Development and Validation of a Postural Controller for Advanced Myoelectric Prosthetic Hands
Myoelectric control systems (MECs) remain the technological bottleneck in the development of advanced prosthetic hands. MECs should provide a human machine interface that deciphers user intent in real-time and operates effectively in daily life. Current MECs like finite state machines and pattern recognition systems require physiologically inappropriate commands to indicate intent and/or lack effectiveness in a clinical setting. The work of this dissertation aims to develop and validate a novel MEC architecture, namely postural control, in order to supplant the current state of the art MECs and recreate more of the characteristics of the intact limb. Specifically, the development of the postural control systems builds upon previous work based on principal component analysis of human grasping. Novel attributes of the postural control system were then added to the MEC, empirically tested, and validated with able limbed subjects using a virtual hand interface. Further investigation of the postural controller was performed by comparing it to state of the art commercial and research MECs with able limbed subjects using a physical prosthesis during activities of daily living. The dissertation concludes by verifying the increased effectiveness and robustness of the postural controller compared to other MECs when used by persons with transradial limb loss to perform activities of daily living with a physical prosthesis.