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Electromyographic robotic hand control
Boyd, Luke Orgeldinger, Sam Bowman, Michael Zhang, Xiaoli
Boyd, Luke
Orgeldinger, Sam
Bowman, Michael
Zhang, Xiaoli
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2025-04
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Abstract
The research being done at the Intelligent Robotics and Systems Lab at the Colorado School of Mines is working to create a inexpensive and simple robotic hand that is able to use complex machine learning classification algorithms in unison to create a consumer friendly prosthetic hand. When looking at other prosthetic hands that are commonly available for consumers and not in research laboratories. They are often either to simple to rival a real hand when performing daily tasks or they cost tens of thousands of dollars. Generally, of these advanced prosthetic hands, they typically are only programmed with a few set grasp types and do not react dynamically to the intent of the user, leading to an unsatisfied patient and a high discharge rate of these advanced hands. Where our hand combats this issue by using a machine learning classification model and interpolation of the binary grasp types to create an infinite amount of different grasp types between the extremes of the set grasp motions. This allows our hand to be more easily used when there is not a preset grasp for the task a user is trying to perform.
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