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An FBG-Based Sensing Glove to Measure Dynamic Finger Flexure With an Angular Resolution of 0.1

机译:基于FBG的传感手套,可测量角度分辨率为0.1的动态手指弯曲

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摘要

We demonstrate a fiber Bragg grating (FBG) sensor-based highly accurate instrumented glove that can measure finger flexure with an angular resolution of 0.1 degrees. The sensing unit consists of an FBG that is very sensitive to axial strain induced by the flexing of fingers. The spectral shift of the reflection spectrum of the FBG varies linearly with the joint rotation angle. The sensor offers a very high angular resolution of 0.1 degrees with a very high sensitivity of 18.45 pm/degree. The accuracy evaluated using a mechanical setup, and the human hand is 0.13 degrees and 0.67 degrees, respectively. This is much better than many other reported sensors. The sensor showed excellent repeatability with a maximum standard deviation of 030 degrees and 0.79 degrees on a mechanical setup and the human hand, respectively. The results are validated using a precalibrated inertial measurement unit (IMU) sensor. The sensor also exhibited much better dynamic response compared to the IMU upto a rotation speed of 80 degrees/s. These results demonstrate that our sensor is a strong potential candidate for the development of high-accuracy instrumented gloves that could be used to monitor the progress in the rehabilitation of stroke survivors. This paper focuses on the careful characterization of the sensor to establish it as a sensitive and practical approach. The accuracy, repeatability, and dynamic response were evaluated using a motorized mechanical model of a finger joint and also on the hands of two human subjects.
机译:我们演示了基于光纤布拉格光栅(FBG)传感器的高精度仪器手套,该手套可以以0.1度的角分辨率测量手指的弯曲度。传感单元由一个FBG组成,该FBG对手指弯曲引起的轴向应变非常敏感。 FBG的反射光谱的光谱偏移随关节旋转角度线性变化。该传感器可提供0.1度的极高角度分辨率,并具有18.45 pm /度的极高灵敏度。使用机械设置评估的准确度,人的手分别为0.13度和0.67度。这比许多其他报告的传感器要好得多。该传感器在机械装置和人手上显示出极好的可重复性,最大标准偏差分别为030度和0.79度。使用预校准的惯性测量单元(IMU)传感器验证结果。与IMU相比,传感器在高达80度/秒的旋转速度下也表现出更好的动态响应。这些结果表明,我们的传感器是开发高精度仪器手套的强大潜力,可用于监测中风幸存者的康复进度。本文着重于对传感器的仔细表征,以将其确立为一种灵敏而实用的方法。使用手指关节以及两个人类受试者手上的电动机械模型评估准确性,可重复性和动态响应。

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