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首页> 外文期刊>Journal of Biomechanics >Calibration of a Hall effect displacement measurement system for complex motion analysis using a neural network.
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Calibration of a Hall effect displacement measurement system for complex motion analysis using a neural network.

机译：使用神经网络对用于复杂运动分析的霍尔效应位移测量系统进行校准。

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摘要

Biomechanics studies often require the analysis of position and orientation. Although a variety of transducer and camera systems can be utilized, a common inexpensive alternative is the Hall effect sensor. Hall effect sensors have been used extensively for one-dimensional position analysis but their non-linear behavior and cross-talk effects make them difficult to calibrate for effective and accurate two- and three-dimensional position and orientation analysis. The aim of this study was to develop and calibrate a displacement measurement system for a hydraulic-actuation joystick used for repetitive motion analysis of heavy equipment operators. The system utilizes an array of four Hall effect sensors that are all active during any joystick movement. This built-in redundancy allows the calibration to utilize fully connected feed forward neural networks in conjunction with a Microscribe 3D digitizer. A fully connected feed forward neural network with one hidden layer containing five neurons was developed.Results indicate that the ability of the neural network to accurately predict the x, y and z coordinates of the joystick handle was good with r(2) values of 0.98 and higher. The calibration technique was found to be equally as accurate when used on data collected 5 days after the initial calibration, indicating the system is robust and stable enough to not require calibration every time the joystick is used. This calibration system allowed an infinite number of joystick orientations and positions to be found within the range of joystick motion.
机译：生物力学研究通常需要分析位置和方向。尽管可以使用各种换能器和摄像头系统，但常见的廉价替代品是霍尔效应传感器。霍尔效应传感器已广泛用于一维位置分析，但是它们的非线性行为和串扰效应使它们难以校准，从而无法有效，准确地进行二维和三维位置和方向分析。本研究的目的是开发和校准用于重型设备操作员重复运动分析的液压致动操纵杆的位移测量系统。该系统利用四个霍尔效应传感器组成的阵列，这些传感器在任何操纵杆运动过程中均处于活动状态。这种内置的冗余功能使校准可以与Microscribe 3D数字化仪结合使用完全连接的前馈神经网络。建立了一个全连接的前馈神经网络，该网络具有一个包含五个神经元的隐藏层。结果表明，神经网络准确预测操纵杆手柄的x，y和z坐标的能力很好，r（2）值为0.98和更高。当在初始校准后5天收集的数据上使用校准技术时，发现该校准技术具有同等的准确性，这表明系统足够强大且稳定，因此每次使用操纵杆都不需要进行校准。该校准系统允许在操纵杆运动范围内找到无限数量的操纵杆方向和位置。

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