Dexterous Hand, Phantom Limbs, Statistical Analysis, Neural Networks, Classification
We analyze techniques for myoelectrical signals classification for the purpose of designing a multifunctional prosthetic device for human amputees. The main advantage of our system over existing models is that it is more robust, easier to work with, more general, and efficient enough to run in real time. We achieve this with the help of "Supervised Growing Cell Structures." an artificial neural network model designed by Fritzke . The current paper focuses on the flexion of the index, middle and ring fingers, as these are the most difficult movements to tackle.
Creel, Christopher T.; Mehrotra, Kishan; Mohan, Chilukuri; and Ranka, Sanjay, "Analysis of Myoelectrical Signals for Building a Dextrous Hand" (1994). Electrical Engineering and Computer Science Technical Reports. Paper 156.