Needle tip force estimation by deep learning from raw spectral OCT data

Abstract Purpose Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly integrated into the needle tip. Optical coherence tomography (OCT) can be used to image tissue. Here, we study how to calibrate OCT to sense forces, e.g., during robotic needle placement. Methods We investigate whether using raw spectral OCT data without a typical image reconstruction can improve a deep learning-based calibration between optical signal and forces. For this purpose, we consider three different needles with a new, more robust design which are calibrated using convolutional neural networks (CNNs). We compare training the CNNs with the raw OCT signal and the reconstructed depth profiles. Results We find that using raw data as an input for the largest CNN model outperforms the use of reconstructed data with a mean absolute error of 5.81 mN compared to 8.04 mN. Conclusions We find that deep learning with raw spectral OCT data can improve learning for the task of force estimation. Our needle design and calibration approach constitute a very accurate fiber-optical sensor for measuring forces at the needle tip.

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Highly Dense FBG Temperature Sensor Assisted with Deep Learning Algorithms

Sensors ◽

10.3390/s21186188 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6188

Author(s):

Alexey Kokhanovskiy ◽

Nikita Shabalov ◽

Alexandr Dostovalov ◽

Alexey Wolf

Keyword(s):

Optical Sensors ◽

Temperature Sensor ◽

Reflectance Spectrum ◽

Absolute Error ◽

Experimental Calibration ◽

Fbg Sensor ◽

Fiber Optical ◽

Fiber Optical Sensors ◽

Fbg Sensors ◽

Calibration Setup

In this paper, we demonstrate the application of deep neural networks (DNNs) for processing the reflectance spectrum from a fiberoptic temperature sensor composed of densely inscribed fiber Bragg gratings (FBG). Such sensors are commonly avoided in practice since close arrangement of short FBGs results in distortion of the spectrum caused by mutual interference between gratings. In our work the temperature sensor contained 50 FBGs with the length of 0.95 mm, edge-to-edge distance of 0.05 mm and arranged in the 1500–1600 nm spectral range. Instead of solving the direct peak detection problem for distorted signal, we applied DNNs to predict temperature distribution from entire reflectance spectrum registered by the sensor. We propose an experimental calibration setup where the dense FBG sensor is located close to an array of sparse FBG sensors. The goal of DNNs is to predict the positions of the reflectance peaks of the reference sparse FBG sensors from the reflectance spectrum of the dense FBG sensor. We show that a convolution neural network is able to predict the positions of FBG reflectance peaks of sparse sensors with mean absolute error of 7.8 pm that is slightly higher than the hardware reused interrogator equal to 5 pm. We believe that dense FBG sensors assisted with DNNs have a high potential to increase spatial resolution and also extend the length of a fiber optical sensors.

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Modelling Light Transmission in a Fiber-Optical Reflection System

Nonlinear Analysis Modelling and Control ◽

10.15388/na.2004.9.1.15170 ◽

2004 ◽

Vol 9 (1) ◽

pp. 55-63

Author(s):

V. Kleiza

Keyword(s):

Optical Sensors ◽

Light Transmission ◽

External Cavity ◽

Linear Displacement ◽

Fiber System ◽

Light Emitting ◽

Optical Media ◽

Reflected Light ◽

Fiber Optical ◽

Fiber Optical Sensors

Light transmission in the reflection fiber system, located in external optical media, has been investigated for application as sensors. The system was simulated by different models, including external cavity parameters such as the distance between light emitting and receiving fibers and mirror positioning distance. The sensitivity to a linear displacement of the sensors was studied as a function of the distance between the tips of the light emitting fiber and the center of the pair reflected light collecting fibers, by positioning a mirror. Physical fundamentals and operating principles of the advanced fiber optical sensors were revealed.

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A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽

10.3390/s21113719 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3719

Author(s):

Aoxin Ni ◽

Arian Azarang ◽

Nasser Kehtarnavaz

Keyword(s):

Heart Rate ◽

Deep Learning ◽

Mean Absolute Error ◽

Absolute Error ◽

Video Camera ◽

Measurement Methods ◽

Rate Measurement ◽

Learning Methods ◽

The Public ◽

Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽

10.3390/vibration4020022 ◽

2021 ◽

Vol 4 (2) ◽

pp. 341-356

Author(s):

Jessada Sresakoolchai ◽

Sakdirat Kaewunruen

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Mean Absolute Error ◽

Absolute Error ◽

Machine Learning Techniques ◽

Rolling Stock ◽

Raw Data ◽

Learning Techniques ◽

Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

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Acoustic emission detection with fiber optical sensors for dry cask storage health monitoring

10.1117/12.2219337 ◽

2016 ◽

Author(s):

Bin Lin ◽

Jingjing Bao ◽

Lingyu Yu ◽

Victor Giurgiutiu

Keyword(s):

Acoustic Emission ◽

Health Monitoring ◽

Optical Sensors ◽

Fiber Optical ◽

Fiber Optical Sensors ◽

Emission Detection

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Dynamic Rotor Blade Displacement Tracking with Fiber-Optical Sensors for a Health and Usage Monitoring System

18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference ◽

10.2514/6.2017-3659 ◽

2017 ◽

Author(s):

Soeren Suesse

Keyword(s):

Monitoring System ◽

Optical Sensors ◽

Rotor Blade ◽

Fiber Optical ◽

Fiber Optical Sensors

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Development of on-fiber optical sensors utilizing chromogenic materials

10.1117/12.335755 ◽

1999 ◽

Cited By ~ 2

Author(s):

Jianming Yuan ◽

Mahmoud A. El-Sherif

Keyword(s):

Optical Sensors ◽

Fiber Optical ◽

Fiber Optical Sensors

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Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Sensors ◽

10.3390/s20082424 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2424 ◽

Cited By ~ 2

Author(s):

Md Atiqur Rahman Ahad ◽

Thanh Trung Ngo ◽

Anindya Das Antar ◽

Masud Ahmed ◽

Tahera Hossain ◽

...

Keyword(s):

Deep Learning ◽

Age Estimation ◽

Prediction Error ◽

Mean Absolute Error ◽

Absolute Error ◽

Training Dataset ◽

Wearable Sensor ◽

Age And Gender ◽

Test Dataset ◽

And Gender

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

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