利用肌电信号进行运动康复的手指外骨骼的设计与开发

Meghdeep Jana, B. Barua, S. Hazarika
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引用次数: 2

摘要

机器人设备可以通过增加治疗频率和消除对物理治疗师的需求来加速受损患者的康复过程。本文介绍了一种新型的人类手指外骨骼的发展,为中风后患者的运动康复提供帮助,为患者主动屈曲手指提供所需的支持。所提出的设计包括两个自由度,七连杆连接机构,其中两个连杆连接到近端和远端指骨,并由两个电动线性执行器驱动,以实现紧凑的尺寸。在交互式几何软件上合成了该机构,使其遵循自然关节轨迹。运动学分析表明,外骨骼与理想轨迹的偏差很小,有限元分析表明,在最大载荷条件下,外骨骼具有良好的结构刚度。文中进一步介绍了样机的机电系统和软件体系结构。该原型利用肌电信号来检测人的意图,并通过开发的基于意图的控制算法提供按需辅助康复的能力。该算法利用信号处理和统计技术从肌电图数据中识别模式。该分类器的人类意图检测准确率接近90%,在真实人类手指上的测试结果表明,该分类器在机器人辅助人类手部抓取任务康复中的临床应用潜力巨大。
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Design and Development of a Finger Exoskeleton for Motor Rehabilitation using Electromyography Signals
Robotic devices can accelerate the rehabilitation process of an impaired patient by increasing the frequency of treatment and eliminating the need for a physiotherapist. This paper presents the development of a novel human finger exoskeleton for the motor rehabilitation of post-stroke patients by providing assistance-as-required support to patients in active flexion of the finger. The proposed design comprises of a two degrees of freedom, seven-link concatenated mechanism with two links attached to proximal and distal phalanges and actuated by two electric linear actuators for compact size. We synthesized the mechanism on an interactive geometry software to make the mechanism follow natural joint trajectories. Kinematic analysis of the exoskeleton showed very low deviation from ideal trajectory and finite element analyses showed good structural rigidity under maximum load conditions. The paper further describes the electro-mechanical system and software architecture of the prototype. The prototype makes use of electromyography signal to detect human intention and has the ability to provide assistance-as-required rehabilitation through a developed intention based control algorithm. The algorithm makes use of signal processing and statistical techniques to recognise patterns from electromyography data. A classifier was modelled with human intention detection accuracy of nearly 90% The results of the testing on real human finger demonstrate the potential for clinical applications in the robot-assisted rehabilitation of the human hand for grasping tasks.
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