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Adaptive Control Strategies for Powered Prostheses

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Excellent Video Summary of the Project

Project Overview

As part of the Georgia Tech EPIC Lab, I worked with researchers to assist in the development and evaluation of a neural-network-based control system for powered lower-limb prostheses. 

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My Role

  • Assisted in the development and testing of machine-learning-based prosthesis controllers

  • Evaluated controller performance across varying walking environments

  • Supported data collection, analysis, and experimental validation efforts

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Spring 2026 Powered Prosthesis Team

Why does this matter?

Traditional powered prostheses often require extensive user-specific tuning and separate control modes for different terrains. This research explored a more adaptive approach capable of responding continuously to changing walking conditions, with the goal of improving accessibility and reducing setup time for users.

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Skills Utilized

  • Research

  • Data Analysis 

  • Experimental Testing

  • Machine Learning

  • Prosthetics

  • Technical Communication​

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Inside the Human Augmented Controls Lab

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