Simple VR for better living

Physical and cognitive rehabilitation based on natural interaction and VR has been on our horizon for several years, and we have been conducting experimentation towards that goal through several exploratory research initiatives.This article addresses some aspects of the state-of-the-art of VR in healthcare and well-being, with opportunities in the domain of rehabilitation based on natural interaction and VR being analyzed and put in perspective with the SmartAL ecosystem roadmap.info:eu-repo/semantics/publishedVersio

Sleeping activity recognition for an intelligent tele-monitoring system

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Petia I. RadevaPeople that need assistance, as for instance elderly or disabled people, may be affected by a decline in daily functioning that usually involves the reduction and discontinuity in daily routines, as well as, a worsening in the overall quality of life. Thus, there is the need to intelligent systems able to monitor indoor activities of users to detect emergencies, recognise activities, send notifications, and provide a summary of all the relevant information. In this TFG, a machine learning system is presented, it is aimed at improving the ruled-based system accuracy in detecting whether the user is performing their sleeping activity or not. It has been integrated in a sensor-based tele-monitoring and home support system. The data used to build and evaluate the system was obtained from a real-world environment with real end-users, thus ensuring the data reflect the complexities of the real-world

Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

Ontology-based personalized system to support patients at home

Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014Chronic diseases are incurable diseases that require long term supervision and treatments by medical professionals. The most common chronic diseases are cardiovascular disease, obesity, diabetes respiratory diseases and cancer. With information and communication technology many applications have been implemented to facilitate different clinical decision making process. With new technology, personalized healthcare systems are in place to enable patients with chronic diseases to acquire continuous and long-term medical services at home. This improves healthcare delivery since medical services can be accessed at any place. Today high prevalence of chronic diseases poses technological challenges to existing personalized healthcare systems including data integration and personalized recommendation plan. This research investigates how semantic technologies could be used to address the above challenges. The goal of this thesis is to use semantic technology for building ontology knowledge repository to provide data integration and medical recommendations for home based diabetes management systems. This ontology focuses on organizing knowledge related to vital sign measurement, questionnaire and recommendations for diabetic patients. To enter and link concepts and data for diabetes ontology, we used Protégé-owl. The ontology model provides knowledge into which information on individual patient including vital-sign data, questionnaires based information and recommendation are derived. Based on ontology’s structure, the model can collect, store and share information from heterogeneous sources, Reason over knowledge. Furthermore, ontology has been proven to be a better way of describing managed data. Therefore ontology based technology could be implemented in the personalized systems to enhance remote care for home-patient. Keywords

Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

Evaluating complex interventions using routinely collected data: Methods to improve the validity of randomised controlled trials and observational studies

This thesis addresses the evaluation of complex interventions using routinely collected data, specifically the internal validity of observational studies and the generalisability of Randomised Controlled Trials (RCTs). Following a literature review, this thesis has four main objectives: to estimate the effect of telephone health coaching on hospital utilisation in an observational study; to assess optimal choices of control area in observational studies; to estimate the effect of telehealth within a large RCT; and to develop methods to assess aspects of the generalisability of RCTs empirically. The first paper compares health-coached patients with matched controls. Controls were selected from areas of England that were first matched to the characteristics of the intervention area. Health coaching did not reduce hospital admissions in this study. A second paper uses simulations to assess the relative bias and statistical precision in the treatment effects estimated under alternative approaches to selecting control areas. Lower bias is reported when using local controls than when selecting controls from matched areas, except when there is little unexplained area-level variation in outcomes, when the opposite is true. The third paper reports that, in the RCT, telehealth patients had fewer hospital admissions than controls, but admissions increased unexpectedly among controls after recruitment, leading to concerns about generalisability. Placebo tests find that control patients in the RCT experienced more admissions than matched non-participants receiving usual care. To address the concern that the control group did not receive ‘usual care’, sensitivity analyses are presented that contrast outcomes between the telehealth patients in the RCT and matched non-participants. In this comparison, telehealth is associated with a trend towards more admissions than usual care. The thesis concludes that careful control matching and placebo tests can address important aspects of the validity of observational studies and RCTs, but that further development of evaluation methods is warranted