骨骼肌变形的柔性电容传感和超声校准。

IF 6.4 2区 计算机科学 Q1 ROBOTICS
Jiajie Guo, Chuxuan Guo, Jialei Zhou, Kui Duan, Qining Wang
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引用次数: 1

摘要

骨骼肌是人体肢体运动动力学和能量学的关键,由于传感技术的实际限制,其机械状态很少在体外进行探索。本文旨在利用可穿戴柔性传感器捕捉肌肉收缩的机械变形,并通过模型校准和实验验证证明了这一点。电容式传感器采用导电织物电极与多孔介质层复合设计,增加了压力灵敏度,防止横向膨胀。这样,肌肉-传感器耦合模型就可以根据传感器变形和预张力、材料、形状等参数来捕获肌肉变形的压缩位移。传感模型是在一个线性形式校准使用超声医学成像。该传感器能够测量70%的肌肉张力,误差为
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flexible Capacitive Sensing and Ultrasound Calibration for Skeletal Muscle Deformations.

Skeletal muscles are critical to human-limb motion dynamics and energetics, where their mechanical states are seldom explored in vitro due to practical limitations of sensing technologies. This article aims to capture mechanical deformations of muscle contraction using wearable flexible sensors, which is justified with model calibration and experimental validation. The capacitive sensor is designed with the composite of conductive fabric electrodes and the porous dielectric layer to increase the pressure sensitivity and prevent lateral expansions. In this way, the compressive displacement of muscle deformation is captured in the muscle-sensor coupling model in terms of sensor deformation and parameters of pretension, material, and shape properties. The sensing model is calibrated in a linear form using ultrasound medical imaging. The sensor is capable of measuring muscle strain of 70% with an error of <3.6% and temperature disturbance of <5.6%. After 10K cycles of compression, the drift is only 3.3%. Immediate application of the proposed method is illustrated by gait pattern identification, where the K-nearest neighbor prediction accuracy of squats, level walking, stair ascent/descent, and ramp ascent is over 97% with a standard deviation below 2.6% compared to that of 94.61 ± 4.24% for ramp descent, and the response time is 14.37 ± 0.52 ms. The wearable sensing method is valid for muscle deformation monitoring and gait pattern identification, and it provides an alternative approach to capture mechanical motions of muscles, which is anticipated to contribute to understand locomotion biomechanics in terms of muscle forces and metabolic landscapes.

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来源期刊
Soft Robotics
Soft Robotics ROBOTICS-
CiteScore
15.50
自引率
5.10%
发文量
128
期刊介绍: Soft Robotics (SoRo) stands as a premier robotics journal, showcasing top-tier, peer-reviewed research on the forefront of soft and deformable robotics. Encompassing flexible electronics, materials science, computer science, and biomechanics, it pioneers breakthroughs in robotic technology capable of safe interaction with living systems and navigating complex environments, natural or human-made. With a multidisciplinary approach, SoRo integrates advancements in biomedical engineering, biomechanics, mathematical modeling, biopolymer chemistry, computer science, and tissue engineering, offering comprehensive insights into constructing adaptable devices that can undergo significant changes in shape and size. This transformative technology finds critical applications in surgery, assistive healthcare devices, emergency search and rescue, space instrument repair, mine detection, and beyond.
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