Case Study

Project Motivation

Reason for Undertaking the Project

Cue ASAP was undertaken as an undergraduate computer science final year project. The primary motivation was to develop a system that can assist individuals in emergencies by predicting events up to three minutes into the future, thereby potentially saving many lives.

Context or Background Information

In emergency situations, timely assistance is crucial. Whether it’s a health crisis, a violent incident, or an educational emergency, having a tool that can predict and provide immediate help could be a game-changer. Cue ASAP aims to be that tool by leveraging AI to assist users in critical moments.

Specific Events or Trends

The increasing reliance on AI and machine learning for real-time problem-solving and the rise of smart devices with advanced sensors triggered the need for this project. The potential to create a life-saving application that could function even without internet connectivity was a significant motivator.

Objectives

Main Goals of the Project

1. Address three major real-world problems: violence, healthcare, and education using AI.
2. Develop a system that can report emergencies to nearby authorities as quickly as possible.
3. Ensure the application can track location accurately even without an internet connection.

Specific Targets or Metrics

1. Implement a system that can track location using phone sensors and mathematical algorithms.
2. Create an AI chatbot, CUE Assist, to interact with users and provide necessary assistance based on the situation.
3. Ensure the platform is accessible and open-sourced for continuous updates and community contributions.

Challenges

Technical Challenges

• Ensuring accurate location tracking without internet connectivity.
• Integrating a self-learning AI chatbot capable of handling diverse emergency scenarios.

Operational or Logistical Challenges

• Coordinating with local authorities to ensure timely responses.
• Maintaining a robust and scalable infrastructure to handle multiple simultaneous emergencies.

User-Related or Market-Related Challenges

• Ensuring user privacy and data security.
• Achieving user adoption and trust in a critical application like this.

Approach and Process

Initial Steps

The project started with a focus on developing a web application using the MERN (MongoDB, Express.js, React, Node.js) stack for its reliability, security, and ease of implementation. The approach was to create a fast and responsive platform that could integrate with REST APIs seamlessly.

Key Phases

1. Requirement Analysis: Detailed discussions with potential users and stakeholders to understand their needs and scenarios.
2. Prototyping: Developing wireframes and initial prototypes to gather feedback.
3. Development: Iterative development using Agile methodology to ensure flexibility and continuous improvement.
4. Testing: Extensive testing to ensure the application’s reliability and accuracy in real-world scenarios.

Tools and Methodologies

• AI Chatbot: Implemented using Kommunicate.io, a powerful self-learning plugin.
• Open Source Development: Hosted on GitHub for transparency and collaborative development.

Technologies Used

• Frontend Technologies: Bootstrap, React
• Backend Technologies: Node.js, Express.js
• Databases: MongoDB (NoSQL)
• AI and Machine Learning: Kommunicate.io for the chatbot
• Version Control and Collaboration: GitHub

Implementation

User Onboarding

Users can register or login as guests for a limited time. The application collects necessary information and shares it with the nearest authorities and subscribed contacts.

AI Chatbot Functionality

The AI chatbot interacts with users through text or voice, processes the information, and provides appropriate responses. It can also create a ticket for a 1-to-1 chat room with a mentor for further assistance.

Location Tracking

The system uses phone sensors and advanced mathematical algorithms to determine the user’s location accurately without requiring internet connectivity.

Results

Outcomes

• Successfully developed a prototype demonstrating the application’s potential in real-time scenarios.
• Bridged the gap between emergency service authorities and individuals in distress, reducing response times and potentially saving lives.

Recognition

• The project was published in a journal, highlighting its innovative approach and potential impact.

Lessons Learned

1. Technological Feasibility: Advanced sensors and frameworks enable the development of robust emergency response applications.
2. Importance of User Feedback: Continuous user feedback is crucial for refining and improving the application.
3. Security and Privacy: Ensuring user data security and privacy is paramount in gaining user trust and adoption.

Future Improvements

Planned Enhancements

1. Native Application Development: Transitioning from a web application to a native app to provide a better user experience across platforms.
2. Advanced Features: Incorporating AI-powered recommendations, AR, and other advanced technologies.
3. UI/UX Improvements: Building an intuitive and engaging user interface.
4. Iterative Approach: Continuously optimizing and updating the application based on user feedback and technological advancements.
5. Enhanced Security Policies: Implementing new security measures as technology evolves to protect user data and privacy.