Body Worn EMG Controlling of Assistive Robotic Arm for Stroke Rehabilitation

描述（由申请人提供）：中风是美国成年人残疾的主要原因，也是第三大死亡原因。世界上每年大约有1500万人中风，美国有超过70万人中风。脑半球中风后，身体一侧肢体的运动控制通常会受到影响。许多患者在身体的对侧遭受各种致残的身体症状。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），如吃饭、穿衣、写作或打字。许多机电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）或主动的方式（当他们打算参与运动时）进行包括重复运动的麻痹肢体的练习。表面肌电图（EMG）信号包含丰富的运动控制信息，从中可以检测使用者的意图。然而，由于上肢的灵巧性，大多数功能性任务通常是通过多个肌肉复杂的时间和空间协调来完成的。通过（肌肉与自由度之间的）一对一映射来实现对这种多自由度的控制是不可行的。模式识别技术近年来在肌电控制系统的发展中受到越来越多的关注。最近，我们提出了一个新的框架，为中风幸存者使用高密度表面肌电记录和模式识别分析。我们的研究表明，在涉及患肢的多达20个手臂，手，手指/拇指运动的分类中可以获得较高的准确性，这表明肌电模式识别技术可以从中风受试者的麻痹肌肉中提取大量的运动控制信息。这些信息将潜在地使辅助装置的意志控制成为可能，从而促进受影响肢体的功能恢复。在第一阶段，我们将展示高密度肌电图系统用于机器人控制的可行性，使中风受试者能够自主控制辅助工具。在第二阶段，肌电控制系统将与辅助机器人完全集成，以实现更好的中风康复，并在患者群体中进行测试。

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.