Advisor Information
Jorge Zuniga
Presentation Type
Poster
Start Date
1-3-2019 10:45 AM
End Date
1-3-2019 12:00 PM
Abstract
There are many options for a prosthetic mode of control. The most commonly created modes of control involve surface EMG (sEMG) and require that the user have well defined muscle activation in order to trigger the movement of the prosthetic. Unfortunately, many prosthetic users do not have the required muscle tone or strength to trigger these sensors, so the need for a control mechanism that doesn’t rely on sEMG is clear. This study focuses on the development of a low-cost, voice activated prosthetic mode of control compatible with the prosthetics developed by Dr. Zuniga and his research team.
The printed circuit board (PCB) was designed using EAGLE EDA and incorporates a battery, with charging circuit, dedicated power supply, microcontroller, and an off the shelf voice recognition device called the EasyVR 3. This is shown in figures one and two.
The obtained power consumption results were analyzed for six different lithium polymer capacities and for a varying number of grip events in a twelve hour period. The battery life of the device is then calculated using a standard battery life equation.
A Voice Activated Prosthetic Mode of Control
There are many options for a prosthetic mode of control. The most commonly created modes of control involve surface EMG (sEMG) and require that the user have well defined muscle activation in order to trigger the movement of the prosthetic. Unfortunately, many prosthetic users do not have the required muscle tone or strength to trigger these sensors, so the need for a control mechanism that doesn’t rely on sEMG is clear. This study focuses on the development of a low-cost, voice activated prosthetic mode of control compatible with the prosthetics developed by Dr. Zuniga and his research team.
The printed circuit board (PCB) was designed using EAGLE EDA and incorporates a battery, with charging circuit, dedicated power supply, microcontroller, and an off the shelf voice recognition device called the EasyVR 3. This is shown in figures one and two.
The obtained power consumption results were analyzed for six different lithium polymer capacities and for a varying number of grip events in a twelve hour period. The battery life of the device is then calculated using a standard battery life equation.