PROJECT TITLE :
High-Density Electromyography and Motor Skill Learning for Robust Long-Term
Myoelectric management offers an instantaneous interface between human intent and numerous robotic applications through recorded muscle activity. Traditional management schemes realize this interface through direct mapping or pattern recognition techniques. The former approach provides reliable control at the expense of functionality, whereas the latter increases functionality at the expense of long-term reliability. Another approach, using concepts of motor learning, provides session-independent simultaneous control, but previously relied on consistent electrode placement over biomechanically independent muscles. This paper extends the functionality and practicality of the motor learning-based mostly approach, using high-density electrode grids and muscle synergy-impressed decomposition to come up with management inputs with reduced constraints on electrode placement. The method is demonstrated via real-time simultaneous and proportional management of a 4-DoF myoelectric interface over multiple days. Subjects showed learning trends per typical motor skill learning without requiring any retraining or recalibration between sessions. Moreover, they adjusted to physical constraints of a robot arm after learning the control in a very constraint-free virtual interface, demonstrating strong control as they performed precision tasks. The results demonstrate the efficacy of the proposed man–machine interface as a viable different to traditional management schemes for myoelectric interfaces designed for long-term use.
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