SELF-IMAGE MULTIMEDIA TECHNOLOGIES FOR FEEDFORWARD OBSERVATIONAL LEARNING

This dissertation investigates the development and use of self-images in augmented reality systems for learning and learning-based activities. This work focuses on self- modeling, a particular form of learning, actively employed in various settings for therapy or teaching. In particular, this work aims to develop novel multimedia systems to support the display and rendering of augmented self-images. It aims to use interactivity (via games) as a means of obtaining imagery for use in creating augmented self-images. Two multimedia systems are developed, discussed and analyzed. The proposed systems are validated in terms of their technical innovation and their clinical efficacy in delivering behavioral interventions for young children on the autism spectrum

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[ES] El aumento de los procedimientos usando la robótica quirúrgica en la última década demanda un alto número de cirujanos, capaces de teleoperar sistemas avanzados y complejos y, al mismo tiempo, de aprovechar los beneficios de la Cirugía Asistida por Robot de forma segura y efectiva. En la actualidad, los planes de formación se basan en la Realidad Virtual y entornos simulados para lograr un establecimiento escalable, rentable y completo del conjunto de habilidades quirúrgicas robóticas. Este trabajo se centra en el desarrolloo de un una escenario clínico mediante sensores que asistan al ciruajano durante su entrenamiento con el daVinci®, implementados en un entorno físico impreso en 3D. Esta investigación busca la obtención de un modelo segmentado, la impresión 3D del modelo para simular el escenraio clínico real y así abituar al cirujano a la interacción de los órganos y tejidos con el robot; y la implementación de sensores con que asistir al cirjuano en el entrenamiento. Para ello, con el fin de demostrar la eficacia de la asistencia durante los entrenamientos, así como la validez de los ejercicios de la operación simulada se ha realizado un estudio con doce voluntarios.Tanto la asistencia visual como el uso de fantomas 3D muestran ser una alternativa óptima para el aprendizaje de la habilidades requeridas en la cirugía robótica: manifestandose un paso adelante hacia un entrenamiento personlizado para cada cirujano.[EN] The increase of surgical procedures using robotic technology in the last decade demands a high number of surgeons capable of teleoperating advanced and complex systems while safely and effectively taking advantage of Robot-Assisted Surgery benefits. Currently, training plans rely on Virtual Reality and simulated environments to achieve a scalable, cost-effective, and comprehensive establishment of robotic surgical skills. This work focuses on the development of a clinical scenario through sensors that assist the surgeon during their training with the daVinci® system, implemented in a 3D-printed physical environment. This research aims to obtain a segmented model, 3D printing the model to simulate the real clinical scenario, thus familiarizing the surgeon with the interaction of organs and tissues with the robot. Additionally, sensors are implemented to assist the surgeon during training. Therefore, to demonstrate the effectiveness of the assistance during the training sessions and the validity of the exercises in the simulated operation, a study was conducted with twelve volunteers. Both visual assistance and the use of 3D phantoms prove to be an optimal alternative for learning the required skills in robotic surgery, representing a significant step forward towards personalized training for each surgeon.Castillo Rosique, P. (2023). Development sensorized 3D-printed realistic phantom to scale for surgical training with a daVinci robot. Universitat Politècnica de València. http://hdl.handle.net/10251/19804

A Comprehensive View of Electrosleep: The History, Finite Element Models and Future Directions

Transcranial Electrical Stimulation (tES) encompasses all methods of non-invasive current application to the brain used in research and clinical practice. We present the first comprehensive and technical review, explaining the evolution of tES in both terminology and dosage over the past 100 years of research to present day. Current transcranial Pulsed Current Stimulation (tPCS) approaches such as Cranial Electrotherapy Stimulation (CES) descended from Electrosleep (ES) through Cranial Electro-stimulation Therapy (CET), Transcerebral Electrotherapy (TCET), and NeuroElectric Therapy (NET) while others like Transcutaneous Cranial Electrical Stimulation (TCES) descended from Electroanesthesia (EA) through Limoge, and Interferential Stimulation. Prior to a contemporary resurgence in interest, variations of transcranial Direct Current Stimulation were explored intermittently, including Polarizing current, Galvanic Vestibular Stimulation (GVS), and Transcranial Micropolarization. The development of these approaches alongside Electroconvulsive Therapy (ECT) and pharmacological developments are considered. Both the roots and unique features of contemporary approaches such as transcranial Alternating Current Stimulation (tACS) and transcranial Random Noise Stimulation (tRNS) are discussed. Trends and incremental developments in electrode montage and waveform spanning decades are presented leading to the present day. Commercial devices, seminal conferences, and regulatory decisions are noted. This is concluded with six rules on how increasing medical and technological sophistication may now be leveraged for broader success and adoption of tES. Despite this history, questions regarding the efficacy of ES remain including optimal dose (electrode placement and waveform). An investigation into brain electric field and current density produced by various montages that are historically relevant to ES was done to evaluate how these montages effect the brain. MRI-derived head models that were segmented using an automated segmentation algorithm and manual corrections were solved for four different electrode montages. The montages that were used are as follows: Sponge electrode on left and right eyes (active), Sponge electrodes over left and right mastoids (return); Sponge electrodes above left and right eyes (active), Sponge electrodes over left and right mastoids (return); High-Definition (HD) electrodes on AF3 and AF4 (active), 5x7 cm sponge on neck (return); HD electrodes on AF3 and AF4 (active), 5x7 sponge electrode on Iz (return). A high concentration of electric field was found on the optic nerve, with levels lowered as the electrodes moved further away from the eyes. There was also a moderate current density on the amygdala, a center involved with anxiety, as well as high electric fields on the brain stem which are centers for sleep. Using the models that were run for the electrosleep inspired montages the montage that was selected for the proposed experiment was to use anodes on AF3 and AF4 with the cathode on Iz. The anodes will be HD electrodes while the cathode will be a 5x7 cm sponge. Subjects will be split into 4 groups of 8 people each and will receive two legs of stimulation spaced one week apart. One leg will have current of 2 mA, 1 mA, 0.5 mA or sham while the other leg is all sham and the order in which they receive it will be randomized. Subjects will be stimulated for 20 minutes at 100 Hz and will spend a total of 40 minutes during the experiment where they will have their eyes recorded with an IR sensitive camera and they will be required to perform an odd-tone response task. Subjects are expected to fall asleep faster with higher levels of current and there is no added effect from baseline expected for subjects who receive sham stimulatio

Thermal imaging developments for respiratory airflow measurement to diagnose apnoea

Sleep-disordered breathing is a sleep disorder that manifests itself as intermittent pauses (apnoeas) in breathing during sleep. The condition disturbs the sleep and can results in a variety of health problems. Its diagnosis is complex and involves multiple sensors attached to the person to measure electroencephalogram (EEG), electrocardiogram (ECG), blood oxygen saturation (pulse oximetry, S

Target classification in multimodal video

The presented thesis focuses on enhancing scene segmentation and target recognition methodologies via the mobilisation of contextual information. The algorithms developed to achieve this goal utilise multi-modal sensor information collected across varying scenarios, from controlled indoor sequences to challenging rural locations. Sensors are chiefly colour band and long wave infrared (LWIR), enabling persistent surveillance capabilities across all environments. In the drive to develop effectual algorithms towards the outlined goals, key obstacles are identified and examined: the recovery of background scene structure from foreground object ’clutter’, employing contextual foreground knowledge to circumvent training a classifier when labeled data is not readily available, creating a labeled LWIR dataset to train a convolutional neural network (CNN) based object classifier and the viability of spatial context to address long range target classification when big data solutions are not enough. For an environment displaying frequent foreground clutter, such as a busy train station, we propose an algorithm exploiting foreground object presence to segment underlying scene structure that is not often visible. If such a location is outdoors and surveyed by an infra-red (IR) and visible band camera set-up, scene context and contextual knowledge transfer allows reasonable class predictions for thermal signatures within the scene to be determined. Furthermore, a labeled LWIR image corpus is created to train an infrared object classifier, using a CNN approach. The trained network demonstrates effective classification accuracy of 95% over 6 object classes. However, performance is not sustainable for IR targets acquired at long range due to low signal quality and classification accuracy drops. This is addressed by mobilising spatial context to affect network class scores, restoring robust classification capability

Digital Image Processing And Metabolic Parameter Linearity To Noninvasively Detect Analyte Concentration

Spectroscopy is the scientific technique of quantifying and measuring electromagnetic, or light, reflectance or absorption. Atoms emit and/or absorb light when light passes through. The excitations provide specific energy signatures that relate to the element that is emitting or absorbing the light. Non-invasive biosensors monitor physical health properties such as heart rate, oxygen saturation, and tissue blood flow as a result of spectroscopy. Several attempts have been made to non-invasively detect metabolic chemical, or analyte, concentration with various spectroscopic techniques. The primary limitation is due to signal-to-noise ratio. This research focuses on a unique method that combines emission spectroscopy and machine learning to non-invasively detect glucose and other metabolic analyte concentrations. Artificial neural network is applied to train a predictive model that enables the remote sensing capability. The data acquisition requires capturing digital images of the spectral reflectance. Image processing and segmentation determines discrete variables that correlate with the metabolic analytes. The clinical trial protocol includes n=90 subjects, and a venipuncture comprehensive metabolic panel blood test within two minutes prior to a non-invasive spectral reading. Results indicate a strong correlation between the spectral system and the clinical gold standard, relative to metabolic analyte concentration

Rapid Syllable Transition treatment for Childhood Apraxia of Speech: exploring treatment efficacy in three service-delivery contexts

Many children are unable to access speech pathology treatment at the recommended intensity. To address this problem, clinicians use a range of strategies: modifying treatment intensity, mode or delivery agent. Accessing sufficient speech pathology treatment for children with childhood apraxia of speech (CAS) is particularly difficult because treatment should be delivered face-to-face, by a clinician, 3–5 times per week. One relatively new treatment for CAS, rapid syllable transition (ReST) treatment has demonstrated significant acquisition and generalisation effects when delivered intensively, face-to-face, by a clinician. This thesis uses three separate single-case experimental studies to investigate the efficacy of ReST treatment when provided via alternative service-delivery approaches. Lower dose-frequency, telehealth delivery, and a combined clinician–parent delivery model were explored. The studies showed that both lower dose-frequency and telehealth delivery were efficacious. Combined clinician–parent delivery was efficacious for fewer than half the children. Parental experiences of telehealth and of the combined clinician–parent delivery models were investigated qualitatively. The parents reported positive experiences of telehealth, finding it convenient and time-efficient. They had concerns about the combined clinician-parent delivery model, reporting discomfort in the role of therapist, and low levels of confidence and competence in delivering treatment. This thesis supports implementation of both lower dose-frequency and telehealth delivery of ReST treatment. Despite the intuitive appeal of parent-delivered treatment for overcoming access barriers, this thesis does not support clinical application of parent-delivered ReST treatment. This thesis argues for further investigation of intensity variables in CAS treatment and methods for improving parent-delivered treatment efficacy, and the need to ensure clients receive sufficient service provisio

CHEATING DETECTION IN ONLINE EXAMS BASED ON CAPTURED VIDEO USING DEEP LEARNING

Today, e-learning has become a reality and a global trend imposed and accelerated by the COVID-19 pandemic. However, there are many risks and challenges related to the credibility of online exams which are of widespread concern to educational institutions around the world. Online exam system continues to gain popularity, particularly during the pandemic, due to the rapid expansion of digitalization and globalization. To protect the integrity of the examination and provide objective and fair results, cheating detection and prevention in examination systems is a must. Therefore, the main objective of this thesis is to develop an effective way of detection of cheating in online exams. In this work, a system to track and prevent attempts to cheat on online exams is developed using artificial intelligence techniques. The suggested solution uses the webcam that is already connected to the computer to record videos of the examinee in real time and afterwards analyze them using different deep learning methods to find best combinations of models for face detection and classification if cheating/not cheating occurred. To evaluate the system, we use a benchmark dataset of exam videos from 24 participants who represented examinees in online exam. An object detection technique is used to detect face appeared in the image and crop the face portion, and then a deep learning based classification model is trained from the images to classify a face as cheating or not cheating. We have proposed an effective combination of data preprocessing, object detection, and classification models to obtain high detection accuracy. We believe that the suggested invigilation methodology can be used in colleges, institutions, and schools to look for and keep an eye on suspicious student behavior. Hopefully, by putting the proposed invigilation method into place, we can aid in eliminating and reducing cheating incidences as it undermines the integrity and fairness of the educational system