UC Davis

The aim of my dissertation was to investigate the command and control of machines by humans. There have been many control methods to interface with machines, and this dissertation focused on surface electromyography (sEMG). Surface EMG is the measurement of electrical signals produced by muscle(s) and measured at the skin’s surface. Several EMG techniques rely on multiple sensors and machine learning for multi-DOF (degree of freedom) or multi-command control and are therefore more complex and may be sensitive to signal degradation. The first goal was to develop a robust, single-site sEMG control methodology that could communicate more than one command from a single muscle site or EMG sensor. A computer-based cursor-to-target task assessed the performance of the single-site sEMG for two levels of control: auto-rotate (automatic cursor rotation and user-controlled cursor forward motion) and manual rotate (user-controlled cursor rotation and forward motion). The experiment demonstrated that the auto-rotate method led to better throughput, or the rate of selecting targets adjusted for difficulty, but not significantly better path efficiency. However, the subjects were able to learn the manual-rotate method where a single-site sEMG control method communicated two commands. The manual rotate method appeared viable for expansion from two to four commands while maintaining a single sensor, but it was unclear how to best train subjects to use the system. The subsequent experiment investigated the effects of different training methodologies on performance, cognitive workload, and trust. The augmented feedback training techniques led to early and sustained performance gains with lower cognitive workload and higher trust. Even with extensive training to learn the commands, subjects did not perfectly perform the four sEMG commands. A subsequent analysis revealed that adjusting the sEMG command parameters may help personalize the command system to the individuals and improve command performance. The knowledge gained from these studies culminated in a three-handed coordination pilot study with a sEMG-controlled, collaborative robot serving as the third hand. Subjects learned how to improve their coordination of the three “hands.” The pilot study also served to inform future experiments that will continue to integrate the human with a collaborative robot for more complex tasks.