Machine-learning-assisted omnidirectional bending sensor based on a cascaded asymmetric dual-core PCF sensor
Bingsen Huang, Xinzhi Sheng, Jiaqi Cao, Haoqiang Jia, Wei Gao, Shuai Gu, Xin Wang, Paul K. Chu, Shuqin Lou
Abstract
An omnidirectional bending sensor comprising cascaded asymmetric dual-core photonic crystal fibers (ADCPCFs) is designed and demonstrated experimentally. Upon cascading and splicing two ADCPCFs at a lateral rotation angle, the transmission spectrum of the sensor becomes highly dependent on the bending direction. Machine learning (ML) is employed to predict the curvature and bending orientation of the bending sensor for the first time, to the best of our knowledge. The experimental results demonstrate that the ADCPCF sensor used in combination with machine learning can predict the curvature and omnidirectional bending orientation within 360° without requiring any post-processing fabrication steps. The prediction accuracy is 99.85% with a mean absolute error (MAE) of 2.7° for bending direction measurement and 98.08% with an MAE of 0.03 m −1 for the curvature measurement. This promising strategy utilizes the global features (full spectra) in combination with machine learning to overcome the dependence of the sensor on high-quality transmission spectra, the wavelength range, and a special wavelength dip in the conventional dip tracking method. This excellent omnidirectional bending sensor has large potential for structural health monitoring, robotic arms, medical instruments, and wearable devices.