Project # 57
Enriching Prosthetic Freedom Through Machine Learning and Energy Conservation

Team Lead:	Dakota Houdeshell
Other Team Members:	Kevin Hoang
Faculty Advisor(s):	Dr. Young-Tae Kim
Department:	BE
Project Type:	REU
Title:	Enriching Prosthetic Freedom Through Machine Learning and Energy Conservation
Abstract:	The realm of prosthesis development has greatly expanded in recent years. Many amputated individuals now have access to neural prosthetics that allow for various freedoms of movement. The current marketed prosthetic devices use myoelectric signals to dictate movement however, this method is relatively limited on its number of motions. To expand and develop more freedom our team would like to add different actuation methods such as voice control or a machine learning module that is activated by a live camera feed. Additionally, we are researching potential energy conservation methods, such as having the device charge itself through kinetic motion, that can be embedded into the prosthetic. This would contribute to reducing the extensive charge times associated with the batteries found in the prosthesis, as well as serving to elongate the usage of the prosthetic by supporting a backup power supply for emergency situations.
Poster:	View Poster #57 (PDF)
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Project # 57Enriching Prosthetic Freedom Through Machine Learning and Energy Conservation