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Active tracking movements of flexion and extension on the elbow joint for an initial prototype of EMG-driven assistant exoskeleton

机译:EMG驱动的辅助外骨骼的初始原型主动跟踪肘关节屈伸的运动

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Various exoskeletons have been developed for the assistance of upper and lower limbs rehabilitation, however, therapists and doctors have pointed out that passive-repeating motions of limbs is ineffective in terms of neurological recovery. To improve the neurological recovery of human motor control, active-assisted encouragement as therapist and doctors manually provided should also be simulated with help of exoskeleton targeting for rehabilitative purpose. In this paper, a single-degree-of-freedom (SDOF) robotic joint to mimic elbow movements is developed and EMG signals are collected using as a bio-feedback signal. A pair of muscles including biceps and triceps that involved in flexion and extension on the elbow joint are selected, acquisition and processing of EMG signals is then achieved by designed hardware filtering and proposed software algorithms. By analysis of the resultant EMG signals, the intentions of flexion and extension movements are quantitatively presented. Online control commands for the exoskeleton are then spontaneously produced. A close-loop control with EMG-driven in real time is applied on an initial prototype of human-machine interface (HMI) for an active assistance using the exoskeleton. Initial applications on the experimental prototype have proved the effectiveness of the proposed active tracking method that has successfully tracking effort that bridge the wearer and the wearable exoskeleton together.
机译:已经开发出各种外骨骼来辅助上肢和下肢的康复,但是,治疗师和医生已经指出,被动重复肢体的运动在神经功能恢复方面是无效的。为了改善人类运动控制的神经功能恢复,还应在外骨骼靶向的帮助下模拟作为治疗师和手动提供的医生的主动辅助鼓励措施。在本文中,开发了一种模拟肘部运动的单自由度(SDOF)机器人关节,并使用EMG信号作为生物反馈信号进行了收集。选择包括肱二头肌和肱三头肌在内的一对涉及肘关节屈伸的肌肉,然后通过设计的硬件过滤和建议的软件算法实现对EMG信号的采集和处理。通过分析所得的EMG信号,定量地显示了屈伸运动的意图。然后自动生成外骨骼的在线控制命令。带有EMG实时驱动的闭环控制被应用于人机界面(HMI)的初始原型,以使用外骨骼进行主动辅助。在实验原型上的最初应用已经证明了所提出的主动跟踪方法的有效性,该方法已经成功地跟踪了将佩戴者和可穿戴外骨骼桥接在一起的努力。

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