899 research outputs found

    Non-invasive anaerobic threshold measurement using fuzzy model interpolation

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    The interface between skeletal muscle activation through aerobic and anaerobic glycolysis is of key interest to sportspeople and athletes who participate in medium to long distance sports, such as middle- and long-distance running, cycling, swimming, rowing, kayaking and a variety of other events. To date, the gold standard for measuring anaerobic threshold (AT) is a structured test to exhaustion where blood lactate concentration is measured at regular intervals. However, the need for invasive testing, requiring trained personnel and specialist equipment, limits the availability of such tests. This paper proposes a non-invasive AT measurement method, which validates well against AT measured using lactate analysis. In addition, the proposed test has a relatively loose set of requirements on the exercise test protocol required and just requires a measure of exercise intensity and heart-rate. While the test is applicable to a range of sports, usage is demonstrated in this paper for a set of cyclists, using velocity as a measure of exercise intensity

    An intelligent strategy for endurance training based on a virtual lactate sensor

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    Capítulo 5 confidencial a solicitud del autor. Tesis completa 210 p. --- Tesis censurada 196 p.In this thesis, a first fully operational virtual LT sensor was created for recreational runners. This way, a so demanded operational solution to help the training of recreational runners was created. Moreover, the Lactatus software was created to guide, ease the athletes' LT estimation process and implement the additional information obtained in this thesis into their training decision-making process. This way, the work of this thesis is made tangible, widely available and usable to recreational runners. This solution grew from the creation and formalization of a strategy to help pose and apply ML to complex phenomena, an important contribution of this thesis. This strategy combined an iterative meta-process and a satisficing approach to deal with the problem boundary discovery and reduce the problem complexity. Then, the design of the virtual LT sensor was divided into three steps: context characterization, content representation and next step decision. The formalization of this methodology and a modification of next step decision are novel contributions. Additionally, several novel techniques are used, including a standardization of the temporal axis, a modified stratified sampling and a computational algorithm to discover the inherent noise that the features may contain. This way, a robust strategy and methodology is created to design virtual sensors for problems with similar characteristics. The application of this methodology led to an important conclusion. Concretely, the Dmax LT intrinsic error analysis showed that a higher accuracy of the virtual LT sensor was unnecessary and even non-characterizable. This manifested the importance of understanding the variability of the output features with respect to the input errors. The computation algorithm also allows to LT protocols could also be evaluated from this perspective in order to quantitatively address their reliability. This may allow to make an objective cross-comparison of the accuracy of different LT protocols, something that, is not well addressed in the literature. One of the possible limitations of this solution is that the recreational runner population here characterized may not be representative of recreational runners of other culture, ethnicity or different contexts. However, one of the main advantages of providing a simple solution is that, unlike other black-box models, it is easily reproducible and adjustable, meaning that we have set a common ground for other researchers to evaluate the impact of our proposal. In the best-case scenario, future experiments done in other contexts will validate that we have been capable of discovering a common characteristic of recreational runner population. In the worst-case scenario, we have provided an easy to follow methodology and a strong prior that will allow to adjust the estimator according to individual characteristics of different populations

    Heterogeneidad tumoral en imágenes PET-CT

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    Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Estructura de la Materia, Física Térmica y Electrónica, leída el 28/01/2021Cancer is a leading cause of morbidity and mortality [1]. The most frequent cancers worldwide are non–small cell lung carcinoma (NSCLC) and breast cancer [2], being their management a challenging task [3]. Tumor diagnosis is usually made through biopsy [4]. However, medical imaging also plays an important role in diagnosis, staging, response to treatment, and recurrence assessment [5]. Tumor heterogeneity is recognized to be involved in cancer treatment failure, with worse clinical outcomes for highly heterogeneous tumors [6,7]. This leads to the existence of tumor sub-regions with different biological behavior (some more aggressive and treatment-resistant than others) [8-10]. Which are characterized by a different pattern of vascularization, vessel permeability, metabolism, cell proliferation, cell death, and other features, that can be measured by modern medical imaging techniques, including positron emission tomography/computed tomography (PET/CT) [10-12]. Thus, the assessment of tumor heterogeneity through medical images could allow the prediction of therapy response and long-term outcomes of patients with cancer [13]. PET/CT has become essential in oncology [14,15] and is usually evaluated through semiquantitative metabolic parameters, such as maximum/mean standard uptake value (SUVmax, SUVmean) or metabolic tumor volume (MTV), which are valuables as prognostic image-based biomarkers in several tumors [16-17], but these do not assess tumor heterogeneity. Likewise, fluorodeoxyglucose (18F-FDG) PET/CT is important to differentiate malignant from benign solitary pulmonary nodules (SPN), reducing so the number of patients who undergo unnecessary surgical biopsies. Several publications have shown that some quantitative image features, extracted from medical images, are suitable for diagnosis, tumor staging, the prognosis of treatment response, and long-term evolution of cancer patients [18-20]. The process of extracting and relating image features with clinical or biological variables is called “Radiomics” [9,20-24]. Radiomic parameters, such as textural features have been related directly to tumor heterogeneity [25]. This thesis investigated the relationships of the tumor heterogeneity, assessed by 18F-FDG-PET/CT texture analysis, with metabolic parameters and pathologic staging in patients with NSCLC, and explored the diagnostic performance of different metabolic, morphologic, and clinical criteria for classifying (malignant or not) of solitary pulmonary nodules (SPN). Furthermore, 18F-FDG-PET/CT radiomic features of patients with recurrent/metastatic breast cancer were used for constructing predictive models of response to the chemotherapy, based on an optimal combination of several feature selection and machine learning (ML) methods...El cáncer es una de las principales causas de morbilidad y mortalidad. Los más frecuentes son el carcinoma de pulmón de células no pequeñas (NSCLC) y el cáncer de mama, siendo su tratamiento un reto. El diagnóstico se suele realizar mediante biopsia. La heterogeneidad tumoral (HT) está implicada en el fracaso del tratamiento del cáncer, con peores resultados clínicos para tumores muy heterogéneos. Esta conduce a la existencia de subregiones tumorales con diferente comportamiento biológico (algunas más agresivas y resistentes al tratamiento); las cuales se caracterizan por diferentes patrones de vascularización, permeabilidad de los vasos sanguíneos, metabolismo, proliferación y muerte celular, que se pueden medir mediante imágenes médicas, incluida la tomografía por emisión de positrones/tomografía computarizada con fluorodesoxiglucosa (18F-FDG-PET/CT). La evaluación de la HT a través de imágenes médicas, podría mejorar la predicción de la respuesta al tratamiento y de los resultados a largo plazo, en pacientes con cáncer. La 18F-FDG-PET/CT es esencial en oncología, generalmente se evalúa con parámetros metabólicos semicuantitativos, como el valor de captación estándar máximo/medio (SUVmáx, SUVmedio) o el volumen tumoral metabólico (MTV), que tienen un gran valor pronóstico en varios tumores, pero no evalúan la HT. Asimismo, es importante para diferenciar los nódulos pulmonares solitarios (NPS) malignos de los benignos, reduciendo el número de pacientes que van a biopsias quirúrgicas innecesarias. Publicaciones recientes muestran que algunas características cuantitativas, extraídas de las imágenes médicas, son robustas para diagnóstico, estadificación, pronóstico de la respuesta al tratamiento y la evolución, de pacientes con cáncer. El proceso de extraer y relacionar estas características con variables clínicas o biológicas se denomina “Radiomica”. Algunos parámetros radiómicos, como la textura, se han relacionado directamente con la HT. Esta tesis investigó las relaciones entre HT, evaluada mediante análisis de textura (AT) de imágenes 18F-FDG-PET/CT, con parámetros metabólicos y estadificación patológica en pacientes con NSCLC, y exploró el rendimiento diagnóstico de diferentes criterios metabólicos, morfológicos y clínicos para la clasificación de NPS. Además, se usaron características radiómicas de imágenes 18F-FDG-PET/CT de pacientes con cáncer de mama recurrente/metastásico, para construir modelos predictivos de la respuesta a la quimioterapia, combinándose varios métodos de selección de características y aprendizaje automático (ML)...Fac. de Ciencias FísicasTRUEunpu

    Aerospace medicine and biology: A continuing bibliography with indexes (supplement 402)

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    This bibliography lists 244 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Nov. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

    Deep learning methods for improving diabetes management tools

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    Diabetes is a chronic disease that is characterised by a lack of regulation of blood glucose concentration in the body, and thus elevated blood glucose levels. Consequently, affected individuals can experience extreme variations in their blood glucose levels with exogenous insulin treatment. This has associated debilitating short-term and long-term complications that affect quality of life and can result in death in the worst instance. The development of technologies such as glucose meters and, more recently, continuous glucose monitors have offered the opportunity to develop systems towards improving clinical outcomes for individuals with diabetes through better glucose control. Data-driven methods can enable the development of the next generation of diabetes management tools focused on i) informativeness ii) safety and iii) easing the burden of management. This thesis aims to propose deep learning methods for improving the functionality of the variety of diabetes technology tools available for self-management. In the pursuit of the aforementioned goals, a number of deep learning methods are developed and geared towards improving the functionality of the existing diabetes technology tools, generally classified as i) self-monitoring of blood glucose ii) decision support systems and iii) artificial pancreas. These frameworks are primarily based on the prediction of glucose concentration levels. The first deep learning framework we propose is geared towards improving the artificial pancreas and decision support systems that rely on continuous glucose monitors. We first propose a convolutional recurrent neural network (CRNN) in order to forecast the glucose concentration levels over both short-term and long-term horizons. The predictive accuracy of this model outperforms those of traditional data-driven approaches. The feasibility of this proposed approach for ambulatory use is then demonstrated with the implementation of a decision support system on a smartphone application. We further extend CRNNs to the multitask setting to explore the effectiveness of leveraging population data for developing personalised models with limited individual data. We show that this enables earlier deployment of applications without significantly compromising performance and safety. The next challenge focuses on easing the burden of management by proposing a deep learning framework for automatic meal detection and estimation. The deep learning framework presented employs multitask learning and quantile regression to safely detect and estimate the size of unannounced meals with high precision. We also demonstrate that this facilitates automated insulin delivery for the artificial pancreas system, improving glycaemic control without significantly increasing the risk or incidence of hypoglycaemia. Finally, the focus shifts to improving self-monitoring of blood glucose (SMBG) with glucose meters. We propose an uncertainty-aware deep learning model based on a joint Gaussian Process and deep learning framework to provide end users with more dynamic and continuous information similar to continuous glucose sensors. Consequently, we show significant improvement in hyperglycaemia detection compared to the standard SMBG. We hope that through these methods, we can achieve a more equitable improvement in usability and clinical outcomes for individuals with diabetes.Open Acces

    Acta kinesiologiae Universitatis Tartuensis. 5

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    http://www.ester.ee/record=b1227224*es

    MATLAB

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    A well-known statement says that the PID controller is the "bread and butter" of the control engineer. This is indeed true, from a scientific standpoint. However, nowadays, in the era of computer science, when the paper and pencil have been replaced by the keyboard and the display of computers, one may equally say that MATLAB is the "bread" in the above statement. MATLAB has became a de facto tool for the modern system engineer. This book is written for both engineering students, as well as for practicing engineers. The wide range of applications in which MATLAB is the working framework, shows that it is a powerful, comprehensive and easy-to-use environment for performing technical computations. The book includes various excellent applications in which MATLAB is employed: from pure algebraic computations to data acquisition in real-life experiments, from control strategies to image processing algorithms, from graphical user interface design for educational purposes to Simulink embedded systems

    spinfortec2022 : Tagungsband zum 14. Symposium der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung für Sportwissenschaft (dvs), Chemnitz 29. - 30. September 2022

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    Dieser Tagungsband enthält die Beiträge aller Vorträge und Posterpräsentationen des 14. Symposiums der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung für Sportwissenschaft (dvs) an der Technischen Universität Chemnitz (29.-30. September 2022). Mit dem Ziel, das Forschungsfeld der Sportinformatik und Sporttechnologie voranzubringen, wurden knapp 20 vierseitige Beiträge eingereicht und in den Sessions Informations- und Feedbacksysteme im Sport, Digitale Bewegung: Datenerfassung, Analyse und Algorithmen sowie Sportgeräteentwicklung: Materialien, Konstruktion, Tests vorgestellt.This conference volume contains the contributions of all oral and poster presentations of the 14th Symposium of the Section Sport Informatics and Engineering of the German Association for Sport Science (dvs) at Chemnitz University of Technology (September 29-30, 2022). With the goal of advancing the research field of sports informatics and sports technology, nearly 20 four-page papers were submitted and presented in the sessions Information and Feedback Systems in Sport, Digital Movement: Data Acquisition, Analysis and Algorithms, and Sports Equipment Development: Materials, Construction, Testing
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