Minority Predoctoral Fellowship Program

少数族裔博士前奖学金计划

• 批准号: 7174206
• 负责人: ABIDEMI BOLU AJIBOYE
• 金额: $ 2.83万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7174206
• 关键词: arm; assistive device /technology; bioengineering /biomedical engineering; body movement; clinical research; data collection methodology /evaluation; electromyography; electronic stimulator; hand; human subject; implant; medical implant science; muscle function; predoctoral investigator; skeletal prosthesis; wrist

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.

描述（由申请人提供）：植入式肌电传感器（IMES）技术的持续开发增加了肌电（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/腕关节假体的自然运动行为。具体而言，它建议调查使用简化策略和姿势的协同作用，由完整的中枢和外周神经系统控制复杂的达到把握运动。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。在抓握过程中，将记录来自多达10个外部手部肌肉的肌内EMG数据，沿着手部和腕部的运动学数据。独立成分分析技术将用于分析EMG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/腕关节假体。通过创建一个假肢控制器，实现简化的策略很像完整的电机控制系统，希望更多的功能可以添加到手/手腕假肢，而不增加用户的精神负荷。