Enable

Live prototype: here

Inspiration

After years of working with individuals facing disabilities, one thing was clear: the assistive technology market was extremely underserved. All solutions that did exist in the space were mediocre at best and ignorant of what truly periled the end users, and left individuals disheartened and with no other recourse. In the status quo, prosthetic devices must be customized for their user. Because of this, prosthetics are associated with incredibly high costs, costing tens of thousands of dollars. This cost establishes an extremely difficult barrier to access, blocking people who desperately need assistive technology from receiving it. Enable seeks to break down that barrier, and make independence accessible.

What it does

Using an LLM model, Enable takes in a natural language input and returns a prosthetic, ready-to-3D-print file. Users type in a prompt related to a specific use case - say, "I want to play the guitar". Our fine tuned model takes in that input, analyzes what that action requires, and returns a prosthetic design STL, a file that's ready to 3D print. This revolutionary, novel approach enables individuals to gain access to crucial assistive technology from the comfort of their own homes, with no need for an engineer or designer, all at an extremely affordable cost. Each of these generated prosthetics are modular, attaching to one central mount that provides pneumatic - or powered by air through pumps - power. This enables rapid flexibility and an unprecedented opportunity for independence, something that was previously unheard of.

How we built it

Our interface has a lot of moving parts. The process centers around 3D printing, specifically to make our prosthetic models. Using open source software and our fine tuned model, we use 3D printing as our primary manufacturing technique. To create these files, we interface with a mathematical model capable of mapping equations to stereolithography (STL) files. These files are then taken into a printer-specific software that prepares it for printing. Going back to our mathematical model, we rely on an LLM to take our natural language inputs and turn them into requirements and parameters, each of which are deducted into mathematical representations and eventually, physical characteristics. On the front end, we utilize next.js to manage our GUI.

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