抓住
任务(项目管理)
对象(语法)
人机交互
人工智能
计算机科学
肌电图
计算机视觉
工作(物理)
工作量
机械手
人机系统
控制(管理)
机器人
工程类
模拟
物理医学与康复
机械工程
系统工程
程序设计语言
操作系统
医学
作者
Taylor C. Hansen,Marshall A. Trout,Jacob L. Segil,David J. Warren,Jacob A. George
标识
DOI:10.1109/embc46164.2021.9629622
摘要
Multiarticulate bionic hands are now capable of recreating the endogenous movements and grip patterns of the human hand, yet amputees continue to be dissatisfied with existing control strategies. One approach towards more dexterous and intuitive control is to create a semi-autonomous bionic hand that can synergistically aid a human with complex tasks. To that end, we have developed a bionic hand that can automatically detect and grasp nearby objects with minimal force using multi-modal fingertip sensors. We evaluated performance using a fragile-object task in which participants must move an object over a barrier without applying pressure above specified thresholds. Participants completed the task under three conditions: 1) with their native hand, 2) with the bionic hand using surface electromyography control, and 3) using the semi-autonomous bionic hand. We show that the semi-autonomous hand is extremely capable of completing this dexterous task and significantly outperforms a more traditional surface-electromyography controller. Furthermore, we show that the semi-autonomous bionic hand significantly increased users' grip precision and reduced users' perceived task workload. This work constitutes an important step towards more dexterous and intuitive bionic hands and serves as a foundation for future work on shared human-machine control for intelligent bionic systems.
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