Walker, Ian D.2009-06-032009-06-031995Farry, Kristin Ann. "Issues in myoelectric teleoperation of complex artificial hands." (1995) Diss., Rice University. <a href="https://hdl.handle.net/1911/16820">https://hdl.handle.net/1911/16820</a>.https://hdl.handle.net/1911/16820This dissertation introduces a novel method of teleoperation of complex anthropomorphic robotic hands: converting the myoelectric signal generated by an operator's muscles during movement into robot commands replicating the motion. This teleoperation scenario is, in a sense, the limiting case of myoelectric prosthetic hand control. This project contributes to implementation of a practical myoelectric teleoperation system and improved prosthetic hand control by analyzing the myoelectric spectrum's variation during thumb motions. The investigation applies a new spectral estimation approach, Thomson's multiple window method (MWM), to the myoelectric signal. The MWM estimate has much lower bias and variance than traditional periodogram estimates, making it a better candidate to compute motion classification features. The MWM is also less sensitive to motion artifact than autoregressive methods. Extending Thomson's MWM into a time-frequency analysis tool analogous to the short-time Fourier transform, here called the short-time Thomson transform, shows that the myoelectric signal may be more stationary than previously thought. This project includes development of a unique myoelectric data collection system (MDCS) and a myoelectric teleoperation demonstration system (MTDS). The MDCS allows simultaneous measurement of 16 hand joint motions and 8 myoelectric signals. This capability enables close alignment of myoelectric signatures in time based on the hand motions and a search for motion-specific temporal characteristics in the myoelectric signal. While this study yields little evidence of motion-specific temporal consistency, it shows promising motion-specific spectral consistency. Spectral analysis proves less sensitive to alignment uncertainties than temporal analysis. An evaluation of five techniques for finding a motion's starting point in the myoelectric signal, a major implementation concern, suggests that we not pursue alignment-sensitive myoelectric control algorithms. Finally, the MTDS is used to demonstrate myoelectric control of chuck and key grasp motions of NASA/JSC's Utah/MIT Dextrous Hand, realtime, with 90% accuracy. The demonstration uses the time-varying myoelectric spectrum estimated with short-time Fourier transforms; however, this project lays the foundation for using the superior short-time Thomson transforms in this application.367 p.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.ElectronicsElectrical engineeringBiomedical engineeringAerospace engineeringThesisTHESIS E.E. 1995 FARRY