Development and Characterization of Compliant FBG-Based, Shear and Normal Force Sensing Elements for Biomechanical Applications

被引:6
作者
Al-Mai, Osama [1 ]
Albert, Jacques [2 ]
Ahmadi, Mojtaba [1 ]
机构
[1] Carleton Univ, Dept Mech & Aersopace Engn, Ottawa, ON K1S 5B6, Canada
[2] Carleton Univ, Dept Elect, Ottawa, ON K1S 5B6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Normal force measurements; compliant sensors; fiber-optic; shear force sensing strategies; fiber Bragg grating based; rehabilitation system; biomechanics; NEURAL-NETWORK MODEL; PRESSURE SENSOR; TACTILE SENSOR; INTERFACE PRESSURES; IDENTIFICATION; AMPUTEES; STRESSES; SYSTEM; CALIBRATION; VALIDATION;
D O I
10.1109/JSEN.2020.2969866
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents the development process and the evaluation of compliant Fiber Bragg Gratings (FBG) based, normal and shear force sensing elements for biomechanical measurements with their effective novel calibration methodology and preliminary experimental results. The sensing elements are intended for human machine interaction such as in transfemoral prosthetic interface. The sensing elements are designed to utilize the generated deflections due to the applied force to stretch the optical fiber when a normal or shear force is applied on the sensor's top surface. The performance of the sensor is evaluated through a series of experiments including both dynamic and static loading conditions. The experimental results show that the fabricated sensing elements have the ability to measure applied normal and shear force. Due to the deformable material used, the sensor exhibits slightly nonlinear behaviour between force and deformation. This has been addressed using a novel calibration procedure composed of a linear model to characterize the main sensor data and a nonlinear estimation model based on a nonlinear autoregressive exogenous (NARX) model to simultaneously estimate the errors from the input wavelength data in real-time. The results achieved from the proposed calibration method have revealed an improvement from an R-squared value of 93% to 100% when compared to a data obtained using a linear least squares method.
引用
收藏
页码:5176 / 5186
页数:11
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