Minority Predoctoral Fellowship Program

描述(由申请人提供)：植入式肌电传感器(IMES)技术的不断发展增加了肌电(EMG)控制信号的数量和完整性。这项研究提案的长期愿景是重建手/腕假体中自然运动行为的结构。具体地说，建议研究在控制复杂的伸展抓握运动中，完整的中枢和外周神经系统使用简化策略和姿势协同效应。受试者将执行伸手动作来抓取在日常任务环境中发现的物体，这需要不同的抓取模式。在抓握过程中，将记录最多10块手部外在肌肉的肌内肌电数据，以及手部和手腕的运动学数据。独立成分分析技术将用于分析肌电和运动学数据，以确定是否存在简单姿势协同效应。这些简化策略也将在截肢者中进行研究，以控制复杂的手/手腕假体。通过创造一种实现简化策略的假肢控制器，就像完整的电机控制系统一样，人们希望可以在不增加用户精神负担的情况下，为手/手腕假肢添加更多功能。

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.