Medical science has made it possible to use prosthetic devices to restore the basic abilities needed to function in everyday life. Although robotic prosthetic hands can improve mobility over a simple hook prosthetic, the current state-of-the-art devices are still limited in their ability to grasp and hold objects as quickly and as accurately as the natural human hand. This project trains a deep learning neural network to control a robotic prosthetic hand in performing a grasping task.
|Commitee:||Rogers, Tamara, Sekmen, Ali|
|School:||Tennessee State University|
|School Location:||United States -- Tennessee|
|Source:||MAI 58/04M(E), Masters Abstracts International|
|Subjects:||Robotics, Computer science|
|Keywords:||Convolutional neural network, Deep learning, Prosthetic|
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