Revolutionizing Healthcare Through Data-driven Music Technology

Project background 

Overview

LUCID is a cutting-edge health-tech company on a mission to transform music into medicine. By leveraging data-driven methodologies and clinically validated research, the company creates personalized music experiences aimed at improving health and wellness outcomes. Their primary product is an API powered by a deep reinforcement learning engine capable of recommending and generating playlists based on users’ current mood and desired emotional states. This groundbreaking technology is designed to support clients in the digital health and wellness space.

Our involvement began with developing a measurement microservice, a critical component of LUCID’s platform, designed to seamlessly integrate with the existing infrastructure and help scale its API offering.

Project Goals

• Build a reliable and scalable measurement microservice to support LUCID’s API.
• Create real-time data collection and processing for improved music personalization.
• Setup integration with the company’s affective music recommendation system.
• Lay the foundation for further expansion in the digital health sector.

Challenges

• Designing an architecture that integrates with existing services.
• Managing large-scale real-time data flows using RabbitMQ and WebSockets.
• Implementing CI/CD pipelines to streamline deployments.

Our approach

Solution

To meet LUCID’s ambitious goals, we adopted an agile methodology and focused on creating a secure, efficient, and scalable measurement microservice that aligns with LUCID’s API and deep learning engine.

The measurement microservice was developed as a core component of LUCID’s music recommendation system. It collects, processes, and streams data in real time, providing essential inputs for the reinforcement learning engine. MongoDB was utilized for efficient data storage, while Kubernetes ensured scalability and fault tolerance.

Through this microservice, LUCID can analyze user interactions and continuously improve playlist recommendations, offering personalized experiences for end-users in the digital health space.

Team

The project was executed by a small but highly skilled team of two developers. They focused on building the measurement microservice, integrating it into the existing infrastructure, and making sure the system is scalable. Overall, the team’s strong collaboration and technical expertise were key to the success of this initiative.

Results

The successful implementation of the measurement microservice laid the groundwork for scaling LUCID’s platform to new clients. The microservice enhanced data processing capabilities and contributed to more accurate and personalized music recommendations. It also improved the system’s performance, enabling faster response times and greater reliability.

This milestone allows LUCID to expand its reach in the health and wellness industry, reinforcing its position as a pioneer in using music as medicine.