Soft Robotic Glove with Integrated Sensing for Intuitive Grasping Assistance Post Spinal Cord Injury

Yu Meng Zhou, Diana Wagner, Kristin Nuckols, Roman Heimgartner, Carolina Correia, Megan E. Clarke, D. Orzel, Ciarán T. O’Neill, Ryan Solinsky, S. Paganoni, C. Walsh
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引用次数: 34

Abstract

This paper presents a fully-integrated soft robotic glove with multi-articular textile actuators, custom soft sensors, and an intuitive state machine intent detection controller. We demonstrate that the pressurized actuators can generate motion and force comparable to natural human fingers through bench-top testing. We apply textile-elastomer capacitive sensors to the glove to track finger flexion via strain and detect contact with objects via force. Intuitive user control is achieved via a state machine controller based on signals from the integrated sensors to detect relative changes in hand-object interactions. Results from an initial evaluation with 3 participants with spinal cord injury (SCI), of varied injury levels and years since injury, wearing and controlling the glove show an average of 87% improvement in grasping force, and improvements in functional assessments for participants with recent injuries. A significant variation in response suggests further investigation is required to understand the adaptation needed across different injury levels and durations since injury. Additionally, we evaluate the controller and find an average of 3 seconds from user initiations to completed grasps, and 10% inadvertent grasp triggers and no false releases when objects are held.
集成传感的软机器人手套用于脊髓损伤后的直观抓取辅助
本文提出了一种完全集成的柔性机器人手套,它具有多关节纺织致动器、定制软传感器和直观的状态机意图检测控制器。通过台架测试,我们证明了加压致动器可以产生与自然人类手指相当的运动和力。我们将纺织弹性体电容传感器应用于手套,通过应变来跟踪手指的弯曲,并通过力来检测与物体的接触。直观的用户控制是通过基于集成传感器信号的状态机控制器来实现的,以检测手-物体交互的相对变化。对3名脊髓损伤(SCI)参与者的初步评估结果显示,不同损伤程度和损伤后的年份,佩戴和控制手套的参与者在抓握力方面平均有87%的改善,最近受伤的参与者在功能评估方面也有改善。反应的显著差异表明,需要进一步研究以了解不同损伤水平和损伤后持续时间所需的适应。此外,我们对控制器进行了评估,发现从用户启动到完成抓取的平均时间为3秒,10%的无意抓取触发,当物体被握住时没有错误释放。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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