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Endometriosis (EM) is a chronic inflammatory estrogen-dependent disorder that affects 10% of women worldwide. It affects the female reproductive tract and its resident microbiota, as well as distal body sites that can serve as surrogate markers of EM. Currently, no single definitive biomarker can diagnose EM. For this pilot study, we analyzed a cohort of 21 patients with endometriosis and infertility-associated conditions. A microbiome dataset was created using five sample types taken from the reproductive and gastrointestinal tracts of each patient. We evaluated several machine learning algorithms for EM detection using these features. The characteristics of the dataset were derived from endometrial biopsy, endometrial fluid, vaginal, oral, and fecal samples. Despite limited data, the algorithms demonstrated high performance with respect to the F1 score. In addition, they suggested that disease diagnosis could potentially be improved by using less medically invasive procedures. Overall, the results indicate that machine learning algorithms can be useful tools for diagnosing endometriosis in low-resource settings where data availability and availability are limited. We recommend that future studies explore the complexities of the EM disorder using artificial intelligence and prediction modeling to further define the characteristics of the endometriosis phenotype.

The influence of sleep on human life, including physiological, psychological, and mental aspects, is remarkable. Therefore, it is essential to apply appropriate therapy in the case of sleep disorders. For this, however, the irregularities must first be recognised, preferably conveniently for the person concerned. This dissertation, structured as a composition of research articles, presents the development of mathematically based algorithmic principles for a sleep analysis system. The particular focus is on the classification of sleep stages with a minimal set of physiological parameters. In addition, the aspects of using the sleep analysis system as part of the more complex healthcare systems are explored. Design of hardware for non-obtrusive measurement of relevant physiological parameters and the use of such systems to detect other sleep disorders, such as sleep apnoea, are also referred to. Multinomial logistic regression was selected as the basis for development resulting from the investigations carried out. By following a methodical procedure, the number of physiological parameters necessary for the classification of sleep stages was successively reduced to two: Respiratory and Movement signals. These signals might be measured in a contactless way. A prototype implementation of the developed algorithms was performed to validate the proposed method, and the evaluation of 19324 sleep epochs was carried out. The results, with the achieved accuracy of 73% in the classification of Wake/NREM/REM stages and Cohen's kappa of 0.44, outperform the state of the art and demonstrate the appropriateness of the selected approach. In the future, this method could enable convenient, cost-effective, and accurate sleep analysis, leading to the detection of sleep disorders at an early stage so that therapy can be initiated as soon as possible, thus improving the general population's health status and quality of life., La influencia del sueño en la vida humana, incluidos los aspectos fisiológicos, psicológicos y mentales, es notable. Por tanto, es fundamental aplicar la terapia adecuada para los trastornos del sueño. Para conseguirlo, es necesario en primer término reconocer los trastornos que presenta la persona afectada. En esta tesis, estructurada como un compendio de artículos de investigación, se presenta el desarrollo de los principios algorítmicos con base matemática para un sistema de análisis del sueño. Se hace especial hincapié en la clasificación de las etapas del sueño con un conjunto mínimo de parámetros fisiológicos. Además, se exploran los aspectos de la utilización del sistema de análisis del sueño como parte de sistemas sanitarios más complejos. También se hace referencia al diseño de hardware para la medición no intrusiva de los parámetros fisiológicos relevantes en el análisis del sueño y el uso de estos sistemas para detectar otros trastornos, como el de la apnea del sueño. Se ha seleccionado la regresión logística multinomial como base del desarrollo en las investigaciones realizadas. Siguiendo un procedimiento metódico, el número de parámetros fisiológicos necesarios para la clasificación de las etapas del sueño se ha ido reduciendo hasta conseguirlo con tan sólo dos: señales respiratorias y de movimiento. Estas señales pueden medirse de manera no invasiva, esto es, sin contacto. La validar del método propuesto en la tesis se ha conseguido implementando un prototipo con los algoritmos desarrollados y se han probado evaluando 19324 épocas de sueño. Los resultados obtenidos han conseguido una precisión del 73% en la clasificación de las etapas de vigilia/NREM/REM y un kappa de Cohen de 0,44, mejorando a los existentes en el estado del arte y demostrando la idoneidad del enfoque realizado. En el futuro, este método podría permitir un análisis del sueño cómodo, rentable y preciso, lo que permitiría detectar los trastornos del sueño en una fase temprana para poder

The development of healthcare patient digital twins in combination with machine learning technologies helps doctors in therapeutic prescription and in minimally invasive intervention procedures. The confidentiality of medical records or limited data availability in many health domains are drawbacks that can be overcome with the generation of synthetic data conformed to real data. The use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients has been previously introduced as a tool to solve this problem in the form of anonymized synthetic patients. However, generated synthetic data are mainly validated from the machine learning domain (loss functions) or expert domain (oncologists). In this paper, we propose statistical decision making as a validation tool: Is the model good enough to be used? Does the model pass rigorous hypothesis testing criteria? We show for the case at hand how loss functions and hypothesis validation are not always well aligned.

Healthy sleep is required for sufficient restoration of the human body and brain. Therefore, in the case of sleep disorders, appropriate therapy should be applied timely, which requires a prompt diagnosis. Traditionally, a sleep diary is a part of diagnosis and therapy monitoring for some sleep disorders, such as cognitive behaviour therapy for insomnia. To automatise sleep monitoring and make it more comfortable for users, substituting a sleep diary with a smartwatch measurement could be considered. With the aim of providing accurate results, a study with a total of 30 night recordings was conducted. Objective sleep measurement with a Samsung Galaxy Watch 4 was compared with a subjective approach (sleep diary), evaluating the four relevant sleep characteristics: time of getting asleep, wake up time, sleep efficiency (SE), and total sleep time (TST). The performed analysis has demonstrated that the median difference between both measurement approaches was equal to 7 and 3 minutes for a time of getting asleep and wake up time correspondingly, which allows substituting a subjective measurement with a smartwatch. The SE was determined with a median difference between the two measurement methods of 5.22%. This result also implicates a possibility of substitution. Some single recordings have indicated a higher variance between the two approaches. Therefore, the conclusion can be made that a substitution provides reliable results primarily in the case of long-term monitoring. The results of the evaluation of the TST measurement do not allow to recommend substitution of the measurement method.

In the area of human digital twins, the designed model should be as close as possible to the reality. The weight variable is one of the interested parameters of these mathematical models, as well as its interaction with other vital signals. The aim of this paper is including the information gathered from weight sensors of a person in its digital twin. To do this it is described an algorithm that filters and forecasts the human weight to use it in indexes such as BMI. It has been applied to a real sample and the results obtained are good

Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive. Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes. We present the literature review of non-invasive sound acquisition devices and techniques. The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope. Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.

Sustainable technologies are being increasingly used in various areas of human life. While they have a multitude of benefits, they are especially useful in health monitoring, especially for certain groups of people, such as the elderly. However, there are still several issues that need to be addressed before its use becomes widespread. This work aims to clarify the aspects that are of great importance for increasing the acceptance of the use of this type of technology in the elderly. In addition, we aim to clarify whether the technologies that are already available are able to ensure acceptable accuracy and whether they could replace some of the manual approaches that are currently being used. A two-week study with people 65 years of age and over was conducted to address the questions posed here, and the results were evaluated. It was demonstrated that simplicity of use and automatic functioning play a crucial role. It was also concluded that technology cannot yet completely replace traditional methods such as questionnaires in some areas. Although the technologies that were tested were classified as being “easy to use”, the elderly population in the current study indicated that they were not sure that they would use these technologies regularly in the long term because the added value is not always clear, among other issues. Therefore, awareness-raising must take place in parallel with the development of technologies and service

The digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages.

The digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages., Peer Reviewed, Postprint (published version)

The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.

This document presents a new complete standalone system for a recognition of sleep apnea using signals from the pressure sensors placed under the mattress. The developed hardware part of the system is tuned to filter and to amplify the signal. Its software part performs more accurate signal filtering and identification of apnea events. The overall achieved accuracy of the recognition of apnea occurrence is 91%, with the average measured recognition delay of about 15 seconds, which confirms the suitability of the proposed method for future employment. The main aim of the presented approach is the support of the healthcare system with the cost-efficient tool for recognition of sleep apnea in the home environment.

The main objective in this proposal is to orchestrate an ecosystem of manipulation of reliable and safe data, applied to the field of health, specifically lung cancer, by introducing the creation of digital twins for personalised healthcare about the behaviour of this disease on patients. Digital twins is a very popular and novel approach in digitisation units in industry which will be used by both kind of experts: (i) data analysts, who will design expert recommender systems and extract knowledge – explainable Artificial Intelligence (AI); and (ii) professionals in medicine, who will consume that knowledge generated with their research for better diagnosis. This knowledge generation/extraction process will work in the form of a lifelong learning system by iterative and continuous use. The produced software platform will be abstracted so it can be applied like a general purpose service tool in other domains of knowledge, specially health and industry. Furthermore, a rule extraction module will be made available for explainability issues.

Cardiovascular diseases are directly or indirectly responsible for up to 38.5% of all deaths in Germany and thus represent the most frequent cause of death. At present, heart diseases are mainly discovered by chance during routine visits to the doctor or when acute symptoms occur. However, there is no practical method to proactively detect diseases or abnormalities of the heart in the daily environment and to take preventive measures for the person concerned. Long-term ECG devices, as currently used by physicians, are simply too expensive, impractical, and not widely available for everyday use. This work aims to develop an ECG device suitable for everyday use that can be worn directly on the body. For this purpose, an already existing hardware platform will be analyzed, and the corresponding potential for improvement will be identified. A precise picture of the existing data quality is obtained by metrological examination, and corresponding requirements are defined. Based on these identified optimization potentials, a new ECG device is developed. The revised ECG device is characterized by a high integration density and combines all components directly on one board except the battery and the ECG electrodes. The compact design allows the device to be attached directly to the chest. An integrated microcontroller allows digital signal processing without the need for an additional computer. Central features of the evaluation are a peak detection for detecting R-peaks and a calculation of the current heart rate based on the RR interval. To ensure the validity of the detected R-peaks, a model of the anatomical conditions is used. Thus, unrealistic RR-intervals can be excluded. The wireless interface allows continuous transmission of the calculated heart rate. Following the development of hardware and software, the results are verified, and appropriate conclusions about the data quality are drawn. As a result, a very compact and wearable ECG device with different wireless t

In previous studies, we used a method for detecting stress that was based exclusively on heart rate and ECG for differentiation between such situations as mental stress, physical activity, relaxation, and rest. As a response of the heart to these situations, we observed different behavior in the Root Mean Square of the Successive differences heartbeats (RMSSD). This study aims to analyze Virtual Reality via a virtual reality headset as an effective stressor for future works. The value of the Root Mean Square of the Successive Differences is an important marker for the parasympathetic effector on the heart and can provide information about stress. For these measurements, the RR interval was collected using a breast belt. In these studies, we can observe the Root Mean Square of the successive differences heartbeats. Additional sensors for the analysis were not used. We conducted experiments with ten subjects that had to drive a simulator for 25 minutes using monitors and 25 minutes using virtual reality headset. Before starting and after finishing each simulation, the subjects had to complete a survey in which they had to describe their mental state. The experiment results show that driving using virtual reality headset has some influence on the heart rate and RMSSD, but it does not significantly increase the stress of driving.

Polysomnography is a gold standard for a sleep study, and it provides very accurate results, but its cost (both personnel and material) are quite high. Therefore, the development of a low-cost system for overnight breathing and heartbeat monitoring, which provides more comfort while recording the data, is a well-motivated challenge. The system proposed in this manuscript is based on the usage of resistive pressure sensors installed under the mattress. These sensors can measure slight pressure changes provoked during breathing and heartbeat. The captured signal requires advanced processing, like applying filters and amplifiers before the analog signal is ready for the next step. Then, the output signal is digitalized and further processed by an algorithm that performs a custom filtering before it can recognize breathing and heart rate in real-time. The result can be directly visualized. Furthermore, a CSV file is created containing the raw data, timestamps, and unique IDs to facilitate further processing. The achieved results are promising, and the average deviation from a reference device is about 4bpm.

Good sleep is crucial for a healthy life of every person. Unfortunately, its quality often decreases with aging. A common approach to measuring the sleep characteristics is based on interviews with the subjects or letting them fill in a daily questionnaire and afterward evaluating the obtained data. However, this method has time and personal costs for the interviewer and evaluator of responses. Therefore, it would be important to execute the collection and evaluation of sleep characteristics automatically. To do that, it is necessary to investigate the level of agreement between measurements performed in a traditional way using questionnaires and measurements obtained using electronic monitoring devices. The study presented in this manuscript performs this investigation, comparing such sleep characteristics as "time going to bed", "total time in bed", "total sleep time" and "sleep efficiency". A total number of 106 night records of elderly persons (aged 65+) were analyzed. The results achieved so far reveal the fact that the degree of agreement between the two measurement methods varies substantially for different characteristics, from 31 minutes of mean difference for "time going to bed" to 77 minutes for "total sleep time". For this reason, a direct exchange of objective and subjective measuring methods is currently not possible.

The main aim of presented in this manuscript research is to compare the results of objective and subjective measurement of sleep quality for older adults (65+) in the home environment. A total amount of 73 nights was evaluated in this study. Placing under the mattress devicewas used to obtain objective measurement data, and a common question on perceived sleep quality was asked to collect the subjective sleep quality level. The achieved results confirm the correlation between objective and subjective measurement of sleep quality with the average standard deviation equal to 2 of 10 possible quality points.

Methods based exclusively on heart rate hardly allow to differentiate between physical activity, stress, relaxation, and rest, that is why an additional sensor like activity/movement sensor added for detection and classification. The response of the heart to physical activity, stress, relaxation, and no activity can be very similar. In this study, we can observe the influence of induced stress and analyze which metrics could be considered for its detection. The changes in the Root Mean Square of the Successive Differences provide us with information about physiological changes. A set of measurements collecting the RR intervals was taken. The intervals are used as a parameter to distinguish four different stages. Parameters like skin conductivity or skin temperature were not used because the main aim is to maintain a minimum number of sensors and devices and thereby to increase the wearability in the future.

This document presents an algorithm for a nonobtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen’s kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement.

The goal of the presented project is to develop the concept of home ehealth centers for barrier-free and cross-border telemedicine. AAL technologies are already present on the market but there is still a gap to close until they can be used for ordinary patient needs. The general idea needs to be accompanied by new services, which should be brought together in order to provide a full coverage of service for the users. Sleep and stress were chosen as predominant diseases for a detailed study within this project because of their widespread influence in the population. The executed scientific study of available home devices analyzing sleep has provided the necessary to select appropriate devices. The first choice for the project implementation is the device EMFIT QS+. This equipment provides a part of a complete system that a home telemedical hospital can provide at a level of precision and communication with internal and/or external health services.

The main aim of this short position paper is to introduce a proposal to orchestrate an ecosystem of manipulation of reliable and safe data, applied to the field of health, proposing the creation of digital twins for personalised healthcare. These will be used by both kind of experts: (i) data analysts, who will design expert recommender systems and extract knowledge (explainable AI); and (ii) professionals in medicine, who will consume that knowledge generated with their research for better diagnosis. Knowledge generation / extraction processes will work in the form of a lifelong learning system by iterative and continuous use. The produced platform would be abstracted so it can be applied like a general purpose service tool in other domains. A rule extraction module would be also made available., Peer Reviewed, Postprint (published version)

This Doctoral Dissertation, presented as a set of research contributions, focuses on resource efficiency in data centers. This topic has been faced mainly by the development of several energy-efficiency, resource managing and scheduling policies, as well as the simulation tools required to test them in realistic cloud computing environments. Several models have been implemented in order to minimize energy consumption in Cloud Computing environments. Among them: a) Fifteen probabilistic and deterministic energy-policies which shut-down idle machines; b) Five energy-aware scheduling algorithms, including several genetic algorithm models; c) A Stackelberg game-based strategy which models the concurrency between opposite requirements of Cloud-Computing systems in order to dynamically apply the most optimal scheduling algorithms and energy-efficiency policies depending on the environment; and d) A productive analysis on the resource efficiency of several realistic cloud–computing environments. A novel simulation tool called SCORE, able to simulate several data-center sizes, machine heterogeneity, security levels, workload composition and patterns, scheduling strategies and energy-efficiency strategies, was developed in order to test these strategies in large-scale cloud-computing clusters. As results, more than fifty Key Performance Indicators (KPI) show that more than 20% of energy consumption can be reduced in realistic high-utilization environments when proper policies are employed., Esta Tesis Doctoral, que se presenta como compendio de artículos de investigación, se centra en la eficiencia en la utilización de los recursos en centros de datos de internet. Este problema ha sido abordado esencialmente desarrollando diferentes estrategias de eficiencia energética, gestión y distribución de recursos, así como todas las herramientas de simulación y análisis necesarias para su validación en entornos realistas de Cloud Computing. Numerosas estrategias han sido desarrolladas para minimizar el consumo energético en entornos de Cloud Computing. Entre ellos: 1. Quince políticas de eficiencia energética, tanto probabilísticas como deterministas, que apagan máquinas en estado de espera siempre que sea posible; 2. Cinco algoritmos de distribución de tareas que tienen en cuenta el consumo energético, incluyendo varios modelos de algoritmos genéticos; 3. Una estrategia basada en la teoría de juegos de Stackelberg que modela la competición entre diferentes partes de los centros de datos que tienen objetivos encontrados. Este modelo aplica dinámicamente las estrategias de distribución de tareas y las políticas de eficiencia energética dependiendo de las características del entorno; y 4. Un análisis productivo sobre la eficiencia en la utilización de recursos en numerosos escenarios de Cloud Computing. Una nueva herramienta de simulación llamada SCORE se ha desarrollado para analizar las estrategias antes mencionadas en clústers de Cloud Computing de grandes dimensiones. Los resultados obtenidos muestran que se puede conseguir un ahorro de energía superior al 20% en entornos realistas de alta utilización si se emplean las estrategias de eficiencia energética adecuadas. SCORE es open source y puede simular diferentes centros de datos con, entre otros muchos, los siguientes parámetros: Tamaño del centro de datos; heterogeneidad de los servidores; tipo, composición y patrones de carga de trabajo, estrategias de distribución de tareas y políticas de eficiencia energ

Cloud computing and data centers that support this paradigm are rapidly evolving in order to satisfy new demands. These ever-growing needs represent an energy-related challenge to achieve sustainability and cost reduction. In this paper, we define an expert and intelligent system that applies various en ergy policies. These policies are employed to maximize the energy-efficiency of data-center resources by simulating a realistic environment and heterogeneous workload in a trustworthy tool. An environmental and economic impact of around 20% of energy consumption can be saved in high-utilization scenarios without exerting any noticeable impact on data-center performance if an adequate policy is applied.

The process of restoring our body and brain from fatigue is directly depending on the quality of sleep. It can be determined from the report of the sleep study results. Classification of sleep stages is the first step of this study and this includes the measurement of biovital data and its further processing. In this work, the sleep analysis system is based on a hardware sensor net, namely a grid of 24 pressure sensors, supporting sleep phase recognition. In com-parison to the leading standard, which is polysomnography, the proposed ap-proach is a non-invasive system. It recognises respiration and body movement with only one type of low-cost pressure sensors forming a mesh architecture. The nodes implement as a series of pressure sensors connected to a low-power and performant microcontroller. All nodes are connected via a system wide bus with address arbitration. The embedded processor is the mesh network endpoint that enables network configuration, storing and pre-processing of the data, external data access and visualization. The system was tested by executing experiments recording the sleep of differ-ent healthy young subjects. The results obtained have indicated the potential to detect breathing rate and body movement. A major difference of this system in comparison to other approaches is the innovative way to place the sensors under the mattress. This characteristic facilitates the continuous using of the system without any influence on the common sleep process.

This paper presents an algorithm for non-invasive sleep stage identification using respiratory, heart rate and movement signals. The algorithm is part of a system suitable for longterm monitoring in a home environment, which should support experts analysing sleep. Approach: As there is a strong correlation between bio-vital signals and sleep stages, multinomial logistic regression was chosen for categorical distribution of sleep stages. Several derived parameters of three signals (respiratory, heart rate and movement) are input for the proposed method. Sleep recordings of five subjects were used for the training of a machine learning model and 30 overnight recordings collected from 30 individuals with about 27 000 epochs of 30 s intervals each were evaluated. Main results: The achieved rate of accuracy is 72% for Wake, NREM, REM (with Cohen’s kappa value 0.67) and 58% for Wake, Light (N1 and N2), Deep (N3) and REM stages (Cohen’s kappa is 0.50). Our approach has confirmed the potential of this method and disclosed several ways for its improvement. Significance: The results indicate that respiratory, heart rate and movement signals can be used for sleep studies with a reasonable level of accuracy. These inputs can be obtained in a non-invasive way applying it in a home environment. The proposed system introduces a convenient approach for a long-term monitoring system which could support sleep laboratories. The algorithm which was developed allows for an easy adjustment of input parameters that depend on available signals and for this reason could also be used with various hardware systems.

This Doctoral Dissertation, presented as a set of research contributions, focuses on resource efficiency in data centers. This topic has been faced mainly by the development of several energy-efficiency, resource managing and scheduling policies, as well as the simulation tools required to test them in realistic cloud computing environments. Several models have been implemented in order to minimize energy consumption in Cloud Computing environments. Among them: a) Fifteen probabilistic and deterministic energy-policies which shut-down idle machines; b) Five energy-aware scheduling algorithms, including several genetic algorithm models; c) A Stackelberg game-based strategy which models the concurrency between opposite requirements of Cloud-Computing systems in order to dynamically apply the most optimal scheduling algorithms and energy-efficiency policies depending on the environment; and d) A productive analysis on the resource efficiency of several realistic cloud–computing environments. A novel simulation tool called SCORE, able to simulate several data-center sizes, machine heterogeneity, security levels, workload composition and patterns, scheduling strategies and energy-efficiency strategies, was developed in order to test these strategies in large-scale cloud-computing clusters. As results, more than fifty Key Performance Indicators (KPI) show that more than 20% of energy consumption can be reduced in realistic high-utilization environments when proper policies are employed., Esta Tesis Doctoral, que se presenta como compendio de artículos de investigación, se centra en la eficiencia en la utilización de los recursos en centros de datos de internet. Este problema ha sido abordado esencialmente desarrollando diferentes estrategias de eficiencia energética, gestión y distribución de recursos, así como todas las herramientas de simulación y análisis necesarias para su validación en entornos realistas de Cloud Computing. Numerosas estrategias han sido desarrolladas para minimizar el consumo energético en entornos de Cloud Computing. Entre ellos: 1. Quince políticas de eficiencia energética, tanto probabilísticas como deterministas, que apagan máquinas en estado de espera siempre que sea posible; 2. Cinco algoritmos de distribución de tareas que tienen en cuenta el consumo energético, incluyendo varios modelos de algoritmos genéticos; 3. Una estrategia basada en la teoría de juegos de Stackelberg que modela la competición entre diferentes partes de los centros de datos que tienen objetivos encontrados. Este modelo aplica dinámicamente las estrategias de distribución de tareas y las políticas de eficiencia energética dependiendo de las características del entorno; y 4. Un análisis productivo sobre la eficiencia en la utilización de recursos en numerosos escenarios de Cloud Computing. Una nueva herramienta de simulación llamada SCORE se ha desarrollado para analizar las estrategias antes mencionadas en clústers de Cloud Computing de grandes dimensiones. Los resultados obtenidos muestran que se puede conseguir un ahorro de energía superior al 20% en entornos realistas de alta utilización si se emplean las estrategias de eficiencia energética adecuadas. SCORE es open source y puede simular diferentes centros de datos con, entre otros muchos, los siguientes parámetros: Tamaño del centro de datos; heterogeneidad de los servidores; tipo, composición y patrones de carga de trabajo, estrategias de distribución de tareas y políticas de eficiencia energ

Sleep quality and in general, behavior in bed can be detected using a sleep state analysis. These results can help a subject to regulate sleep and recognize different sleeping disorders. In this work, a sensor grid for pressure and movement detection supporting sleep phase analysis is proposed. In comparison to the leading standard measuring system, which is Polysomnography (PSG), the system proposed in this project is a non-invasive sleep monitoring device. For continuous analysis or home use, the PSG or wearable Actigraphy devices tends to be uncomfortable. Besides this fact, they are also very expensive. The system represented in this work classifies respiration and body movement with only one type of sensor and also in a non-invasive way. The sensor used is a pressure sensor. This sensor is low cost and can be used for commercial proposes. The system was tested by carrying out an experiment that recorded the sleep process of a subject. These recordings showed the potential for classification of breathing rate and body movements. Although previous researches show the use of pressure sensors in recognizing posture and breathing, they have been mostly used by positioning the sensors between the mattress and bedsheet. This project however, shows an innovative way to position the sensors under the mattress.

Stress and physical activities are important aspects of life of people. Body reactions on stress and on physical activities can be very similar but long-term stress leads to diseases and damages the body [1]. Currently there is no method to differentiate easily and clearly between these two aspects in a time slot. We have confronted this problem while developing a mobile system for detection and analysis of stress. This paper presents an approach, which uses a long-term monitor with ECG/EKG capabilities and analysis of the heart rate data that is extracted from the device. The focus of the work is to find characteristics that are useful for differentiation between physical activity and stress.

This editorial presents advances on Human-centred Ambient Intelligence applications which take into account cognitive issues when modelling users (i.e. stress, attention disorders), and learn users’ activities/preferences and adapt to them (i.e. at home, driving a car). These papers also show AmI applications in health and education, which make them even more valuable for the general society.

Background: In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. Methods: By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. Results: The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. Conclusions: This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.

An adaptive and rule-based driving system is being developed that tries to improve the driving behavior in terms of the energy-efficiency and safety by giving recommendations. Therefore, the driving system has to monitor the adherence of driving rules by matching the rules to the driving behavior. However, existing rule matching algorithms are not sufficient, as the data within a driving system is changing frequently. In this paper a rule matching algorithm is introduced that is able to handle frequently changing data within the context of the driving system. 15 journeys were used to evaluate the performance of the rule matching algorithms. The results showed that the introduced algorithm outperforms existing algorithms in the context of the driving system. Thus, the introduced algorithm is suited for matching frequently changing data against rules with a higher performance, why it will be used in the driving system for the detection of broken energy-efficiency or safety-relevant driving rules.

Saving energy and protecting the environment became fundamental for society and politics, why several laws were enacted to increase the energye ciency. Furthermore, the growing number of vehicles and drivers leaded to more accidents and fatalities on the roads, why road safety became an important factor as well. Due to the increasing importance of energye ciency and safety, car manufacturers started to optimise the vehicle in terms of energy-e ciency and safety. However, energy-e ciency and road safety can be also increased by adapting the driving behaviour to the given driving situation. This thesis presents a concept of an adaptive and rule based driving system that tries to educate the driver in energy-e cient and safe driving by showing recommendations on time. Unlike existing driving systems, the presented driving system considers energy-e ciency and safety relevant driving rules, the individual driving behaviour and the driver condition. This allows to avoid the distraction of the driver and to increase the acceptance of the driving system, while improving the driving behaviour in terms of energy-e ciency and safety. A prototype of the driving system was developed and evaluated. The evaluation was done on a driving simulator using 42 test drivers, who tested the e_ect of the driving system on the driving behaviour and the e_ect of the adaptiveness of the driving system on the user acceptance. It has been proven during the evaluation that the energy-e ciency and safety can be increased, when the driving system was used. Furthermore, it has been proven that the user acceptance of the driving system increases when the adaptive feature was turned on. A high user acceptance of the driving system allows a steady usage of the driving system and, thus, a steady improvement of the driving behaviour in terms of energy-e ciency and safety.

A methodology to quantify the dependence be tween features using the Ameva discretization algorithm and the advantages of qualitative models is presented in this paper. This approach will be applied over medical data sets. A com parison among Ameva and other related works has been done. The results, as will be depth explained in this paper, show that Ameva-based methodology can be used to determine the depen dence between features in a fast and understandable way from data sets with a high number of attributes and low number of instances. This is a quite important feature in genomic en vironments among others. This methodology has been applied to some well-known medical data sets and the results obtained shown that is a good alternative to other established algorithms in terms of clarity and computational cost.

Stress is recognized as a predominant disease with raising costs for rehabilitation and treatment. Currently there several different approaches that can be used for determining and calculating the stress levels. Usually the methods for determining stress are divided in two categories. The first category do not require any special equipment for measuring the stress. This category useless the variation in the behaviour patterns that occur while stress. The core disadvantage for the category is their limitation to specific use case. The second category uses laboratories instruments and biological sensors. This category allow to measure stress precisely and proficiently but on the same time they are not mobile and transportable and do not support real-time feedback. This work presents a mobile system that provides the calculation of stress. For achieving this, the of a mobile ECG sensor is analysed, processed and visualised over a mobile system like a smartphone. This work also explains the used stress measurement algorithm. The result of this work is a portable system that can be used with a mobile system like a smartphone as visual interface for reporting the current stress level.

An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the 2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy.

In this paper a new neuroscience technique is applied into Marketing, which is becoming commonly known as the field of Neuromarketing. The aim of this paper is to recognize how brain responds during the visualization of short advertising movies. Using low cost electroencephalography (EEG), brain regions used during the presentation have been studied. We may wonder about how useful it is to use neuroscience knowledge in marketing, what can neuroscience add to marketing, or why use this specific technique. By using discrete techniques over EEG frequency bands of a generated labeled dataset, C4.5 and ANN learning methods have been applied to obtain the score assigned to each ads by the user. This techniques allows to reach more than 82% of accuracy, which is an excellent result taking into account the kind of low-cost EEG sensors used.

El aumento de la población en áreas urbanas y el ritmo de vida cada vez más sedentario es una preocupación creciente. Por otra parte, los avances tecnológicos en sensores y redes de comunicaciones permiten obtener mucha información, que antes no era posible conocer, prácticamente en tiempo real. Este trabajo en progreso utiliza estos avances, para recopilar datos de los habitantes de una zona urbana en una plataforma web en que en un futuro, los profesionales puedan obtener datos anónimos, analizarlos y suministrar patrones de salud en base a los mismos, dotando al sistema de la capacidad de crear planes de acción comunes y personalizados a los perfiles de los ciudadanos, con el fin de mejorar su calidad de vida.