Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks in Patients with Complex Regional Pain Syndrome

[ES] El síndrome de dolor regional complejo (SDRC) es un trastorno de dolor crónico debilitante que suele afectar a una extremidad, y se caracteriza por su compleja e incomprendida fisiopatología subyacente, lo que supone un reto para su diagnóstico y tratamiento. Para evitar el deterioro de la calidad de vida de los pacientes, la consecución de un diagnóstico y tratamiento tempranos marca un punto de inflexión. Entre los diferentes tratamientos, los bloqueos simpáticos lumbares (BSLs) tienen como objetivo aliviar el dolor y reducir algunos signos simpáticos de la afección. Este procedimiento intervencionista se lleva a cabo inyectando anestesia local alrededor de los ganglios simpáticos y, hasta ahora, se realiza frecuentemente bajo el control de diferentes técnicas de imagen, como los ultrasonidos o la fluoroscopia. Dado que la termografía infrarroja (TIR) ha demostrado ser una herramienta eficaz para evaluar la temperatura de la piel, y teniendo en cuenta el efecto vasodilatador que presentan los anestésicos locales inyectados, se ha considerado el uso de la IRT para la evaluación de los BSLs. El objetivo de esta tesis es, estudiar la capacidad de la TIR como una técnica complementaria para la evaluación de la eficacia en la ejecución de los BSLs. Para cumplir este objetivo, se han realizado tres estudios implementando la TIR en pacientes diagnosticados de SDRC de miembros inferiores sometidos a BSLs. El primer estudio se centra en la viabilidad de la TIR como herramienta complementaria para la evaluación de la eficacia ejecución de los BSLs. Cuando se realizan los BSLs, la colocación correcta de la aguja es crítica para llevar realizar el procedimiento técnicamente correcto y, en consecuencia, para lograr los resultados clínicos deseados. Para verificar la posición de la aguja, tradicionalmente se han utilizado técnicas de imagen, sin embargo, los BSLs bajo control fluoroscópico no siempre aseguran su exacta ejecución. Por este motivo, se han aprovechado las alteraciones térmicas inducidas por los anestésicos locales y se han evaluado mediante la TIR. Así, cuando en las imágenes infrarrojas se observaron cambios térmicos en la planta del pie afectado tras la inyección de lidocaína, se consideró que el BSL era exitoso. El segundo estudio trata del análisis cuantitativo de los datos térmicos recogidos en el entorno clínico a partir de diferentes parámetros basados en las temperaturas extraídas de ambos pies. Según los resultados, para predecir adecuadamente los BSLs exitosos, se deberían analizar las temperaturas de las plantas de los pies durante los primeros cuatro minutos tras la inyección del anestésico local. Así, la aplicación de la TIR en el entorno clínico podría ser de gran ayuda para evaluar la eficacia de ejecución de los BSLs mediante la evaluación de las temperaturas de los pies en tiempo real. Por último, el tercer estudio aborda el análisis cuantitativo mediante la implementación de herramientas de machine learning (ML) para evaluar su capacidad de clasificar automáticamente los BSLs. En este estudio se han utilizado una serie de características térmicas extraídas de las imágenes infrarrojas para evaluar cuatro algoritmos de ML para tres momentos diferentes después del instante de referencia (inyección de lidocaína). Los resultados indican que los cuatro modelos evaluados presentan buenos rendimientos para clasificar automáticamente los BSLs entre exitosos y fallidos. Por lo tanto, la combinación de parámetros térmicos junto con de clasificación ML muestra ser eficaz para la clasificación automática de los procedimientos de BSLs. En conclusión, el uso de la TIR como técnica complementaria en la práctica clínica diaria para la evaluación de los BSLs ha demostrado ser totalmente eficaz. Dado que es un método objetivo y relativamente sencillo de implementar, puede permitir que los médicos especialistas en dolor identifiquen los bloqueos realizados fallidos y, en consecuencia, puedan revertir esta situación.[CA] La síndrome de dolor regional complex (SDRC) és un trastorn de dolor crònic debilitant que sol afectar una extremitat, i es caracteritza per la seua complexa i incompresa fisiopatologia subjacent, la qual cosa suposa un repte per al seu diagnòstic i tractament. Per a evitar la deterioració de la qualitat de vida dels pacients, la consecució d'un diagnòstic i tractament primerencs marca un punt d'inflexió. Entre els diferents tractaments , els bloquejos simpàtics lumbars (BSLs) tenen com a objectiu alleujar el dolor i reduir alguns signes simpàtics de l'afecció. Aquest procediment intervencionista es duu a terme injectant anestèsia local al voltant dels ganglis simpàtics i, fins ara, es realitza freqüentment sota el control de diferents tècniques d'imatge, com els ultrasons o la fluoroscopia. Atés que la termografia infraroja (TIR) ha demostrat ser una eina eficaç per a avaluar la temperatura de la pell, i tenint en compte l'efecte vasodilatador que presenten els anestèsics locals injectats, s'ha considerat l'ús de la TIR per a l'avaluació dels BSLs. L'objectiu d'aquesta tesi és, estudiar la capacitat de la TIR com una tècnica complementària per a l'avaluació de l'eficàcia en l'execució dels BSLs. Per a complir aquest objectiu, s'han realitzat tres estudis implementant la TIR en pacients diagnosticats de SDRC de membres inferiors sotmesos a BSLs. El primer estudi avalua la viabilitat de la TIR com a eina complementària per a l'analisi de l'eficàcia en l'execució dels BSLs. Quan es realitzen els BSLs, la col·locació correcta de l'agulla és crítica per a dur a terme el procediment tècnicament correcte i, en conseqüència, per a aconseguir els resultats clínics desitjats. Per a verificar la posició de l'agulla, tradicionalment s'han utilitzat tècniques d'imatge, no obstant això, els BSLs baix control fluoroscòpic no sempre asseguren la seua exacta execució. Per aquest motiu, s'han aprofitat les alteracions tèrmiques induïdes pels anestèsics locals i s'han avaluat mitjançant la TIR. Així, quan en les imatges infraroges es van observar canvis tèrmics en la planta del peu afectat després de la injecció de lidocaIna, es va considerar que el BSL era exitós. El segon estudi tracta de l'anàlisi quantitativa de les dades tèrmiques recollides en l'entorn clínic a partir de diferents paràmetres basats en les temperatures extretes d'ambdós peus. Segons els resultats, per a predir adequadament l'execució exitosa d'un BSL, s'haurien d'analitzar les temperatures de les plantes dels peus durant els primers quatre minuts després de la injecció de l'anestèsic local. Així, l'implementació de la TIR en l'entorn clínic podria ser de gran ajuda per a avaluar l'eficàcia d'execució dels BSLs mitjançant l'avaluació de les temperatures dels peus en temps real. El tercer estudi aborda l'anàlisi quantitativa mitjançant la implementació d'eines machine learning (ML) per a avaluar la seua capacitat de classificar automàticament els BSLs. En aquest estudi s'han utilitzat una sèrie de característiques tèrmiques extretes de les imatges infraroges per a avaluar quatre algorismes de ML per a tres moments diferents després de l'instant de referència (injecció de lidocaïna). Els resultats indiquen que els quatre models avaluats presenten bons rendiments per a classificar automàticament els BSLs en exitosos i fallits. Per tant, la combinació de paràmetres tèrmics juntament amb models de classificació ML mostra ser eficaç per a la classificació automàtica dels procediments de BSLs. En conclusió, l'ús de la TIR com a tècnica complementària en la pràctica clínica diària per a l'avaluació dels BSLs ha demostrat ser totalment eficaç. Atés que és un mètode objectiu i relativament senzill d'implementar, pot ajudar els metges especialistes en dolor a identificar els bloquejos realitzats fallits i, en conseqüència, puguen revertir aquesta situació.[EN] Complex regional pain syndrome (CRPS) is a debilitating chronic pain condition that usually affects one limb, and it is characterized by its misunderstood underlying pathophysiology, resulting in both challenging diagnosis and treatment. To avoid the patients' impairment quality of life, the achievement of both an early diagnosis and treatment marks a turning point. Among the different treatment approaches, lumbar sympathetic blocks (LSBs) are addressed to alleviate the pain and reduce some sympathetic signs of the condition. This interventional procedure is performed by injecting local anaesthetic around the sympathetic ganglia and, until now, it has been performed under different imaging techniques, including the ultrasound or the fluoroscopy approaches. Since infrared thermography (IRT) has proven to be a powerful tool to evaluate skin temperatures and taking into account the vasodilatory effects of the local anaesthetics injected in the LSB, the use of IRT has been considered for the LSBs assessment. Therefore, the purpose of this thesis is to evaluate the capability of IRT as a complementary assessment technique for the LSBs procedures performance. To fulfil this aim, three studies have been conducted implementing the IRT in patients diagnosed with lower limbs CRPS undergoing LSBs. The first study focuses on the feasibility of IRT as a complementary assessment tool for LSBs performance, that is, for the confirmation of the proper needle position. When LSBs are performed, the correct needle placement is critical to carry out the procedure technically correct and, consequently, to achieve the desired clinical outcomes. To verify the needle placement position, imaging techniques have traditionally been used, however, LSBs under radioscopic guidance do not always ensure an exact performance. For this reason, the thermal alterations induced by the local anaesthetics, have been exploited and assessed by means of IRT. Thus, the LSB procedure was considered successfully performed when thermal changes within the affected plantar foot were observed in the infrared images after the lidocaine injection. The second study deals with the quantitative analysis of the thermal data collected in the clinical setting through the evaluation of different temperature-based parameters extracted from both feet. According to the results, the proper LSB success prediction could be achieved in the first four minutes after the block through the evaluation of the feet skin temperatures. Therefore, the implementation of IRT in the clinical setting might be of great help in assessing the LSBs performance by evaluating the plantar feet temperatures in real time. Finally, the third study addresses the quantitative analysis by implementing machine learning (ML) tools to assess their capability to automatically classify LSBs. In this study, a set of thermal features retrieved from the infrared images have been used to evaluate four ML algorithms for three different moments after the baseline time (lidocaine injection). The results indicate that all four models evaluated present good performance metrics to automatically classify LSBs into successful and failed. Therefore, combining infrared features with ML classification models shows to be effective for the LSBs procedures automatic classification. In conclusion, the use of IRT as a complementary technique in daily clinical practice for LSBs assessment has been evidenced entirely effective. Since IRT is an objective method and it is not very demanding to perform, it is of great help for pain physicians to identify failed procedures, and consequently, it allow them to reverse this situation.Cañada Soriano, M. (2022). Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks in Patients with Complex Regional Pain Syndrome [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181699TESI

A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

Evaluation of infrared technology for predicting transport stress and meat quality in market pigs

Three experiments were conducted to evaluate infrared technology as a tool for predicting transport stress and pale soft and exudative (PSE) meat traits in market pigs. Experiment 1 compared the ability of two digital infrared thermographic cameras [research grade (RG) and consumer grade (CG)] to detect temperature change in market pigs (N=168 market pigs, BW=111.5±13.2 kg). There were two treatment groups: Control and Handling treatment (N=84 per treatment). Control pigs remained in their home pen while Handling treatment pigs received a mild handling stressor (walking a distance of approximately 100m). The ocular (OT) and body temperature (BT) of all pigs were measured using both cameras at two time points: before (baseline) and approximately 1 hr later (after Handling treatment pigs were moved). Experiment 2 compared pre- and post-transport ocular and body temperatures of market pigs to determine if pre-transport temperatures were predictive of post-transport temperatures using CG digital infrared cameras. In Experiment 2, pigs (N=120, BW=105.1 ± 4.9 kg) were transported in five replicates (20-25 pigs/replicate) for ~2 h to an abattoir during summer. Thermographic ocular and body images were collected from each pig at three time points; twice before and once after transport (T1: three days prior to transport, T2: one day before transport, and T3: in lairage post-transport). Experiment 3 was conducted using animals from Experiment 2. The objective of Experiment 3 was to determine if infrared technology can predict meat quality based on the post-transport ocular and body temperatures (T3) collected at the packing plant prior to slaughter. At slaughter, blood samples were collected for cortisol, glucose and lactate analyses. Carcass pH was taken at 1 and 3 h postmortem and loin samples were collected for meat quality (ultimate pH, meat color, drip loss and meat tenderness) assessment. Data collected in Experiment 1 were analyzed using Pearson correlations, linear regression and a mixed model with main effects: treatment, time and their interaction, with pen as a random factor using SAS (SAS 9.4). Data collected in Experiment 2 were analyzed using Pearson correlations and regression analysis, while data collected in Experiment 3 were analyzed using Pearson correlations, linear regression and mixed model analyses in SAS. In Experiment 1, the infrared measures from RG and CG cameras were positively (r=0.93, P<0.05) correlated. In addition, handling treatment led to increases (P<0.05) in body and ocular temperatures in handled pigs compared to unhandled controls. In Experiment 2, significant positive correlations were found between T1 and T2 body temperatures. Moreover, the regression analyses showed strong associations (r2=0.80, P=0.01) between T1 and T2 body temperatures. Correlations between T1 and T2 ocular temperatures were non-significant, and there were no relationships between T1 and T3, or T2 and T3 temperatures for body or ocular measures. Experiment 3 showed positive correlations (r=0.40, P<0.010) between IR ocular temperatures post-transport and blood cortisol at slaughter, suggesting a relationship between temperature and stress physiology in pigs. Meat yellowness (b*) increased with elevated body temperatures (r=0.2, P <0.001). Meat tenderness increased with increase in IR ocular and body temperatures post-transport (r=-0.51, P<0.001). Pigs with high IR body temperatures post-transport/pre-slaughter had poorer meat quality characterized by pale soft and exudative (PSE), moderately pale soft and exudative (MPSE), pale firm and normal (PFN) carcasses postmortem. In conclusion, no correlation was found between on-farm and post-transport IR temperatures. However, IR body temperatures post-transport were predictive of meat quality traits in market pigs. Results in this thesis support the potential for infrared technology to identify stressed or febrile pigs on-farm and to predict pork quality before slaughter. Automation of infrared technology in commercial barns or packing plants could allow real-time data collection and monitoring of pig health for improved animal welfare and meat quality

From textiles to humans: the role of textile moisture transfer properties on human physiological and perceptual responses

Clothing provides the body with a protective barrier from environmental factors, such as rain, snow, wind and solar radiation. Beside this imperative protective function, the interaction between clothing and the human body has implications in terms of temperature regulation and comfort. Specifically, wetness at the skin-clothing interface represents one of the highest sources of discomfort when wearing clothing, which could even contribute to reductions in human performance and, in extreme environments, impact human health. To maximise heat and mass transfer through the clothing barrier, the textile and clothing industry constantly works on apparel innovations. Textile test methods allow assessments of objective improvements in material performance; however it is often unknown whether improvements at material level have an impact on human physiological and/or perceptual responses. Therefore, the aim of this research was to adopt an integrative paradigm in which textile and clothing moisture transfer parameters are instrumentally characterised and, subsequently, assessed in human physiological as well as sensorial experiments. In this thesis, the current literature review focuses on the interactions occurring between the thermal environment, the human body and the clothes worn by the person (Chapter 1). The test methods applied to evaluate textile and clothing parameters are reviewed and discussed (Chapter 1). This is followed by an outlined of the methodological developments adopted in the current research to measure human responses when interacting with textiles and clothing, both during rest and exercise conditions (Chapter 2). In the first laboratory study (Chapter 3), a skin regional experiment (fabrics applied on a restricted body area) was conducted to study the role of fabric thickness and fibre type on human cutaneous wetness perception, in condition of static fabric contact with the skin. In the same study, the approach adopted to characterise fabric moisture content, i.e. absolute (same µL of water per area (cm2)) versus relative (same µL of water per unit of fabric volume (cm3)) was studied and the implications that fabric total saturation has on skin wetness perception were explored. The results showed the role of fabric thickness as major determinant of fabric absorption capacity and also wetness perception. In fabrics presenting same saturation percentage (same water content per volume) a positive relation between fabric thickness and wetness perception was observed and this was independent of fibre type. When applying the same relative to volume water content (same saturation percentage) thicker fabrics were perceived wetter than the thinner ones. Conversely, when applying the same absolute water amount, thicker fabrics were perceived dryer compared to thinner fabrics, given that thinner fabrics were more saturated. These findings indicate that human wetness perception responses between fabrics with different volume/thickness parameters should be interpreted in light of their saturation parameters rather than considering the absolute moisture content. In the same study, it was observed that the weight of the fabric in wet state can also modulate wetness-related perceptual responses. Specifically, heavier fabrics were perceived wetter than lighter ones, despite using the same fabric and applying the same level of physical moisture. This phenomenon was explained in light of the synthetic nature of wetness perception, specifically through the effect of fabric weight on cutaneous perceived pressure which was associated with higher physical wetness in fabrics. In a following skin regional experiment (inner forearm), the individual and combined role of fabric surface texture (contact points with the skin) and fabric thickness on wetness perception as well as stickiness sensation was studied (Chapter 4). In contrast to Study 1, in this experiment, fabrics were examined in dynamic contact conditions with the skin. It was observed that, when pre-wetted (same relative water content, corresponding to 50% of their maximum absorption capacity), fabric materials with a smoother surface (higher contact) resulted in greater skin wetness perception and stickiness sensation compared to the rougher fabric surfaces. Interestingly, the power of wetness perception prediction became stronger when including, together with stickiness, fabric thickness, indicating the important role of these two parameters when developing next to skin clothing. In the same dynamic application, to assess whether texture data can be used as predictors of fabric stickiness sensation, fabric surface texture was quantified using the Kawabata Evaluation System. The results showed that the Kawabata Evaluation System failed to predict stickiness sensation of wet fabrics commonly assumed to be associated with fabric texture, thus a different way to define fabric texture may be needed in order to represent this link (stickiness and texture). Moving from this first research stage, where the impact of textile properties on human perceptual responses was investigated using a mechanistic approach, in the second research phase a more applied approach was adopted. The aim was to study textile parameters and clothing performance in conditions of exercise-induced sweat production as opposed to laboratory-induced wetness conditions. Before investigating human sensorial responses in transient exercise conditions, in Study 3 (Chapter 5) we addressed potential biases which can occur when sensorial scores of temperature, wetness and discomfort are repeatedly reported in transient exercise conditions. We pointed out that, when repeatedly reported, previous sensorial scores can be set by the participants as reference values and the subsequent score may be given based on the previous point of reference, the latter phenomenon leading to a bias which we defined as anchoring bias . Indeed, the findings showed that subsequent sensorial scores are prone to anchoring biases and that the bias consists in a systematically higher magnitude of sensation expressed, as compared to when reported a single time only. As such, the study allowed recognition and mitigation of the identified error, in order to improve the methodological rigour of the following research involving sensorial data in transient exercise conditions. Following from Study 2, where the impact of stickiness sensation on wetness perception was highlighted, in the fourth laboratory study (Chapter 6) we aimed to investigate the combined effect of garment contact area, sweat content and moisture saturation percentage, in conditions of exercise-induced sweat production. Furthermore, the influence that both stickiness sensation and wetness perception have on wear discomfort was studied. The findings showed that fabric saturation percentage mainly affected stickiness sensation of wet fabrics, dominating the impact of fabric contact area and absolute sweat content. On the contrary, wetness perception was not different between garments. This indicated that stickiness sensation and wetness perception are not always strongly related; as such they should both be measured and considered individually. Texture and stickiness sensation presented the best relation with wear discomfort at baseline and during exercise, respectively. Due to the impact of fabric moisture saturation percentage on stickiness sensation and wear discomfort, identified in Study 1 and Study 2, in Study 5 (Chapter 7) we aimed to quantifying temporal and regional sweat absorption in cotton and synthetic upper body garments. Sweat production was induced in male athletes during 50 minutes of running exercise, performed in a warm environment. Considerable variations in sweat absorption were observed over time and between garment regions. Based on these data, we provided temporal and spatial sweat absorption maps which could guide the process of clothing development, using a sweat mapping approach. In Study 5 a destructive gravimetric method was developed to quantify local garment sweat absorption. While this currently is the only methodology that permits direct and analytical measurements of garment regional sweat absorption, the latter approach is time-consuming and expensive, therefore of limited applicability. As such, in study 6 (Chapter 8), it was assessed whether infrared thermography could be used as an indirect method to estimate garment regional sweat absorption, right after exercise, in a non-destructive fashion. Spatial and temporal sweat absorption data, obtained from Study 5, were correlated with spatial and temporal temperature data (also obtained from study 5) measured with an infrared thermal camera. The data suggested that infrared thermography is a good tool to qualitatively predict regional sweat absorption in garments at separate individual time points; however temporal and quantitative changes are not predicted well, due to a moisture threshold causing a temperature limit above which variations in sweat content cannot be discriminated by temperature changes any further. In conclusion, the textile parameters identified in this PhD research as major determinants of fabric absorption capacity and related perceptions are thickness/volume, wet weight, moisture saturation percentage, surface area and surface texture. These textile factors influence wetness-related sensations and perceptions over time, in relation to the over-time changes in human thermophysiological responses (such as metabolic rate and sweating) and to the environmental conditions the person is exposed to. This clearly shows that in a multifactorial system such as the environment-human-clothing one, the strength of different cutaneous moisture-related stimuli, triggered by various textiles parameters, should be considered. Finally, this indicates that, to obtain a better understanding of clothing performance and its impact on human sensation

Fuzzy Modelling of Human Psycho-Physiological State and Fuzzy Adaptive Control of Automation in Human-Machine Interface

This research aims at proposing a new modelling and control framework that monitors the human operators' psychophysiological state in the human-machine interface to prevent performance breakdown. This research started with the exploration of new psychophysiological state assessment approaches to the adaptive modelling and control method for predicting human task performance and balancing the engagement of the human operator and the automatic system. The results of this research may also be further applied in developing advanced control mechanisms, investigating the origins of human compromised performance and identifying or even remedying operators' breakdown in the early stages of operation, at least. A summary of the current human psychophysiological studies, previous human-machine interface simulation and existing biomarkers for human psychophysiological state assessment was provided for simulation experiment design of this research. The use of newly developed facial temperature biomarkers for assessing the human psychophysiological state and the task performance was investigated. The research continued by exploring the uncertainty of the human-machine interface system through the use of the complex fuzzy logic based offline modelling approach. A new type-2 fuzzy-based modelling approach was then proposed to assess the human operators' psychophysiological states in the real-time human-machine interface. This new modelling technique integrated state tracking and type-2 fuzzy sets for updating the rule base with a Bayesian process. Finally, this research included a new type-2 fuzzy logic-based control algorithm for balancing the human-machine interface systems via adjusting the engagement of the human operators according to their psychophysiological state and task performance. This innovative control approach combined the state estimation of the human operator with the type-2 fuzzy sets to maintain the balance between the task requirements (i.e. difficulty level) and the human operator feasible effort (i.e. psychophysiological states). In addition, the research revealed the impacts of multi-tasking and general fatigue on human operator's performance

Exploring Methods to Improve Pressure Ulcer Detection: Spectroscopic Assessment of the Blanch Response

Pressure damage in intact skin is difficult to detect, particularly in individuals with dark skin, because color changes and tissue blanching are masked by the skin's pigmentation. Tissue reflectance spectroscopy (TRS) may be able to detect the blanch response regardless of skin color by measuring the change in total hemoglobin (delta tHb) that occurs when pressure is applied to the skin. The objective of this dissertation was to examine the ability of TRS to detect the blanch response at sites at risk for pressure ulcer development in individuals with various levels of skin pigmentation. Three studies were conducted to address this objective. In Study 1, delta tHb was assessed at the heel and sacrum of light and dark-skinned healthy participants using a portable TRS system. Study 1 showed that a significant decrease (p less than 0.001) in tHb could be measured in both light and dark skinned-participants with good intra-rater reliability (ICC greater than or equal to 0.80) at the heel, but not at the sacrum. Study 2 was conducted to identify a reliable method of skin color description for use in subsequent studies of the spectroscopic blanch response. Two examiners (B and C) performed three skin color assessments at the volar forearm of ten healthy participants using Munsell color tile matching and colorimetry. Intra and inter-rater reliability was excellent for colorimetry (ICCs typically greater than or equal to 0.90). Reliability for Munsell color tile matching was highest for Munsell value within Examiner B (93% agreement, kappa 0.87-1.00), which was determined to be sufficiently high for use in subsequent studies. In Study 3, delta tHb was assessed at the heels of light, moderate, and dark-skinned elderly nursing home residents at risk for pressure ulcers. As in the pilot study, a significant decrease in tHb was observed in all skin color groups (p less than 0.05). Intra-rater reliability for delta tHb was moderate or greater (ICC greater than or equal to 0.61). In combination, the results of Study 1 and Study 3 demonstrated that a significant spectroscopic blanch response could be detected with moderate or greater intra-rater reliability at the heel regardless of age or pressure ulcer risk status

Tissue Damage Characterization Using Non-invasive Optical Modalities

The ability to determine the degree of cutaneous and subcutaneous tissue damage is essential for proper wound assessment and a significant factor for determining patient treatment and morbidity. Accurate characterization of tissue damage is critical for a number of medical applications including surgical removal of nonviable tissue, severity assessment of subcutaneous ulcers, and depth assessment of visually open wounds. The main objective of this research was to develop a non-invasive method for identifying the extent of tissue damage underneath intact skin that is not apparent upon visual examination. This work investigated the relationship between tissue optical properties, blood flow, and tissue viability by testing the hypotheses that (a) changes in tissue oxygenation and/or microcirculatory blood flow measurable by Diffuse Near Infrared Spectroscopy (DNIRS) and Diffuse Correlation Spectroscopy (DCS) differ between healthy and damaged tissue and (b) the magnitude of those changes differs for different degrees of tissue damage. This was accomplished by developing and validating a procedure for measuring microcirculatory blood flow and tissue oxygenation dynamics at multiple depths (up to 1 centimeter) using non-invasive DCS and DNIRS technologies. Due to the lack of pressure ulcer animal models that are compatible with our optical systems, a proof of concept was conducted in a porcine burn model prior to conducting clinical trials in order to assess the efficacy of the system in-vivo. A reduction in total hemoglobin was observed for superficial (5%) and deep burns (35%) along with a statistically significant difference between the optical properties of superficial and deep burns (p < 0.05). Burn depth and viable vessel density were estimated via histological samples. 42% of vessels in the dermal layer were viable for superficial burns, compared to 25% for deep burns. The differences detected in optical properties and hemoglobin content by optical measurements correlated with the extent of tissue injury observed in histological stains. After proof of concept in animals, a human study was conducted and optical data was collected from 20 healthy subjects and 8 patients at risk of developing pressure ulcers. Blood flow index (BFI) values from the sacral region of patients were compared with those of healthy volunteers. Prior to loading measurements, baseline BFI values were measured in subjects in lateral position. These values were systematically higher for patients who developed open ulcers than for the other research subjects. While under the loading position, patients who developed a pressure ulcer had a decrease in BFI from baseline values an order of magnitude larger than healthy subjects (p < 0.01) and patients whose redness dissipated (p < 0.01). The hyperemic response, when pressure was released as the patient was moved back to a lateral position, showed a decreasing trend from one session to the next for patients who developed open ulcers. Overall, this work presents a novel non-invasive method of pressure ulcer assessment and provides an improvement over current assessment methods. The obtained results suggest the system may potentially predict whether non-blanchable redness will develop into an advanced pressure ulcer within four weeks from initial observation.Ph.D., Biomedical Engineering -- Drexel University, 201