Librecorder Project

Invention Coach:

Robert L. Read, Avinash Baskaran, Megan Cadena

Public Inventor(s):

Roy Phillips, Sanchay Devnath, Nipul Avduth, Guillaume

Previous PIs:

Jeff Lin

Main Link:

https://github.com/PubInv/librecorder

Motivation:

Origin of Concept

Modern diagnostic devices are often expensive, proprietary, and designed to perform a single task, making them inaccessible or impractical in many low-resource clinical and field environments. At the same time, advances in AI-powered image and signal analysis offer enormous potential for rapid, accurate point-of-care diagnostics but researchers and innovators frequently lack a unified, open platform for collecting data, integrating sensors, and deploying algorithms. Librecorder was created to bridge this gap: to provide a flexible, open-source foundation that replaces a patchwork of standalone tools with a single, extensible system for capturing, organizing, and analyzing diagnostic data. By lowering technical barriers and reducing redundant engineering work, the project empowers clinicians, public-interest technologists, and researchers to focus on what truly matters: developing and validating new diagnostic methods that can improve healthcare access and outcomes worldwide.

Story:

Project Story & Evolution

The Librecorder project began with a simple realization: every new diagnostic idea seemed to require reinventing the same technical foundation — sensor management, data capture, case tracking, user interfaces, and hardware integration. Researchers and public-interest technologists were repeatedly building ad-hoc tools just to begin experimenting, slowing innovation and creating steep entry barriers for anyone working outside large, well-funded labs. Early prototypes were little more than scripts and hacked-together hardware, but they proved the concept: a single, shared platform could dramatically accelerate diagnostic research.

As the team explored real-world use cases — from field clinics to community laboratories — the need for a modular, device-agnostic system became clearer. The project evolved into a structured, open-source framework designed to make diagnostic experimentation easier, more repeatable, and more portable. Cameras, sensors, and analysis algorithms were gradually abstracted into interchangeable components; the software matured into a robust core capable of organizing patient cases, managing workflows, and supporting diverse diagnostic modalities.

Today, Librecorder represents the convergence of these lessons: a practical, extensible tool built to reduce duplicated effort, support rapid prototyping, and empower innovators working toward more accessible and equitable diagnostic technology.


























		

	
	
		

			
Status:

		  
Active

		

	
	
		

			
Skills Needed

		  
1. Software Engineer (Backend / Systems)


Skills needed:


    

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    HTTP and REST fundamentals, JSON message structuring, Simple API design, event-driven data flow understanding, modular system design, light integration testing
    

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What they support:
Designing the simple communication protocol and building the backend glue that connects classifier ↔ server ↔ browser.


2. Machine Learning Engineer / Data Scientist


Skills needed:


    

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    Understanding classifier inputs/outputs, designing abstraction layers around models, pre-processing and post-processing concepts, handling model confidence and error outputs, versioning and reproducibility best practices
    

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What they support:
Preparing the classifier so it can be plugged into the system in a predictable, modular way.


3. Front-End / Web Developer


Skills needed:


    

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    Consuming APIs from the browser (fetch/async), basic UI/UX for technical tools, browser-side state management, displaying structured results clearly, understanding how WebSockets or polling works (conceptually)
    

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What they support:
Creating the browser tool that interacts with the backend, visualizes predictions, and demonstrates the communication protocol in action.


4. System Architect (optional but valuable for POC)


Skills needed:


    

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    High-level system design, separation of concerns (UI ↔ backend ↔ classifier), designing modular, replaceable components, ensuring scalability paths even for a small prototype
    

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What they support:
Ensuring the entire system fits together cleanly and remains maintainable as the project grows.


5. QA / Test Engineer (light involvement)


Skills needed:


    

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    Designing simple test cases for protocol behavior, regression checks for classifier outputs, end-to-end interaction testing in browser
    

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What they support:
Verifying the POC works consistently and produces predictable behavior.


6. UX/UI Designer (User Surveys & Studies Focus)


Skills needed:


    

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    User research methods (interviews, surveys, contextual inquiry), usability study design and execution, journey mapping and task analysis, low- and high-fidelity prototyping, accessibility and interaction design principles, translating user insights into actionable requirements
    

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What they support:
Ensuring the browser tool and workflow are aligned with real user needs and validating how clinicians/researchers interact with the system through structured studies.



		

	
	
	
		

			
Collateral

		  
Phase 1 — Core Foundations (MVP Collateral)


    

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    Project overview, motivation & problem statement, high-level system architecture diagram, basic communication protocol overview, classifier integration summary, developer onboarding README, user personas (initial), and simple browser demo
    

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Phase 2 — Functional Expansion


    

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    Detailed module interaction diagrams, API overview, data flow maps, low-fi UI wireframes, user research plan & survey templates, usability study plan, sample data sets (synthetic or open), and basic user manual/FAQ
    

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Phase 3 — Validation & Community Engagement


    

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    Usability study reports, hi-fi UI prototypes, case studies & example workflows, demo video or interactive walkthrough, website/landing page, slide deck for conferences & funders, impact statement, and responsible AI & ethics overview
    

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Phase 4 — Maturity & Sustainability


    

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    Full design system/UI standards, maintenance & governance model, version release notes, extended documentation (hardware, sensors, advanced setup), open-source community guidelines, budget & funding package, post-study insights & long-term roadmap
    

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Photo Gallery

		  



		

	
	
		

			
Video