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用于受软协同启发的肌电假手的手部-物体交互过程中抓握力精细控制的改进

Improving Fine Control of Grasping Force during Hand-Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand.

作者信息

Fu Qiushi, Santello Marco

机构信息

Neural Control of Movement Laboratory, School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States.

Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, United States.

出版信息

Front Neurorobot. 2018 Jan 10;11:71. doi: 10.3389/fnbot.2017.00071. eCollection 2017.

Abstract

The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional single-gain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pick-and-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands.

摘要

人体手部姿势协同的概念已被证明可能会降低抓握神经肌肉控制的复杂性。通过将这一概念与软机器人技术方法相结合,创建了一种多自由度软协同假手[SoftHand-Pro(SHP)]。SHP的机械创新使得仅通过两个表面肌电图(sEMG)通道进行简单直观的肌电控制就能实现自适应且稳健的功能性抓握。然而,当前的肌电控制器在抓握力精细控制方面的能力非常有限。我们通过设计一种混合增益肌电控制器来应对这一挑战,该控制器根据SHP的感觉运动状态切换控制增益。该控制器针对传统单增益(SG)控制器以及健全受试者的自然手进行了测试。我们使用以下任务来评估抓握力控制的性能:(1)使用强力或精确抓握拿起并放置不同尺寸、重量和易碎程度的物体,以及(2)挤压不同硬度的物体。抓握力的感觉反馈通过安装在上臂的非侵入式机械触觉触觉反馈装置提供给用户。我们证明,在物体拾取和放置任务中,新型混合控制器在任务完成速度和精细力控制方面优于SG控制器。我们还发现,混合控制器的性能在质量上与人类自然手的性能相符。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ae2/5767584/ff937ae21d98/fnbot-11-00071-g001.jpg

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