Minority Predoctoral Fellowship Program

描述（由申请人提供）：植入式肌电传感器（IMES）技术的持续发展增加了肌电图（EMG）控制信号的数量和完整性。这项研究计划的长期愿景是重建手/手腕假肢的自然运动行为。具体来说，我们建议研究完整的中枢和周围神经系统在控制复杂的伸手抓握动作时使用简化策略和姿势协同作用。受试者将执行伸手动作来抓取日常任务环境中发现的物体，需要不同的抓取模式。在抓取过程中，将记录多达十块外源性手部肌肉的肌内肌电图数据，以及手部和手腕的运动数据。独立成分分析技术将用于分析肌电图和运动学数据，以确定是否存在简单的姿势协同作用。这些简化策略也将在截肢者控制复杂的手/腕假体中进行研究。通过创建一个像完整的运动控制系统一样实现简化策略的假体控制器，希望可以在不增加用户精神负荷的情况下为手/手腕假体添加更多功能。

DESCRIPTION (provided by applicant): Ongoing development of Implantable Myoelectric Sensors (IMES) technology increases the number and integrity of electromyographic (EMG) control signals. The long-term vision of this research proposal is to recreate construction of natural motor behavior in hand/wrist prostheses. Specifically, it is proposed to investigate the use of simplifying strategies and postural synergies by the intact central and peripheral nervous systems in controlling complex reach-to-grasp movements. Subjects will perform reaching movements to grasp objects found in an everyday task environment, requiring different grasp patterns. Intramuscular EMG data from up to ten extrinsic hand muscles, along with kinematic hand and wrist data, will be recorded during grasping. Independent component analysis techniques will be used to analyze EMG and kinematic data to determine the existence of simple postural synergies. These simplifying strategies will also be investigated in amputees for controlling complex hand/wrist prostheses. By creating a prosthesis controller that implements simplifying strategies much like the intact motor control system, it is hoped that more functionality can be added to hand/wrist prostheses without increasing the user's mental load.