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Ultra-sensitive and resilient compliant strain gauges for soft machines

机译：用于软机的超敏和弹性柔性应变仪

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摘要

Strain gauges with both high sensitivity and high mechanical resilience, based on strain-mediated contact in anisotropically resistive structures, are demonstrated within a sensor-integrated, textile-based sleeve that can recognize human hand motions via muscle deformations.Soft machines are a promising design paradigm for human-centric devices(1,2) and systems required to interact gently with their environment(3,4). To enable soft machines to respond intelligently to their surroundings, compliant sensory feedback mechanisms are needed. Specifically, soft alternatives to strain gauges-with high resolution at low strain (less than 5 per cent)-could unlock promising new capabilities in soft systems. However, currently available sensing mechanisms typically possess either high strain sensitivity or high mechanical resilience, but not both. The scarcity of resilient and compliant ultra-sensitive sensing mechanisms has confined their operation to laboratory settings, inhibiting their widespread deployment. Here we present a versatile and compliant transduction mechanism for high-sensitivity strain detection with high mechanical resilience, based on strain-mediated contact in anisotropically resistive structures (SCARS). The mechanism relies upon changes in Ohmic contact between stiff, micro-structured, anisotropically conductive meanders encapsulated by stretchable films. The mechanism achieves high sensitivity, with gauge factors greater than 85,000, while being adaptable for use with high-strength conductors, thus producing sensors resilient to adverse loading conditions. The sensing mechanism also exhibits high linearity, as well as insensitivity to bending and twisting deformations-features that are important for soft device applications. To demonstrate the potential impact of our technology, we construct a sensor-integrated, lightweight, textile-based arm sleeve that can recognize gestures without encumbering the hand. We demonstrate predictive tracking and classification of discrete gestures and continuous hand motions via detection of small muscle movements in the arm. The sleeve demonstration shows the potential of the SCARS technology for the development of unobtrusive, wearable biomechanical feedback systems and human-computer interfaces.
机译：基于菌株介导的各向异性电阻结构的应变介导的接触具有高灵敏度和高机械弹性的应变仪在传感器集成的纺织品的套筒内进行说明，可以通过肌肉变形识别人类手动运动。徽令是一个有前途的设计用于以人为本的设备（1,2）和用环境轻轻地交互的系统（3,4）。为了使软机能够智能地响应周围环境，因此需要兼容的感官反馈机制。具体而言，在低菌株（小于5％）下具有高分辨率的软替代品 - 在软系统中解锁有希望的新功能。然而，目前可用的传感机制通常具有高应变敏感性或高机械弹性，但并不是两者。弹性和柔性的超敏传感机制的稀缺性将其运作局限于实验室环境，抑制了他们广泛的部署。在这里，我们基于在各向异性电阻结构（疤痕）中的应变介导的接触，提出了一种具有高机械弹性的高灵敏度应变检测的通用和柔顺的转导机制。该机构依赖于封装在拉伸薄膜封装的欧姆，微结构的各向异性导电叶片之间的欧姆接触的变化。该机制达到高灵敏度，规格因子大于85,000，同时适用于高强度导体，从而产生传感器弹性对不利的负载条件。传感机制还表现出高线性度，以及对软件应用重要的弯曲和扭曲变形的不敏感性。为了展示我们技术的潜在影响，我们构建了一种传感器集成，轻质，纺织品的臂套，可以识别手势而不会缠绕手。我们通过检测手臂中的小肌肉运动来展示采用离散手势和连续手动运动的预测跟踪和分类。袖子示范显示了疤痕技术的潜力，用于开发不引人注心，可穿戴的生物力学反馈系统和人机接口。

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  • 来源
    《Nature》 |2020年第7833期|219-224|共6页
  • 作者

    Araromi Oluwaseun A.; Graule Moritz A.; Dorsey Kristen L.; Castellanos Sam; Foster Jonathan R.; Hsu Wen-Hao; Passy Arthur E.; Vlassak Joost J.; Weaver James C.; Walsh Conor J.; Wood Robert J.;

  • 作者单位

    Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA;

    Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA;

    Smith Coll Northampton MA 01063 USA;

    Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA;

    Wyss Inst Biol Inspired Engn Boston MA 02115 USA;

    Wyss Inst Biol Inspired Engn Boston MA 02115 USA;

    Ecole Polytech Fed Lausanne Sch Engn Lausanne Switzerland;

    Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA;

    Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA|Wyss Inst Biol Inspired Engn Boston MA 02115 USA;

    Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA|Wyss Inst Biol Inspired Engn Boston MA 02115 USA;

    Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA|Wyss Inst Biol Inspired Engn Boston MA 02115 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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