在功能性抓取和释放过程中运动学和肌肉活动的预期信号

N. Beckers, R. Fineman, L. Stirling
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引用次数: 8

摘要

机器人辅助装置显示出使用表面肌电图(sEMG)作为控制信号来辅助手部功能的潜力。目前这些机器人系统的实现通常不包括与环境的交互,这在功能性任务中自然会发生。此外,在许多应用中,专家将表面肌电信号传感器放置在特定的肌肉上,这有利于非专家可能无法实现的精确对准。这项研究为使用运动学和非特定位置的表面肌电信号传感器控制抓取和释放物体的辅助装置的算法开发提供了信息。对象类型对握把孔径和关节运动学有显著影响。肌肉活动受到传感器位置的微小对齐变化的显著影响,但分析的特征显示了在抓取和释放之前的预期机制。在控制体系结构中适当地包含位置可变性可以与运动学和sEMG特征相结合,以告知对象类型并预测抓取和释放。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anticipatory signals in kinematics and muscle activity during functional grasp and release
Robotic assistive devices show potential to aid hand function using surface electromyography (sEMG) as a control signal. Current implementations of these robotic systems typically do not include interaction with the environment, which naturally occurs during functional tasks. Further, many applications have experts place the sEMG sensors on specific muscles, which benefits precision alignment that may not be possible by non-experts. This study informs algorithm development for controlling assistive devices for grasping and releasing objects using kinematics and non-specifically placed sEMG sensors. Significant effects of object type were found in the grip aperture and joint kinematics. Muscle activity was significantly affected by small alignment changes in the sensor placement, yet the features analyzed showed anticipatory mechanisms prior to grasp and release. The appropriate inclusion of placement variability within a control architecture can be coupled with the kinematics and sEMG features to inform object type and anticipate grasp and release.
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