Nuevas metodologías para el reconocimiento de cambios posturales a través de sensores

Con el fin de posibilitar nuevas alternativas que permitan mitigar la complicación de las úlceras por presión, en este trabajo se presentan los resultados de investigación de la tesis doctoral, que han permitido implementar dos metodologías de reconocimiento de cambios posturales de monitoreo en tiempo real, con dispositivos vestibles inerciales no invasivos para la detección y cálculo de postura, usando técnicas de inteligencia artificial. La primera metodología está basada en un registro histórico de la actividad corporal, dataset, y por el reconocimiento de posturas en tiempo real con técnicas de Inteligencia Artificial. Por su parte, la segunda metodología comprende el uso de dispositivos vestibles inerciales en zonas no invasivas, encargados de registrar el tiempo en que la persona ha permanecido en la misma posición, la recolección de datos de personas reales en diferentes posturas, la estimación de las posturas en tiempo real se realiza mediante técnicas de inteligencia artificial.To enable new alternatives to mitigate the complication of pressure ulcers, this work presents the research results of the doctoral thesis, which have allowed the implementation of two real-time monitoring methodologies, with devices non-invasive inertial wearables for posture detection and calculation and using artificial intelligence techniques. The first methodology is based on a historical record of body activity, a dataset, and the recognition of postures in real-time with Artificial Intelligence techniques. On other hand, the second methodology includes the use of inertial wearable devices in non-invasive areas, recording the time the person has remained in the same position, the collection of data from real people in key ulcer prevention positions, the estimation of postures in real-time using artificial intelligence techniques.Tesis Univ. Jaén. Departamento de Informática. Leída el 19/11/2021

Architecture, logique floue, classification et clustering pour l’exploration de données réelles issues de multiples maisons intelligentes

Selon l’Organisation des Nations Unies (2019), il est estimé que le nombre de personnes âgées de 65 ans et plus dans le monde devrait doubler d’ici 2050 pour atteindre un total de 1,5 milliard d’individus. Cette évolution impacte le risque de déclarer une maladie neurodégénérative qui, selon le rapport de l’Alzheimer’s Disease International (2015), augmenterait exponentiellement avec l’âge. Or, ces maladies provoquent une perte progressive d’autonomie ce qui induit des besoins en ressources matérielles et humaines. En parallèle de ces enjeux, des progrès allient technologie et santé, comme le dépistage automatique du cancer du sein, présenté dans le travail de Karabatak (2015). Avec l’amélioration continue des environnements intelligents et du matériel, notamment suite à l’apparition de technologies comme l’Arduino (2005), la Rasbperry Pi (2012) ou la Latte Panda (2016), on est en droit d’imaginer les applications possibles de la maison intelligente aux problématiques posées par l’accroissement des maladies dégénératives. C’est ce que nous proposons dans cette thèse, où nous faisons le point sur les environnements intelligents et la reconnaissance d’activités. Point duquel nous dérivons deux propositions : d’une part, une architecture basée sur la gestion d’un flux d’événements descriptifs par des compositions d’agents autonomes permettant le support de plus de 30 environnements hétérogènes. D’autre part, une approche basée sur la logique floue permettant de conduire un processus de reconnaissance d’activités malgré la grande diversité de nos jeux de données. Ces deux solutions participant à l’élaboration d’un outil permettant aux cliniciens de suivre à distance, l’évolution du comportement de patients atteints de maladies dégénératives. According to the United Nations (2019), the world population aged 65 years and more will double up to 1.5 billion individuals before 2050. This trend will impact the growth of neurodegenerative disorders that are subject to an exponential risk of appearance with aging, as reported by Alzheimer’s Disease International (2015). As this kind of disease induces a decrease in the autonomy of the elderly, this evolution will heavily increase the need for human and material resources around the world. In parallel, various research works combine technology and healthcare, like for the automatic breast cancer detection described in the article of Karabatak (2015). This tendency, in conjunction with hardware and intelligent environments improvement, notably with the Arduino (2005), the Rasbperry Pi (2012), and the Latte Panda (2016), affords us to imagine how smart-homes could solve the implications of the aforementioned growth of degenerative diseases. To investigate this question, this thesis derivates two proposals from a careful study of intelligent environments and activity recognition methods. The first is an architecture that supports more than 30 heterogeneous environments and that works by assembling autonomous agents for processing a flux of descriptive events. Finally, the second is a model built upon fuzzy logic that enables us to recognize activities despite the inherent diversity of our datasets. These two solutions answer some aspects of the process of making a tool that allows clinicians to monitor people with degenerative diseases from their homes

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area

Multi-Event Naive Bayes Classifier for Activity Recognition in the UCAmI Cup

This short paper presents the activity recognition results obtained from the CAR-CSIC team for the UCAmI’18 Cup. We propose a multi-event naive Bayes classifier for estimating 24 different activities in real-time. We use all the sensorial information provided for the competition, i.e., binary sensors fixed to everyday objects, proximity BLE-based tags, location-aware smart floor sensing and the wrist’s acceleration. The results using training data-sets of 7 days show accuracies (true positives) about 68%; however for the three extra data-sets of the competition we were able to reach a 60.5% accuracy

Multi-Event Naive Bayes Classifier for Activity Recognition in the UCAmI Cup

This short paper presents the activity recognition results obtained from the CAR-CSIC team for the UCAmI’18 Cup. We propose a multi-event naive Bayes classifier for estimating 24 different activities in real-time. We use all the sensorial information provided for the competition, i.e., binary sensors fixed to everyday objects, proximity BLE-based tags, location-aware smart floor sensing and the wrist’s acceleration. The results using training data-sets of 7 days show accuracies (true positives) about 68%; however for the three extra data-sets of the competition we were able to reach a 60.5% accuracy

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida