Alberto de Ramón-Fernández

Data Structures Modelling for Citizen Tracking Based Applications in Smart Cities

Internet of Things is a promising paradigm for designing context-aware systems. The information provided can be used for understanding the environment and providing intelligent services to the citizen. There are several studies on the information acquisition of Smart Cities. This work is focused on citizens’ traceability technologies and the data structures involved in representing and querying this information. A location and route data structure is proposed for handling data from different sources and devices, and meet the requirements of citizens’ flow management in Smart Cities.

Personalised Support System for Hypertensive Patients Based on Genetic Algorithms

Hypertension is a common and dangerous condition, which is the most important preventable cause of stroke and heart disease. Long-term conditions result in a reduced quality of life that can be improved through self-management and empowerment of patients using information technologies. Current support systems include self-management and empowerment in patients, but both features are not personalised in terms of patient preferences and decision-making. In this work an adaptive genetic algorithm is proposed for personalised support systems in hypertensive patients by including patient blood pressure data in the generational replacement step of evolutionary computing.

Business Process Management for the Crohn’s Disease Clinical Process

Crohn’s disease belongs to the group of inflammatory bowel diseases. The current process of disease management has significant weaknesses that cause high cost for health systems and a significant loss of quality of life for the patient. This paper shows a new approach to redesign process for the management of Crohn’s disease based on Business Process Management strategy. This approach seeks to improve the patient empowerment and self-management, reducing costs and obtaining constant and updated information throughout the process.

A Distributed Model for Stressors Monitoring Based on Environmental Smart Sensors

Nowadays, in many countries, stress is becoming a problem that increasingly affects the health of people. Suffering stress continuously can lead to serious behavioral disorders such as anxiety or depression. Every person, in his daily routine, can face many factors which can contribute to increase his stress level. This paper describes a flexible and distributed model to monitor environmental variables associated with stress, which provides adaptability to any environment in an agile way. This model was designed to transform stress environmental variables in value added information (key stress indicator) and to provide it to external systems, in both proactive and reactive mode. Thus, this value-added information will assist organizations and users in a personalized way helping in the detection and prevention of acute stress cases. Our proposed model is supported by an architecture that achieves the features above mentioned, in addition to interoperability, robustness, scalability, autonomy, efficient, low cost and consumption, and information availability in real time. Finally, a prototype of the system was implemented, allowing the validation of the proposal in different environments at the University of Alicante.

Proposal for Monitoring the Stress Through the Sensing of Environmental Variables in a Workplace

Occupational stress has become a problem that increasingly affects the health of workers. Suffering stress continuously can lead to more serious behavioural disorders such as anxiety or depression. Our work focuses in the proposal of a system which can monitor the stress thanks to subjective information from the workers and objective data from the environment. The information can be consulted by a specialist, and thus, assist the worker in a personalised way and help in the detection and prevention of acute stress cases.

A Support System for Cardiopulmonary Resuscitation Technique Using Wearable Devices

Cardiopulmonary resuscitation is an emergency procedure that can save the life of a person, which combines manual chest compressions on the sternum and insufflations. The depth of the compressions and the cadence at which they are performed are two key factors for the effectiveness of the technique. This study presents a novel support system for cardiopulmonary resuscitation, helping the rescuer to perform the compressions with a correct cadence. To do this, a wearable device adapts to the initial pace of the rescuer, leading him gradually to reach the appropriate cadence.

A Knowledge-Based Clinical Decision Support System for Monitoring Chronic Patients

Monitoring chronic patients represent one of the main challenges in healthcare and information technologies. Knowledge-based and decision support systems seek to facilitate the adherence of physicians to treatments and guidelines, while improving self-management and empowerment of patients. In this work, a knowledge-based clinical decision support system is proposed for monitoring chronic patients, whose knowledge is given beforehand by human experts in the domain application.

Architecture of a Monitoring System for Hipertensive Patients

Hypertension is a chronic disease affecting eight out of ten over 65 years. Current medical systems focus on the monitoring of the hypertensive patient and, in some cases, on the diagnosis, making the doctor-patient communication more direct. However, these systems have a rigid structure that does not adapt neither to the patient’s real needs nor to manage key aspects such as security, scalability, integration, flexibility, interoperability or standardisation data. Our proposal aims to create an integrative architecture that solves the different weaknesses that the current systems have.

Monitoring-Based Model for Personalizing the Clinical Process of Crohn’s Disease

Crohn’s disease is a chronic pathology belonging to the group of inflammatory bowel diseases. Patients suffering from Crohn’s disease must be supervised by a medical specialist for the rest of their lives; furthermore, each patient has its own characteristics and is affected by the disease in a different way, so health recommendations and treatments cannot be generalized and should be individualized for a specific patient. To achieve this personalization in a cost-effective way using technology, we propose a model based on different information flows: control, personalization, and monitoring. As a result of the model and to perform a functional validation, an architecture based on services and a prototype of the system has been defined. In this prototype, a set of different devices and technologies to monitor variables from patients and their environment has been integrated. Artificial intelligence algorithms are also included to reduce the workload related to the review and analysis of the information gathered. Due to the continuous and automated monitoring of the Crohn’s patient, this proposal can help in the personalization of the Crohn’s disease clinical process.