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## Inspiration

Our project, OCUS.LY, is inspired by the desire to improve the focus and reduce stress in elderly individuals using technology. We understand that focus and stress management can greatly impact the quality of life for seniors, and we aim to address these challenges through a blend of technology and empathy.

## What it does

OCUS.LY is a platform that leverages advanced technology to track and enhance the well-being of our elderly users. Using computer vision, we monitor eye movements and blink patterns to gain insights into their focus levels and identify drowsiness. Additionally, we integrate with Terra-API to gather stress-related data, allowing us to provide a personalized solution.

Our platform does more than just collecting data; it offers hope and empowerment. When our elderly users need to boost their focus or unwind, we curate music recommendations and binaural beats designed to reduce stress and promote overall well-being.

## How we built it

We built OCUS.LY using a combination of technology and care:

* Firebase: We set up a Firebase project, utilizing Firebase Realtime Database for data storage and Firebase Authentication for secure access.
* Next.js: We created a user-friendly web interface using Next.js, enabling elderly users to log in, access their data, and receive music recommendations.
* Terra-API: We integrated with Terra-API to gather stress-related data, which serves as a crucial input for our music recommendations.
* Data Processing: We employed Python and OpenCV to process data related to eye movements and blinking, enabling us to calculate focus levels and detect drowsiness.
* User Interface: We designed an intuitive user interface to display stress and focus data in a visually engaging way.
* Music Recommendations: Based on the analysis of focus levels and stress data, we provide personalized music recommendations.

## Challenges we ran into

While developing OCUS.LY, we encountered several challenges:

* Data Processing Complexity: Analyzing eye movements and blink patterns accurately required advanced computer vision techniques.
* Real-time Data Updates: Ensuring real-time updates and seamless user experiences required careful optimization, Trying to integrate Python API using Flask API and again integrating that to next.js was hard.
* Secure Data Handling: Handling sensitive user data securely was a top priority, and we implemented robust security measures.

## Accomplishments that we're proud of

We take pride in the following accomplishments:

* Developing a platform that combines technology and compassion to improve the lives of elderly individuals.
* Providing data-driven insights into focus levels and stress management.
* Offering personalized music recommendations to enhance well-being.

## What we learned

Through the development of OCUS.LY, we learned:

* The power of technology to positively impact the lives of the elderly.
* The importance of empathy and care in the development of well-being solutions.

## What's next for OCUS.LY

Our journey with OCUS.LY is just beginning. In the future, we plan to:

* Further enhance the accuracy and capabilities of our data analysis.
* Expand our music recommendation offerings to provide a wider range of stress-reduction and focus-enhancing options.
* Reach more elderly individuals and empower them to experience a life with improved focus and reduced stress.

By continuing to blend technology and compassion, we aim to make OCUS.LY the bridge to a happier, more focused life for our beloved seniors.
## Inspiration

We wanted to bring the fun experience of Prisma to the immersive world of VR.

## What it does

The user explores Google street view photospheres transformed by our app into the style of different artists.

## How we built it

We have a distributed server architecture comprising of a Node.js server and a Python server (which hosts our trained Torch models). The two run on different AWS instances and communicate with each other with the gRPC library. A latitude longitude request is sent to the Node.js server, which saves a photosphere to Amazon S3 and sends the link to the Python server which processes the image in the style of the artist and sends back an S3 image link. The Node.js server then sends the S3 image link to the VR app.

## Challenges we ran into

1) The photosphere images for VR are 16MB. Right now, deep learning models are capable of dealing with images that are 1MB.
2) Training and integrating a deep learning network into Python.
3) Decreasing the latency of the model in creating different styles which is difficult as it is an iterative optimization process.
## 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.