Body Worn EMG Controlling of Assistive Robotic Arm for Stroke Rehabilitation

用于中风康复的辅助机械臂的穿戴式肌电图控制

• 批准号: 8589235
• 负责人: GARY D HAVEY
• 金额: $ 16.13万
• 依托单位: ADVANCED MEDICAL ELECTRONICS CORPORATION
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8589235
• 关键词: Activities of Daily Living; Adult; Affect; Age; Attention; Back; Cause of Death; Chicago; Classification; Clinical; Complex; Computer software; Computers; Consult; Data; Detection; Development; Devices; Digital Signal Processing; Discrimination; Doctor of Medicine; Doctor of Philosophy; Eating; Electrodes; Electromyography; Electronics; Engineering; Exercise; Fingers; Hand; Human; Impairment; Institutes; Intention; Limb structure; Maps; Measurement; Measures; Medical; Medical Device; Medical Electronics; Motor; Movement; Muscle; Noise; Paralysed; Patient Selection; Patients; Pattern Recognition; Performance; Phase; Physical Medicine; Post Technic; Principal Investigator; Process; Production; Rehabilitation therapy; Research; Research Personnel; Robot; Robotics; Scientist; Self-Help Devices; Sensory Motor Performances; Side; Signal Transduction; Sterile coverings; Stroke; Supervision; Surface; Survivors; Symptoms; System; Techniques; Testing; Thumb structure; Time; Training; United States; Universities; Upper Extremity; Work; Writing; arm; computerized data processing; data acquisition; density; design; disability; experience; functional restoration; health related quality of life; human subject; improved; instrumentation; limb movement; motor control; neuromuscular; novel; patient population; professor; programs; prototype; public health relevance; software development; stroke rehabilitation; tool

DESCRIPTION (provided by applicant): Stroke is the leading cause of adult disability and the third leading cause of death in the United States. Approximately 15 million people in the world, and more than 700,000 people in the United States, experience a stroke each year. Following a hemispheric stroke, motor control of extremities on one side of the body is usually affected. Many patients suffer a variety of disabling physical symptoms on the contralesional side of the body. In particular, upper limb (arm, hand, finger/thumb) dexterity is often affected, limiting fundamental activities of daily living (ADL) such as eating, dressing, writing or typing. A number of mechatronic devices have been designed as assistive tools for robot-aided stroke rehabilitation to improve upper limb function. Most of the devices can assist users to perform exercises which involve repetitive movement of their paretic limb in a passive way (as they relax), or in an active way (as they intend to contribute to the movement). Surface electromyogram (EMG) signals contain rich motor control information, from which the user's intention can be detected. Due to the upper-limb dexterity, however, most functional tasks are generally accomplished through complex temporal and spatial coordination of multiple muscles. It is unfeasible to realize the control of such multiple DOFs via one-to-one mapping (between a muscle and a DOF). Pattern recognition techniques have recently attracted increasing attention in the development of myoelectric control systems. Recently, we have presented a novel framework for stroke survivors using high density surface EMG recording and pattern recognition analysis. Our research demonstrates that high accuracies can be obtained in classification of up to 20 arm, hand, finger/thumb movements involving the affected limb, suggesting that with myoelectric pattern recognition techniques substantial motor control information can be extracted from the paretic muscles of stroke subjects. Such information will potentially enable volitional control of assistive devices, thereby facilitating the functional restoration for the affected limb. In phase I, we will demonstrate the feasibility of a high densit EMG system for robot control to allow stroke subjects volitional control of assistive tools. In phase II, the myoelectric control system will be fully integrated with the assistive robot for implementing improved stroke rehabilitation and tested on a patient population.

描述（由申请人提供）：中风是成人残疾的主要原因，也是美国第三大死亡原因。全世界每年约有1500万人，其中美国有70多万人经历中风。半球中风后，身体一侧肢体的运动控制通常受到影响。许多患者在身体的对侧病灶上遭受各种致残性身体症状。特别是，上肢（手臂、手、手指/拇指）的灵活性经常受到影响，限制了基本的日常生活活动（ADL），如进食、穿衣、写作或打字。许多机电一体化设备已被设计为机器人辅助中风康复的辅助工具，以改善上肢功能。大多数设备可以帮助用户以被动的方式（当他们放松时）或以主动的方式（当他们打算有助于运动时）进行涉及他们的麻痹肢体的重复运动的锻炼。表面肌电信号包含丰富的运动控制信息，可以从中检测用户的意图。然而，由于上肢的灵活性，大多数功能性任务通常通过多个肌肉的复杂的时间和空间协调来完成。通过一对一映射（肌肉和自由度之间的映射）来实现这种多自由度的控制是不可行的。近年来，模式识别技术在肌电控制系统的发展中引起了越来越多的关注。最近，我们提出了一个新的框架，中风幸存者使用高密度表面肌电图记录和模式识别分析。我们的研究表明，可以获得高精度的分类多达20个手臂，手，手指/拇指运动，涉及受影响的肢体，这表明与肌电模式识别技术，大量的运动控制信息可以从中风患者的瘫痪肌肉提取。这些信息将有可能使辅助设备的意志控制，从而促进患肢的功能恢复。在第一阶段，我们将展示一个高密度肌电系统的可行性，机器人控制，让中风科目的意志控制的辅助工具。在第二阶段，肌电控制系统将与辅助机器人完全集成，以实现改进的中风康复，并在患者人群中进行测试。