Surface Myoelectric Control of Hand Prothetics

手部假肢的表面肌电控制

• 批准号: 7537333
• 负责人: DAVID Lee SHERMAN
• 金额: $ 27.01万
• 依托单位: INFINITE BIOMEDICAL TECHNOLOGIES, LLC
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7537333
• 关键词: Address; Algorithms; Amplifiers; Amputees; Biological Neural Networks; Classification; Clinical; Code; Computer software; Computers; Confusion; Cosmetics; Cues; Data; Development; Drops; Electrodes; Electromyography; Fingers; Foundations; Frequencies; Future; Generations; Goals; Hand; Hand functions; Individual; Learning; Left; Life; Limb structure; Mechanics; Motion; Movement; Muscle; Neurons; Numbers; Output; Patient Education; Patients; Pattern; Performance; Persons; Phase; Prospective Studies; Prosthesis; Public Health; Quality of life; Radial; Residual state; Rotation; Running; Signal Transduction; Surface; System; Technology; Testing; Time; Today; Training; Translating; United States; Upper Extremity; Validation; Visual; Work; Wrist; base; design; desire; feeding; grasp; improved; independent component analysis; next generation; prototype; psychosocial; research study; response; success

DESCRIPTION (provided by applicant): There are approximately 500,000 upper extremity amputees currently living in the United States; with 18,000 new upper extremity amputees added each year. The loss of an upper limb causes a person's quality of life to plummet and brings about massive physical and psychosocial challenges. The majority of amputees are hampered by restricted functionality and use a mechanical hook or a passive, cosmetic hand. Electric Grabber hands are available, but their use is limited due to a cumbersome control mechanism. Therefore, we propose to develop a noninvasive Surface EMG Decoder And Controller (SEDAC) for use in currently available Electric Grabber hands. It employs a feature extractor and an artificial neural network classifier to estimate intended hand movements. This will enable intuitive control of the prosthetic hand. Our Phase I effort is focused on the development and validation of SEDAC for real-time decoding and control of a two-function prosthesis. This offers three key advantages over current technology: 1) intuitive activation of muscle groups for each kind of movement; 2) smooth transition from one movement to another; and 3) a learning capacity which transfers the burden of training from the patient to the prosthetic. Our Phase II effort builds upon this to incorporate dimensionality reducing algorithms to improve accuracy, reduce latency, and enable intuitive control of 4 additional hand functions. This will allow for actuation of next- generation dexterous prosthetic hands which are currently under development. Through these advances, we hope to bring about a much needed improvement in the quality of life for upper extremity amputees. PUBLIC HEALTH RELEVANCE: This project will provide trans-radial amputees with intuitive control of current generation prosthetics using surface electromyography (EMG). The technology will also provide a foundation for surface EMG control of fully dexterous prosthetics.

描述（由申请人提供）：目前约有500，000名上肢截肢者居住在美国;每年新增18，000名上肢截肢者。失去上肢会导致一个人的生活质量下降，并带来巨大的身体和心理社会挑战。大多数截肢者受到功能限制的阻碍，使用机械钩或被动的美容手。电动抓取手是可用的，但它们的使用是有限的，由于繁琐的控制机制。因此，我们建议开发一种非侵入性的表面肌电解码器和控制器（SEDAC），用于目前可用的电动抓斗手。它采用了一个特征提取器和人工神经网络分类器来估计预期的手部动作。这将使得能够直观地控制假手。我们的第一阶段工作重点是开发和验证SEDAC，用于实时解码和控制双功能假体。与现有技术相比，这提供了三个关键优势：1）针对每种运动的肌肉群的直观激活; 2）从一种运动到另一种运动的平滑过渡;以及3）将训练负担从患者转移到假肢的学习能力。我们的第二阶段工作以此为基础，纳入降维算法，以提高准确性、减少延迟，并实现对4种额外手部功能的直观控制。这将允许目前正在开发的下一代灵巧假手的驱动。通过这些进步，我们希望为上肢截肢者的生活质量带来急需的改善。公共卫生关系：该项目将利用表面肌电描记术（EMG）为经桡动脉截肢者提供对当前一代假肢的直观控制。该技术还将为全灵巧假肢的表面肌电控制提供基础。