Proportional Myoelectric Control of Robots: Muscle Synergy Development Drives Performance Enhancement, Retainment, and Generalization


Proportional myoelectric control has been proposed for user-friendly interaction with prostheses, orthoses, and new human-machine interfaces. Recent research has stressed intuitive controls that mimic human intentions. However, these controls have restricted accuracy and functionality, resulting in user-specific decoders with higher-bound constraints on performance. Thus, myoelectric controls have yet to comprehend their potential as a natural interface between humans and multifunctional robotic controls. This study supports a shift in myoelectric management schemes toward proportional simultaneous controls learned through the development of unique muscle synergies. A multiple day study reveals natural emergence of a brand new muscle synergy house as subjects identify the system dynamics of a myoelectric interface. These synergies correlate with long-term learning, increasing performance over consecutive days. Synergies are maintained once one week, helping subjects retain efficient management and generalize performance to new tasks. The extension to robot management is also demonstrated with a robot arm performing reach-to-grasp tasks in a plane. The power to boost, retain, and generalize management, while not desperate to recalibrate or retrain the system, supports control schemes promoting synergy development, not essentially user-specific decoders trained on a subset of existing synergies, for efficient myoelectric interfaces designed for long-term use.

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