's movements and detect any signs of danger without constant human supervision.
* **Better patient care:** In a hospital setting, the app could be used to monitor patients and alert nurses if they are in danger of falling or if their vital signs indicate that they need medical attention. This could lead to improved patient care, reduced medical costs, and faster recovery times.
* **Peace of mind for families and caregivers:** The app provides families and caregivers with peace of mind, knowing that their loved ones are being monitored and protected and that they will be immediately notified in case of any danger or emergency.

## Challenges

One of the biggest challenges have been integrating all the different technologies, such as live streaming and machine learning algorithms, and making sure they worked together seamlessly.

## Successes

The project was a collaborative effort between a designer and developers, which highlights the importance of cross-functional teams in delivering complex technical solutions. Overall, the project was a success and resulted in a cutting-edge solution that can help protect vulnerable individuals.

## Things Learnt

* **Importance of cross-functional teams:** As there were different specialists working on the project, it helped us understand the value of cross-functional teams in addressing complex challenges and delivering successful results.
* **Integrating different technologies:** Our team learned the challenges and importance of integrating different technologies to deliver a seamless and effective solution.
* **Machine learning for health applications:** After doing the research and completing the project, our team learned about the potential and challenges of using machine learning in the healthcare industry, and the steps required to build and deploy a successful machine learning model.

## Future Plans for SafeSpot

* First of all, the usage of the app could be extended to other settings, such as elderly care facilities, schools, kindergartens, or emergency rooms to provide a safer and more secure environment for vulnerable individuals.
* Apart from the web, the platform could also be implemented as a mobile app. In this case scenario, the alert would pop up privately on the user’s phone and notify only people who are given access to it.
* The app could also be integrated with wearable devices, such as fitness trackers, which could provide additional data and context to help determine if the user is in danger or has been injured.
## Inspiration

Growing up, many of us dreamed of exploring the world of music. However, we soon discovered that pursuing this passion often requires significant financial investment in production, mixing, and additional instruments. 🎛️ Well-meaning adults frequently warned us about the instability and security concerns associated with a music career. The reality is that music can be challenging, and we want to change that narrative.

There are barriers to entry at every turn:

* 87.6% of all artists remain undiscovered.
* Out of 1.3 million registered artists on Chartmetric over the past year, 710,000 have yet to achieve any career milestones.
* Only 11% of independent artists can sustain a living through music.

Even signing with a record label doesn’t guarantee success:

* Labels typically take 50-90% of the revenue generated by an artist, often with fine print that includes hidden distributor fees and overhead costs.
* Remarkably, even with a major label, only 1 in 2,149 artists achieves commercial success.

With soundchain, we aim to empower aspiring artists to pursue their musical dreams independently while connecting them with a supportive community that believes in their potential. This platform is also perfect for hobbyists who have a polished piece sitting in their files, just waiting for the right moment to be released. Whether you’re looking to build a career in music or simply share your passion, we’re here to help you take that next step. 🔊

## What it does

soundchain is a decentralized platform that connects aspiring artists with music enthusiasts, enabling fans to discover and support emerging talent through unbiased music playback and NFT purchases in exchange for rights. Artists and "angel music investors" are matched through our music-forward playback system.

It's actually quite simple:

1. Artists upload snippets of their creative process (verses, beats, melody lines, anything in between). They set SUI-based crowdfunding goals to produce their song, and issuance terms for partners.
2. Potential supporters browse uploaded projects. They can view goals and decide to support projects based on the provided snippets. They buy-in to the potential of the project.
3. The Sui blockchain records the smart contract and creates an enforceable legal parameter for investors.
4. If the project takes off, initial investors can now resell their rights on secondary web3 markets. 🚀

## How we built it

We utilized the Sui blockchain for its scalability and performance, implementing smart contracts to handle transactions. The application is integrated/connected with google-auth and the Enoki developer portal. The platform was developed with a user-friendly interface mocked on Figma to ensure seamless interactions between artists and listeners. Our front-end is made using Next.js, typescript, and assets from libraries such as material UI.

## Challenges we ran into

This project was our team's first time ever working with any web3 products. We went from not understanding what blockchain even was to maybe understanding a little bit of how blockchain works. We faced challenges in the integration of blockchain elements into our front-end application since we weren't familiar with the technology. Additionally, navigating copyright issues and establishing fair revenue-sharing models for artists required careful consideration.

## Accomplishments that we're proud of

We think that we came up with a bangin' 🎸 idea that can really change the way funding in the music industry works. We also ensured that this product was realistic and viable for integration in the current state of the music industry.

## What we learned

SO MUCH !!!! All new ways of designing, methodologies of ideation, new technologies, and first-time experiences. We also learned the importance of community engagement in building a platform that truly serves its users. Understanding the pain points and publication process from both artists and listeners helped us refine our approach and identify key features that enhance the overall experience.

## What's next for soundchain

* Mint NFTs that provide a visual badge/proof of support for supporters.
* Implement tags for different types of projects including Songwriting, Lyrics, Rap, and Beats.
* Integrate tracking systems to measure expected revenue shares through traditional music streaming applications, allowing the smart contract to take a more active role in revenue sharing.
## Inspiration

Availability of very expensive and high-quality VR equipment and the realization that this could be used to help disabled or immobilized people experience a realistic simulation.

## Main Features

The high-level purpose of our system is to allow disabled or immobilized users to take advantage of virtual reality in order to experience mobility in any chosen environment. The Oculus Rift, a head-mounted virtual reality device, allows our users to be fully immersed in the robot's virtual environment. The left thumb stick of an Xbox One controller is used to yaw the robot's camera, or move it forwards or backwards to explore more uncharted territories.

## How it was built

The robot was built using a Qualcomm Dragon Board 410c, an Oculus Rift VR headset along with software APIs, a NRF wireless transceiver, Arduino Uno. We used Wi-Fi to allow data flow between the Qualcomm Board and the Oculus VR Headset and then the headset to the local Wi-Fi and then . We used a basic chassis for the prototype of the robot while using DC motors for driving and Servo motors for yawing the camera on the robot.

## Challenges We Ran Into

We encountered several bugs and errors when trying to establish Wi-Fi connection between Arduino and the NRF transceiver.

## What We learned

Interfacing Qualcomm Dragon Board with VR systems and then using Arduino to send data from cameras to the VR system. See our project on [Hackster](https://www.hackster.io/137869/experiencing-mobile-robotics-in-vr-78a52f)!

## What's next for VRobot

More elaborate computer vision of the robot and a wider range of VR environments.