This thesis reports a summary of the PhD programme for the assessment of person‘s emotional anxiety using Electro-optical technology. The thesis focuses mainly on the understanding of fundamental properties of physiological responses to emotional anxiety and how they can be captured by using Electro-optical (EO) imaging methods such as hyperspectral imaging (HSI) and thermal imaging (TI) techniques. The thesis summarises three main areas of work that have been undertaken by the author in the programme: (a) Experimental set up including HSI system and data acquisition software design and implementation, (b) fundamental understanding of physiological responses to emotional anxiety from the EO perspective and (c) the development of a novel remote sensing technique for the assessment of emotions without the requirement of base line information. One of our main results is to provide evidence to prove that the mean temperature in the periorbital region remains the same within 0.2°C during emotional anxiety. Furthermore, we have shown that it is the high temperature pixels within the periorbital, which increases in numbers by a huge amount after 2 minutes of the onset of anxiety. We have also developed techniques to allow the assessment anxiety without the need of base line information. The method has been tested using a sample size of about 40 subjects, and achieved promising result. Technologies for the remote sensing of heart beat rate has been in great demand, this study also involves the development of heart beat detection using TI system. Moreover, we have also attempted for the first time to sense glucose concentration from the blood sample in-vivo using HSI technique remotely. / ©Cranfield University

Shoulder injuries are a focus of work related musculoskeletal disorder (WMSD) research due to rising healthcare costs, an aging workforce, and long recovery times. Substantial research has been performed in the area of shoulder WMSDs and a number of risk factors have been implicated in their development; including static loads, repetition, and deviated posture. However, knowledge of underlying pathophysiological mechanisms is limited. Thermography provides a non-invasive technique that may offer clues to unknown physiological markers associated with injury development during job task performance. The objective of this study was to quantify anterior deltoid surface temperature changes as function of changing task demands. Skin surface temperature changes of the anterior deltoid, modified Borg CR-10 ratings, and endurance time during overhead static exertions until exhaustion for two work loads (15 and 30% MVC) and shoulder angles (90o and 115o) were quantified. Ten participants (5 males and 5 females) participated in the study and were free of confounding conditions (such as chronic or acute shoulder injury) and were required to meet body fat percentile requirements. Thermography showed that the higher shoulder angle had a reduced blood flow while there were no differences in temperature for exertion. Modified Borg ratings were not found to be well correlated with temperature values. The findings suggest that workers performing overhead work should minimize their deviated posture when available to prevent a high risk of developing a shoulder WMSD. / Master of Science

General health screening is needed to decrease the risk of pandemic in high volume areas. Thermal characterization, via infrared imaging, is an effective technique for fever detection, however, strict use requirements in combination with highly controlled environmental conditions compromise the practicality of such a system. Combining advanced processing techniques to thermograms of individuals can remove some of these requirements allowing for more flexible classification algorithms. The purpose of this research was to identify individuals who had febrile status utilizing modern thermal imaging and machine learning techniques in a minimally controlled setting. Two methods were evaluated with data that contained environmental, and acclimation noise due to data gathering technique. The first was a pretrained VGG16 Convolutional Neural Network found to have F1 score of 0.77 (accuracy of 76%) on a balanced dataset. The second was a VGG16 Feature Extractor that gives inputs to a principle components analysis and utilizes a support vector machine for classification. This technique obtained a F1 score of 0.84 (accuracy of 85%) on balanced data sets. These results demonstrate that machine learning is an extremely viable technique to classify febrile status independent of noise affiliated.

In space, visual based relative navigation systems suffer from the harsh illumination conditions of the target (e.g. eclipse conditions, solar glare, etc.). In current Rendezvous and Docking (RvD) missions, most of these issues are addressed by advanced mission planning techniques (e.g strict manoeuvre timings). However, such planning would not always be feasible for Active Debris Removal (ADR) missions which have more unknowns. Fortunately, thermal infrared technology can operate under any lighting conditions and therefore has the potential to be exploited in the ADR scenario. In this context, this study investigates the benefits and the challenges of infrared based relative navigation. The infrared environment of ADR is very much different to that of terrestrial applications. This study proposes a methodology of modelling this environment in a computationally cost effective way to create a simulation environment in which the navigation solution can be tested. Through an intelligent classification of possible target surface coatings, the study is generalised to simulate the thermal environment of space debris in different orbit profiles. Through modelling various scenarios, the study also discusses the possible challenges of the infrared technology. In laboratory conditions, providing the thermal-vacuum environment of ADR, these theoretical findings were replicated. By use of this novel space debris set-up, the study investigates the behaviour of infrared cues extracted by different techniques and identifies the issue of short-lifespan features in the ADR scenarios. Based on these findings, the study suggests two different relative navigation methods based on the degree of target cooperativeness: partially cooperative targets, and uncooperative targets. Both algorithms provide the navigation solution with respect to an online reconstruction of the target. The method for partially cooperative targets provides a solution for smooth trajectories by exploiting the subsequent image tracks of features extracted from the first frame. The second algorithm is for uncooperative targets and exploits the target motion (e.g. tumbling) by formulating the problem in terms of a static target and a moving map (i.e. target structure) within a filtering framework. The optical flow information is related to the target motion derivatives and the target structure. A novel technique that uses the quality of the infrared cues to improve the algorithm performance is introduced. The problem of short measurement duration due to target tumbling motion is addressed by an innovative smart initialisation procedure. Both navigation solutions were tested in a number of different scenarios by using computer simulations and a specific laboratory set-up with real infrared camera. It is shown that these methods can perform well as the infrared-based navigation solutions using monocular cameras where knowledge relating to the infrared appearance of the target is limited.

Gymnasts have a relatively high injury rate and severity with highly qualified gymnasts suffering the most. One of the common injuries in gymnastics is the overuse-type that often remains latent until near the decisive moments of competition when the injury rises to the level of incapacitation. Is there a technology and methodology available to monitor gymnasts during development that can identify latent injuries and thus alert medical personnel to potential performance-limiting problems at the earliest possible time? Imaging consists of the use of a thermal camera to identify inflamed areas and asymmetric temperature patterns. Thermal asymmetries are determined via thermal image and pain is assessed with palpation, history, and subject identification. Video recordings are made of the involved areas and recorded electronically for transfer to physicians, physical therapists, and athletic trainers for further investigation and remediation. This is an ongoing descriptive study of the use of thermal imaging on inflammation and injury in gymnasts. Thermal differentiation of tissue areas is performed by visual inspection and bilateral comparison of the thermal images. Thermal images show bilateral and tissue area thermal differentials by differences in gray scale. This information discriminates injuries, inflammation, and other conditions without invasive procedures. The ability to identify and thus treat injuries while they are minor is a significant improvement over waiting until the injuries become increasingly symptomatic and performance-limiting. Thermal imaging has become a mainstay of our laboratory in assisting young athletes in remaining injury free, making return-to-activity decisions, and collaborating with medical personnel to identify, prevent and treat injuries and other conditions.

Infrared thermal imaging of the periorbital regions of the face shows promise as an input signal modality for an alternative communication system for individuals with conditions that preclude speech or voluntary movement, such as total locked-in syndrome. However, it was unknown if the startle response triggers a change in the skin temperature of these regions; such a change could generate false positives in a thermography-based access system. This study presents an examination of the temperature characteristics of the periorbital regions of 11 able-bodied adult participants before and after an auditory startle stimulus. The results show that the startle response has no substantial effect on the mean temperature of the periorbital regions. This indicates that thermography-based access solutions would be insensitive to startle reactions in their user, an important advantage over other modalities being considered in the context of access solutions for individuals with a severe motor disability.

Infrared thermal imaging of the periorbital regions of the face shows promise as an input signal modality for an alternative communication system for individuals with conditions that preclude speech or voluntary movement, such as total locked-in syndrome. However, it was unknown if the startle response triggers a change in the skin temperature of these regions; such a change could generate false positives in a thermography-based access system. This study presents an examination of the temperature characteristics of the periorbital regions of 11 able-bodied adult participants before and after an auditory startle stimulus. The results show that the startle response has no substantial effect on the mean temperature of the periorbital regions. This indicates that thermography-based access solutions would be insensitive to startle reactions in their user, an important advantage over other modalities being considered in the context of access solutions for individuals with a severe motor disability.

In an effort to gain a more comprehensive and complete understanding of the thermal behaviors occurring during the selective laser sintering process, external temperature measurements were taken during the build process. To accomplish this, an infrared camera was aimed directly through a viewport on the front of the sinterstation. The temperature was monitored during the heating process which showed slightly non-uniform heating of the part bed surface. Temperatures were also recorded while the laser was sintering each layer and the subsequent cooling of the entire machine following the build. By directly capturing infrared images of the part bed’s surface, it is clearer how the temperature gradients behave and the impact such variables have on part build efficiency. / text

Cancer has been one of main causes of human deaths for many years. Early detection of cancer is essential to provide definitive treatment. Among many cancer treatment methods, nanoparticle-mediated photothermal therapy is considered as one of the promising cancer treatment methods because of its non-invasiveness and cancer-specific therapy. Ultrasound and photoacoustic imaging can be utilized for both cancer detection and photothermal therapy guidance. Ultrasound elasticity imaging can detect cancer using tissue elastic properties. Once cancer is diagnosed, spectroscopic photoacoustic imaging can be used to monitor nanoparticle delivery before photothermal therapy. When nanoparticles are well accumulated at the tumor, ultrasound and photoacoustic-based thermal imaging can be utilized for estimating temperature distribution during photothermal therapy to guide therapeutic procedure. In this dissertation, ultrasound beamforming, elasticity imaging, and spectroscopic photoacoustic imaging methods were developed to improve cancer detection and therapy guidance. Firstly, a display pixel based synthetic aperture focusing method was developed to fundamentally improve ultrasound image qualities. Secondly, an autocorrelation based sub-pixel displacement estimation method was developed to enhance signal-to-noise ratio of elasticity images. The developed elasticity imaging method was utilized to clinically evaluate the feasibility of using ultrasound elasticity imaging for prostate cancer detection. Lastly, a minimum mean square error based spectral separation method was developed to robustly utilize spectroscopic photoacoustic imaging. The developed spectroscopic photoacoustic imaging method was utilized to demonstrate ultrasound and photoacoustic image-guided photothermal cancer therapy using in-vivo tumor-bearing mouse models. The results of these studies suggest that ultrasound and photoacoustic imaging can assist both cancer detection and therapy guidance. / text

Leakages in the district heating networks is a current and growing problem. To find the leakages today many district heating companies uses manual techniques that are both time consuming and insecure, the methods can leave a lot of leakages in older pipes undiscovered for a very long time. These undetected leakages costs the district heating companies a lot of money and can even be fatal. It is therefor of great importance that the leakages is found in time, thus the methods for leak detection needs to be improved. The main purpose of this thesis was to investigate the ability to use thermal images to automatically search for leakages in district heating systems. To investigate this aerial thermal images from 2013 were collected. Image analysis was performed using ArcGIS and ENVI. This included, among other things, image preprocessing such as to define the projection of the images and unsupervised isodata classification to find potential leakages in the thermal images. This automatic analysis resulted in many false alarms. One example were false alarms caused by vegetation, since vegetation absorbs heat during the day it appears warmer than the surroundings at night. To deal with this problem an unsupervised classification algorithm, isodata, was used again to classify the vegetational areas and the non-vegetational. This algorithm decreased the number of false alarms drastic and thereby increased the usability of the algorithm. Other false alarm that has not been automatically rejected in this thesis were for example false alarms caused by heat leaking from buildings. One way to map such false alarms could be to analyse the shape and the linearisation of the potential leakages close to buildings. This would hugely increase the accuracy of the used algorithm. The provided thermal images used in this thesis consisted of several confirmed leakages. All these confirmed leakages was found by the used algorithm. Although, the accuracy of the used algorithm could be discussed since many false alarms were generated. Nevertheless, to reject false alarms are much less time consuming than manual leak detection for an entire city. Thereby the conclusion that an automatic leak detection in district heating networks is possible, furthermore a leak detection tool like this would be usable for the district heating companies. The evaluations from several different district heating experts who are using Digpro's district heating application, dpHeating, today shows that a leak detection tool using thermal images would be a useful addition in dpHeating.