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

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

Commissioning and First Observations with Wide FastCam at the Telescopio Carlos S\'anchez

The FastCam instrument platform, jointly developed by the IAC and the UPCT, allows, in real-time, acquisition, selection and storage of images with a resolution that reaches the diffraction limit of medium-sized telescopes. FastCam incorporates a specially designed software package to analyse series of tens of thousands of images in parallel with the data acquisition at the telescope. Wide FastCam is a new instrument that, using the same software for data acquisition, does not look for lucky imaging but fast observations in a much larger field of view. Here we describe the commissioning process and first observations with Wide FastCam at the Telescopio Carlos S\'anchez (TCS) in the Observatorio del Teide.Comment: 7 pages, 8 figures, Proc. SPIE. 9908, Ground-based and Airborne Instrumentation for Astronomy VI, 99082O. (August 09, 2016

An intelligent method to estimate the lactate threshold accessibly and non-invasively in recreational runners

[Resumen] El umbral de lactato es considerado una variable fisiológica útil como apoyo a prescripción de entrenamientos y como indicador del rendimiento en deportes de resistencia. Hoy en día no existe un método fiable para evaluarlo sin hacer uso de equipamiento especializado y/o acudir a costosos centros, de manera que su uso está restringido a aquellos atletas que dispongan de estos recursos. Este trabajo propone un método inteligente para estimar el umbral de lactato de manera eficiente, accesible y no invasiva para que una población más amplia pueda tener acceso a ella. Para ello, se propuso un método basado en relativización de factores combinado con redes neuronales recurrentes que en este trabajo ha continuado desarrollándose enfocándolo hacía la mejora de la capacidad de generalización, calibrándolo con una base de datos nueva. Como resultado, se ha alcanzado una capacidad de generalización del 87%. Esto indica que el método aquí presentado es una herramienta válida para estimar el umbral de lactato de manera accesible y no invasiva en corredores recreacionales.[Abstract] Lactate threshold is considered an essential physiological variable useful for endurance sports as an aid for training prescription and performance evaluation. However, nowadays there is no reliable way to asses it without specialized equipment or without turning to expensive centres, meaning that it is restricted to few people with access to these resources. Thus, this work proposes a cost-efficient, non-invasive and easily accessible intelligent method to estimate the lactate threshold and so making it accessible to a wider population. A new strategy based on feature standardization combined with Recurrent Neural Network was proposed to model the lactate threshold. In this work, this method is further developed to increase its generalization power and calibrated against a new database. The results show that this system successfully estimates the lactate threshold in 87% of the cases, meaning that our model is a valid accessible tool for lactate threshold assessment.Universidad del País Vasco; PPG17/56Universidad del País Vasco; PPG/17/40Gobierno Vasco; PRE 2015 1 012

Application of supervised and unsupervised classification approaches for the estimation of lactate threshold of endurance runners

[Resumen] Hoy en día el running es, sin lugar a dudas, uno de los deportes más populares que se incorpora como hábito saludable en la vida cotidiana. Dicha práctica deportiva se ve además incentivada por la celebración de múltiples competiciones populares, lo que ha favorecido el cada vez mayor interés por mejorar el rendimiento deportivo así como la seguridad del y la deportista. En este sentido, en la última década se ha extendido enormemente la utilización de Relojes Inteligentes para monitorizar variables fisiológicas. Sin embargo, no todas las variables fisiológicas de interés pueden ser monitorizadas, destacando entre ellas el lactato, cuya medida requiere de muestras de sangre. La curva de lactato permite extraer el umbral de lactato, el cual es fundamental a la hora de planificar entrenamientos y conocer el estado del atleta. Por ello, en este trabajo se propone el diseño y desarrollo de un sensor virtual de lactato. Más concretamente, en este artículo se explora la aplicación de técnicas de agrupamiento de deportistas como vía hacia una estimación personalizada del umbral de lactato.[Abstract] Nowadays, running is one of the most popular sports and it is considered a healthy habit in everyday life. This sport practice is also encouraged by the celebration of multiple popular competitions, which are driving a growing interest in improving athletic performance as well as the safety of the runners. In this sense, in the last decade the use of Smart Watches to monitor physiological variables is becoming more and more common. However, not all the physiological variables of interest can be monitored, among them the lactate, whose measurement requires blood sampling. The lactate curve enables to extract the lactate threshold, which is essential for training-load prescription and assessment. In this work the design and development of a virtual lactate sensor is proposed. More specifically, this article analyzes the applicability of grouping techniques as an alternative towards a personalized estimation of the lactate threshold.Universidad del País Vasco UPV/EHU; GIU18/162Universidad del País Vasco UPV/EHU; PPG17/56Universidad del País Vasco UPV/EHU; PPG/17/40Gobierno Vasco, Departamento de Educación; PRE 2015 1 0129Programa ERASMUS MUNDUS PANTHER; PN/TG1/AUT/PhD/06/201