Classification of Corneal Nerve Images Using Machine Learning Techniques

Recent research shows that small nerve fiber damage is an early detector of neuropathy. These small nerve fibers are present in the human cornea and can be visualized through the use of a corneal confocal microscope. A series of images can be acquired from the subbasal nerve plexus of the cornea. Before the images can be quantified for nerve loss, a human expert manually traces the nerves in the image and then classifies the image as having neuropathy or not. Some nerve tracing algorithms are available in the literature, but none of them are reported as being used in clinical practice. An alternate practice is to visually classify the image for neuropathy without quantification. In this paper, we evaluate the potential of various machine learning techniques for automating corneal nerve image classification. First, the images are down-sampled using discrete wavelet transform, filtering and a number of morphological operations. The resulting binary image is used for extracting characteristic features of the image. This is followed by training the classifier on the extracted features. The trained classifier is then used for predicting the state of the nerves in the images. Our experiments yield a classification accuracy of 0.91 reflecting the effectiveness of the proposed method

Coordinated robotic system for civil structural health monitoring

With the recent advances in sensors, robotics, unmanned aerial vehicles, communication, and information technologies, it is now feasible to move towards the vision of ubiquitous cities, where virtually everything throughout the city is linked to an information system through technologies such as wireless networking and radio-frequency identification (RFID) tags, to provide systematic and more efficient management of urban systems, including civil and mechanical infrastructure monitoring, to achieve the goal of resilient and sustainable societies. In this proposed system, unmanned aerial vehicle (UAVs) is used to ascertain the coarse defect signature using panoramic imaging. This involves image stitching and registration so that a complete view of the surface is seen with reference to a common reference or origin point. Thereafter, crack verification and localization has been done using the magnetic flux leakage (MFL) approach which has been performed with the help of a coordinated robotic system. In which the first robot is placed at the top of the structure whereas the second robot is equipped with the designed MFL sensory system. With the initial findings, the proposed system identifies and localize the crack in the given structure. 1 The Authors, published by EDP Sciences, 2017.This paper was made possible by National Priorities Research Program (NPRP) grant No. 7-234-2-109 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Fuzzy Logic in neurosurgery: Predicting poor outcomes after lumbar disk surgery in 501 consecutive patients

Background: Despite a lot of research into Patient selection, a significant number of Patients fail to benefit from surgery for symptomatic lumbar disk herniation. We have used Fuzzy Logic-based fuzzy inference system (FIS) for identifying Patients unlikely to improve after disk surgery and explored FIS as a tool for surgical outcome prediction.Methods: Data of 501 Patients were retrospectively reviewed for 54 independent variables. Sixteen variables were short-listed based on heuristics and were further classified into memberships with degrees of membership within each. A set of 11 rules was formed, and the rule base used individual membership degrees and their values mapped from the membership functions to perform Boolean Logical inference for a particular set of inputs. For each rule, a decision bar was generated that, when combined with the other rules in a similar way, constituted a decision surface. The FIS decisions were then based on calculating the centroid for the resulting decision surfaces and thresholding of actual centroid values. The results of FIS were then compared with eventual postoperative Patient outcomes based on clinical follow-ups at 6 months to evaluate FIS as a predictor of poor outcome.Results: Fuzzy inference system has a sensitivity of 88% and specificity of 86% in the prediction of Patients most likely to have poor outcome after lumbosacral miscrodiskectomy. The test thus has a positive predictive value of 0.36 and a negative predictive value of 0.98.Conclusion: Fuzzy inference system is a sensitive method of predicting Patients who will fail to improve with surgical intervention

Fuzzy logic: A “simple” solution for complexities in neurosciences?

Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences

Coordinated Robotic System for Civil Structural Health Monitoring

With the recent advances in sensors, robotics, unmanned aerial vehicles, communication, and information technologies, it is now feasible to move towards the vision of ubiquitous cities, where virtually everything throughout the city is linked to an information system through technologies such as wireless networking and radio-frequency identification (RFID) tags, to provide systematic and more efficient management of urban systems, including civil and mechanical infrastructure monitoring, to achieve the goal of resilient and sustainable societies. In the proposed system, unmanned aerial vehicle (UAVs) is used to ascertain the coarse defect signature using panoramic imaging. This involves image stitching and registration so that a complete view of the surface is seen with reference to a common reference or origin point. Thereafter, crack verification and localization has been done using the magnetic flux leakage (MFL) approach which has been performed with the help of a coordinated robotic system. In which the first modular robot (FMR) is placed at the top of the structure whereas the second modular robot (SMR) is equipped with the designed MFL sensory system. With the initial findings, the proposed system identifies and localize the crack in the given structure. Research Methodology: The proposed approach used the advantages of the visual and MFL inspection approach to improve the efficiency of the SHM. Therefore, the usage of both approaches should be done in a way that the whole inspection is carried out in an optimal time period. Thus, due to the fast processing of visual inspection, it is done first followed by an MFL based verification approach. The visual inspection has been carried out such that the drone will take-off from a fixed point and take images at different heights without changing the GPS coordinate values of start point during flight. After completing the first scan, the coordinates of the GPS will be shifted and same procedure of taking images at different heights will be conducted. The process remain continue until the drone reaches to the starting GPS coordinates. The images which were taken at different heights for particular coordinates are considered as a single set. Thereafter, the image stitching (IS) is applied on individual sets. The process of IS involves a series of steps which were applied on the consecutive images of a particular set, such that one of the image is taken as a reference image (RI) whereas the other one is termed as the current image (CI). The resultant stitched image will be RI for the next consecutive image and then the whole stitching process is applied. The process remain continue for each set until a final stitched image has been obtained from them. The stitched result will be saved in the database with its corresponding GPS values. The same procedure of taking and stitching the images of the same structure will be repeated again after few months, depending upon the structural sensitivity as well as the severity of the weather condition around it. The current results will be compared with the stitched images present in the data base and if some anomaly is detected then the HP coordinates (i.e. the GPS coordinates) along with the estimated height for that particular location will be sent to the FMR to proceed the crack verification using MFL. The GPS module present in the FMR will guide the robot about its own location. As soon as Arduino Mega2560 Microcontroller receives the GPS coordinates from the system. It will translate them and compare them with its current location. The need of translation is because the FMR is present at the top of the building whereas the drone is flying at particular distance from the building. In order to obtain a correct translation the drone should remain at particular distance form in structure during the whole scanning process. The robot will take its direction based on the comparison result between its current GPS coordinates and the translated received GPS coordinates. As the robot moves it will keep checking the current GPS values and take decision accordingly. Since there might be some temporary or permanent obstacle present on the roof for decoration purpose. Therefore an ultrasonic range sensor has been used such that when the robot come close to an obstacle at defined distance the sensor will guide the robot to change its path and as soon as the obstacle is disappeared from the sensor range the robot will again start checking the GPS value to reach to its target destination. As it reaches to the target destination it will instruct the wrench motor to allow the SMR to reach to the location and obtain the current MFL reading of that place. These readings will be sent to the System. If an anomaly is detected then it is verified that the structure is having deformation at that particular location. If in vision based approach multiple anomalies have been detected then the robot will perform same procedure to determine the faults. Conclusion: With the initial findings, the proposed system appears to be a robust and inexpensive alternative to current approaches for automated inspection of civil/mechanical systems. The combination of VI and MFL approach provided the opportunity to detect, verify and localize the deformation in the structure.qscienc

Robotic Probe Positioning System for Structural Health Monitoring

Structural health Monitoring (SHM) is a very critical component for sustainable civil and mechanical structures in modern urban settings. The sky-scrappers and huge bridges in modern metropolis today are essential aspects of the prosperity and development of a country but at the same time they present a great challenge in terms of maintaining and sustaining the structures in a good health. Due to the complex designs of these structures, it is typically very dangerous to do SHM tasks through human personnel. Deployment of a monitoring team with various forms of equipment and scaffolding accompanied with their hoisting machines becomes extremely exorbitant for the maintenance and planning of the structures causing unnecessary cost-spill on other areas of the available budget. For most of the metallic structures, a fast method of scanning an area more closely is the Magnetic Flux Leakage (MFL) based defect detection. The MFL is considered the most economical approach for inspecting the metallic structures. Traditionally a hand-held device is used for performing the MFL inspection. In this paper, an autonomous MFL inspection robot has been presented which is small, flexible and remotely accessible. The robot is constructed with an Aluminum chassis, driven by two servomotors and holds a stack of very powerful Neodymium magnets to produce the required magnetic circuit. As the robot moves on a metallic surface, the magnetic circuit produces a layered magnetic field just under the scanning probe. The probe is composed of several Hall-effect sensors to detect any leakage in the magnetic circuit, which happens due to abnormality in the surface, thus detecting an anomaly. In this paper, a coordinated robotic inspection system has been proposed that utilizes a set of drones with one positioning robotic crawler platform with additional load hoisting capabilities that are utilized in order to position a specific defect-locating probe on the building under scan. Proposed methodology can play a vital role in SHM since it is capable of scanning a specific area and transmit back the results in a shorter time with a very safe mode of operation. This method is more reliable as compared to fixed sensors that focus a particular area of the structure only. Design for SHM robot involves intelligent integration of navigation system comprising of crucial parts that act as its backbone and assist the robot to work autonomously. These parts include GPS module, compass, range sensor, Infrared (IR) sensor along with MFL probe and winch setup and powerful PMDC Servo Motor controller (MC 160) used to drive two (2) powerful motors. The MC160 brushed Motor Controller proves to be a perfect platform for controlling Brushed DC motors. The controller consists of two power drivers in addition to OSMC connector for a third power driver (winch motor control). All these things add extra degrees of freedom to the robotic system for SHM. Novelty of the methodology is that the robot's program logic is not fixed. It is flexible in terms of path following. It has ability to detect an obstacle while it is on its way to scan the building. It not only detects obstacle but also changes its course and automatically adopts new route to the target destination. Such an autonomous robotic system can play a vital role in Structural Health Monitoring (SHM) in contrast to manual inspection eliminating the need of physical presence of human in severe weather conditions. The presented methodology is condition based in contrast to schedule-based approach. Core scan is easily done and robot is reconfigurable in a sense that it automatically changes its course to adopt to rough terrain and avoids obstacles on its way. Easy deployment makes robot an excellent choice for SHM with minimum cost and enhanced flexibility. Proposed robotic system can perform a coarse level of scan of a tall building using drones and the probe deployment robots (PDR). The drones provide a rough estimate of the location of possible defect or abnormality and PDR inspects the anomaly more closely. In addition, the coarse information about a possible defect can also help in deploying other means of inspection in a much lower cost since the whole structure needs not to be inspected.qscienc

A Prototype Of Virtually Interactive Hand Activating Devise-Low Cost Portable Head Mounted System (vihad Plus) For Neurological Rehabilitation

Background and Purpose: Restoring function in individuals who have severe paralysis of the upper extremity secondary to stroke is challenging. Recent technologies have made it possible to use robotic devices as novel tools for assisting the therapists to provide safe and intensive rehabilitation with repeated motions. However, most of the training robots are types of Continuous Passive Motion (CPM) devices that produce slower and stereotyped movement patterns. Earlier works have shown that passive or slow movements do not significantly benefit motor improvement. Several studies reveal that even the use of ipsilateral electromyographic (EMG) pattern recognition approaches might not be practical to decode movement intention and, may negatively affect re-mapping of the neural pathways in the brain. To have a successful hand rehabilitation system, the system should be able to produce a wide variety of unpredicted and challenging movement patterns of various degrees of speed and range of motion with increasing complexity, sufficient enough to produce the necessary neurological plasticity of the affected brain. Current rehabilitation devices are not sufficient to produce such a range of complex activities which enables maximum neurological plasticity. Objective: In this work, we describe a prototype of the contralateral EMG-based Interactive Hand Activating Devise for Stroke (IHADS) system that can detect a hemiplegic person's intention for bilaterally executed hand activities using his/her surface EMG signals from the non-affected side (contralateral). Furthermore, this system can assist in bilateral hand activities through an exoskeleton attached to the hemiplegic upper extremity to initiate progressively challenging and unpredicted type of activities in a virtual reality (VR) world to obtain optimum functional recovery by inducing maximum neurological plasticity. Design: The IHADS system is made up of an embedded controller and a robotic exoskeleton, contralateral EMG sensors and a VR interface with a semi-immersed VR system, where the patient will be seeing progressively impulsive activities that would force the brain to activate the affected extremity to manipulate through the remaining neural networks and mirror neuronal system which in turn will optimize the neurological recovery. This means that the paralyzed arm will be following the motion of the healthy arm whose motion is picked up by the EMG sensors and are translated as actuation signals for the exoskeleton to execute virtually created challenging activities. Conclusion: Contralateral EMG-based 'IHADS' system is a unique, cost effective, highly innovative and portable robotic device. If incorporated into the stroke rehabilitation, this system will be capable of autonomous guidance through the use of real-time feedback from the contralateral upper limb, integrated via the VR interface and the hand activating device to make rehabilitation more intense, functional, motivating, and capable of inducing maximum neurological plasticity

Design and implementation of a quadruped amphibious robot using duck feet

Roaming complexity in terrains and unexpected environments pose significant difficulties in robotic exploration of an area. In a broader sense, robots have to face two common tasks during exploration, namely, walking on the drylands and swimming through the water. This research aims to design and develop an amphibious robot, which incorporates a webbed duck feet design to walk on different terrains, swim in the water, and tackle obstructions on its way. The designed robot is compact, easy to use, and also has the abilities to work autonomously. Such a mechanism is implemented by designing a novel robotic webbed foot consisting of two hinged plates. Because of the design, the webbed feet are able to open and close with the help of water pressure. Klann linkages have been used to convert rotational motion to walking and swimming for the animal’s gait. Because of its amphibian nature, the designed robot can be used for exploring tight caves, closed spaces, and moving on uneven challenging terrains such as sand, mud, or water. It is envisaged that the proposed design will be appreciated in the industry to design amphibious robots in the near future