Augmentation of sensitivity of FBG strain sensor for biomedical operation

Recent advancements of fiber Bragg grating sensors in biomedical application: a review

Due to attractive application in the medical field, fiber Bragg grating sensor has become increasing attractive from past few decades for various strain sensing applications. FBG sensor has been used in many applications such as different surgical devices, vital sign detection devices, invasive surgery, heart rate, dental applications and biosensing application as wearable sensing devices. This paper reviews the 55 recent research articles published on fiber Bragg grating sensor for biomedical application used the qualitative, quantitative and experimental method to identify the recent advancement and challenges. In this study, particular focus is placed on applications for biomechanical devices, temperature monitors, respiratory monitors, and biosensing applications. Critical things, demands, and emerging trends for these sensing devices are also discussed in order to determine what will be needed for the next generation.

Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.

Augmentation of sensitivity of FBG strain sensor for biomedical operation: publisher's note

This publisher's note reports a correction to Appl. Opt.57, 6906 (2018)APOPAI0003-693510.1364/AO.57.006906.

Ultrahigh-resolution interrogation of a fiber Bragg grating sensor based on higher order four-wave mixing

The performance of a highly sensitive strain sensor based on nonlinear four-wave mixing (FWM) using a fiber Bragg grating (FBG) is investigated. The power change due to the wavelength shift induced by very small strain over the FBG is significantly magnified by a higher order FWM process. Strain sensitivity of 5.547  dBm/μϵ is achieved and minimum wavelength shift of 2.18×10^-6  nm (which corresponds to a strain of 1.80×10^-3  μϵ) is detected with uniform FBG, whereas in case of chirped FBG (CFBG) strain sensitivity of 0.3  dBm/μϵ with minimum detectable wavelength shift of 4×10^-5  nm (which corresponds to a strain of 0.033  μϵ) is obtained.