Teams

Prosthetics Team

NXT Prosthetics Team’s focus is to research and explore the intersection of neuroscience, engineering, and programming to develop prosthetic devices that can restore or enhance the functionality of limbs. The tools we primarily use are Fusion360 and Onshape for the computer-aided design of the prosthetics parts, Flashforge and Prusa 3D printers, and Arduino, which are coupled with electroencephalography (EEG) and electromyography (EMG) for the controls of the prosthetics.

The plan for the Prosthetics Team this upcoming semester is to split people into groups by experience level (newcomers, returning members). The newcomers will either download and load CAD designs to print or design their own CAD designs involving movement, depending on their experience and comfortability with CAD designs. The returning members will continue to work on their previous designs through printing and coding. Programming/Coding workshops will be held during the semester to assist anyone in their projects. This upcoming semester aims to give the newcomers experience with 3D printing and electrical circuit controls while also allowing the returning members to work towards completing their designs from the previous year.

Team Lead: Logan Mesiti

lmm8817@rit.edu

Fabric Electrodes Team

NXT Fabrics is an interdisciplinary team that strives to enhance signal detection for daily use in controlling prosthetics. We use tools like Myoware and Arduino to collect EMG signals and create prototypes using sewing and soldering techniques. We use this hardware to collect signals and then perform signal processing through software (C++) to extract meaningful data.

Our team is currently researching functional myography (FMG) for additional accuracy in signal detection and hopes to implement this technology in future designs.

Team Lead: Drew Hunter

ddh9356@rit.edu

Wheelchair Project

NXT’s Wheelchair Team is developing a motorized wheelchair prototype controlled by EEG data collected with OpenBCI. We are currently developing a data collector and preparing for human subjects research (HSR). Once collected, we will apply EEG data from participants to AI models for signal processing, and intended movement classification. The end goal is real time signal processing, for EEG signal translation into wheelchair movements.

Team Lead: Casey Large

cel9017@rit.edu

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