Force-controlled Transcranial Magnetic Stimulation (TMS) robotic system

The use of robots to assist neurologists in Transcranial Magnetic Stimulation (TMS) has the potential to improve the long term outcome of brain stimulation. Although extensive research has been carried out on TMS robotic system, no single study exists which adequately take into account the control of interaction of contact force between the robot and subject’s head. Thus, the introduction of force feedback control is considered as a desirable feature, and is particularly important when using an autonomous robot manipulator. In this study, a force-controlled TMS robotic system has been developed, which consists of a 6 degree of freedom (DOF) articulated robot arm, a force/torque sensor system to measure contact force and real-time PC based control system. A variant of the external force control scheme was successfully implemented to carry out the simultaneous force and position control in real-time. A number of engineering challenges are addressed to develop a viable system for TMS application; simultaneous real-time force and position tracking on subject’s head, unknown/varies environment stiffness and motion compensation to counter the force-controlled instability problems, and safe automated robotic system. Simulation of a single axis force-controlled robotic system has been carried out, which includes a task of maintaining contact on simulated subject’s head. The results provide a good agreement with parallel experimental tests, which leads to further improvement to the robot force control. An Adaptive Neuro-Fuzzy Force Controller has been developed to provide stable and robust force control on unknown environment stiffness and motion. The potential of the proposed method has been further illustrated and verified through a comprehensive series of experiments. This work also lays important foundations for long term related research, particularly in the development of real-time medical robotic system and new techniques of force control mainly for human-robot interaction. KEY WORDS: Transcranial Magnetic Stimulation, Robotic System, Real-time System, External Force Control Scheme, Adaptive Neuro-Fuzzy Force ControllerEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Force-controlled Transcranial Magnetic Stimulation (TMS) robotic system

The use of robots to assist neurologists in Transcranial Magnetic Stimulation (TMS) has the potential to improve the long term outcome of brain stimulation. Although extensive research has been carried out on TMS robotic system, no single study exists which adequately take into account the control of interaction of contact force between the robot and subject’s head. Thus, the introduction of force feedback control is considered as a desirable feature, and is particularly important when using an autonomous robot manipulator. In this study, a force-controlled TMS robotic system has been developed, which consists of a 6 degree of freedom (DOF) articulated robot arm, a force/torque sensor system to measure contact force and real-time PC based control system. A variant of the external force control scheme was successfully implemented to carry out the simultaneous force and position control in real-time. A number of engineering challenges are addressed to develop a viable system for TMS application; simultaneous real-time force and position tracking on subject’s head, unknown/varies environment stiffness and motion compensation to counter the force-controlled instability problems, and safe automated robotic system. Simulation of a single axis force-controlled robotic system has been carried out, which includes a task of maintaining contact on simulated subject’s head. The results provide a good agreement with parallel experimental tests, which leads to further improvement to the robot force control. An Adaptive Neuro-Fuzzy Force Controller has been developed to provide stable and robust force control on unknown environment stiffness and motion. The potential of the proposed method has been further illustrated and verified through a comprehensive series of experiments. This work also lays important foundations for long term related research, particularly in the development of real-time medical robotic system and new techniques of force control mainly for human-robot interaction. KEY WORDS: Transcranial Magnetic Stimulation, Robotic System, Real-time System, External Force Control Scheme, Adaptive Neuro-Fuzzy Force ControllerEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Vision based Object Recognition of E-Puck Mobile Robot for Warehouse Application

At present, most warehouses still require human services for unloading of goods. Unloading of goods requires a continuous system to ensure the quality of work productivity. Therefore the need of autonomous robot system in warehouse is needed to improve the quality of work. Thus, a localization and recognition algorithm is developed and implemented on the E-puck robot. The task involves the recognition of desired object based on their colour (red and blue) and locating the desired object to the target location (marked by green marker). In addition, the collision avoidance algorithm is also developed and integrated to allow the robot manoeuvre safely in its working environment. The colour histogram technique is used to recognize the desired object and the target location. Based on the experimental results, the developed algorithm is successfully fulfilling the pick and place requirement with success rate of approximately 70% in simulation study and 50% in real implementation

Non-contact Heart Rate Monitoring Analysis from Various Distances with different Face Regions

Heart rate (HR) is one of vital biomedical signals for medical diagnosis. Previously, conventional camera is proven to be able to detect small changes in the skin due to the cardiac activity and can be used to measure the HR. However, most of the previous systems operate on near distance mode with a single face patch, thus the feasibility of the remote heart rate for various distances remains vague. This paper tackles this issue by analyzing an optimal framework that capable to works under the mentioned issues. Initially, plausible face landmarks are estimated by employing cascaded of regression mechanism. Next, the region of interest (ROI) was constructed from the landmarks in a face location where non rigid motion is minimal. From the ROI, temporal photoplethysmograph (PPG) signal is calculated based on the average green pixels intensity and environmental illumination is separated using Independent Component Analysis (ICA) filter. Eventually, the PPG signal is further processed using series of temporal filter to exclude frequencies outside the range of interest prior to estimate the HR. As a conclusion, the HR can be detected up to 5 meters range with 94% accuracy using lower part of face region

Visualization on colour based flow vector of thermal image for movement detection during interactive session

Recently thermal imaging is exploited in applications such as motion and face detection. It has drawn attention many researchers to build such technology to improve lifestyle. This work proposed a technique to detect and identify a motion in sequence images for the application in security monitoring system or outdoor surveillance. Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. The results obtained could be observed in flow vector parameter and the segmentation colour based image for the time frame from 1 to 10 seconds. The visualization of both the parameters clarified the movement and changes of pixel intensity between two frames by the supportive colour segmentation, either in smooth or rough motion. Thus, this technique may contribute to others application such as biometrics, military system, and surveillance machine

Deep transfer learning application for automated ischemic classification in posterior fossa CT images

Abstract—Computed Tomography (CT) imaging is one of the conventional tools used to diagnose ischemic in Posterior Fossa (PF). Radiologist commonly diagnoses ischemic in PF through CT imaging manually. However, such a procedure could be strenuous and time consuming for large scale images, depending on the expertise and ischemic visibility. With the rapid development of computer technology, automatic image classification based on Machine Learning (ML) is widely been developed as a second opinion to the ischemic diagnosis. The practical performance of ML is challenged by the emergence of deep learning applications in healthcare. In this study, we evaluate the performance of deep transfer learning models of Convolutional Neural Network (CNN); VGG-16, GoogleNet and ResNet-50 to classify the normal and abnormal (ischemic) brain CT images of PF. This is the first study that intensively studies the application of deep transfer learning for automated ischemic classification in the posterior part of brain CT images. The experimental results show that ResNet-50 is capable to achieve the highest accuracy performance in comparison to other proposed models. Overall, this automatic classification provides a convenient and time-saving tool for improving medical diagnosis

Glaucoma detection of retinal images based on boundary segmentation

The rapid growth of technology makes it possible to implement in immediate diagnosis for patients using image processing. By using morphological processing and adaptive thresholding method for segmentation of optic disc and optic cup, various sizes of retinal fundus images captured through fundus camera from online databases can be processed. This paper explains the use of color channel separation method for pre-processing to remove noise for better optic disc and optic cup segmentation. Noise removal will improve image quality and in return help to increase segmentation standard. Then, morphological processing and adaptive thresholding method is used to extract out optic disc and optic cup from fundus image. The proposed method is tested on two publicly available online databases: RIM-ONE and DRIONS-DB. On RIM-ONE database, the average PSNR value acquired is 0.01891 and MSE is 65.62625. Meanwhile, for DRIONS-DB database, the best PSNR is 64.0928 and the MSE is 0.02647. In conclusion, the proposed method can successfully filter out any unwanted noise in the image and are able to help clearer optic disc and optic cup segmentation to be performed

Cross validation analysis of convolutional neural network variants with various white blood cells datasets for the classification task

White Blood Cells (WBCs) analysis is an important procedure to detect diseases is that closely related to human immunity system. Manual WBCs analysis is laborious and hence computer aided system (CAD) is a better option to alleviate the shortcoming. Since conventional segmentation�classification approach is tedious to configure, a Convolutional Neural Network (CNN) become recent trend for WBCs classification. Previously, there are many works proposed for WBCs identification. However, the models that can be generalised to works well among various datasets is remain vague. In this paper, an analysis of various CNN models which are simple Alexnet, embedded friendly Mobilenet, inception based Googlenet, systematic architecture VGG�16 and skip connection based model (Resnet & Densenet), are tested with three major WBCs datasets (Kaggle, LISC and IDB-2). From the rigorous experi�ments, it can be concluded that simple CNN model of Alexnet performs well across all three datasets with 98.08% accuracy on Kaggle, 96.34% accuracy on IDB-2 and 84.52% on LISC. This outcome can be utilise as a basis to improve the CNN classification model that can be generalize to works under various WBCs datasets

Comparative performance of filtering methods for reducing noise in ischemic posterior fossa CT Images

deepest part of the brain. This appears to cause significant degradation in CT image quality due to the effect of beam hardening and bone thickness. Whilst these issues may not fundamentally limit the scanning procedure, it does appear to be the contributory factors in reducing the performance of the ischemic diagnosis procedure. Thus, it is seen that image filtering is playing an important role in improving the CT image quality and effectively eliminates the influence of Poisson noise in the PF region. Therefore, this paper attempts to assess the feasibility of four different filtering methods; Anisotropic diffusion, Bilateral, Median and Wiener to eliminate noise in the CT image of PF containing ischemic. To the best of our knowledge, this is the first study to report the performance of filtering in ischemic PF. The efficacy of these four filters is evaluated in details using qualitative and quantitative metrics such as Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Structural Similarity Index (SSIM) and processing time. The experimental works demonstrate that Bilateral filtering offers promising results in which this method can eliminate Poisson noise in CT images for ischemic PF with average PSNR, RMSE, SSIM values of 32.95, 5.7416 and 0.9749 respectively. This filter has provided the flexibility of being applicable even in cases where ischemic is presented in PF