The new advances in sensor technology, personal digital assistants (PDAs), and wireless communications favor the development of a new type of monitoring system that can provide patients with assistance anywhere and at any time. Of particular interest are the monitoring systems designed for people that suffer from heart arrhythmias, due to the increasing number of people with cardiovascular diseases. PDAs can play a very important role in these kinds of systems because they are portable devices that can execute more and more complex tasks. The main questions answered in this paper are whether PDAs can perform a complete electrocardiogram beat and rhythm classifier, if the classifier has a good accuracy, and if they can do it in real time. In order to answer these questions, in this paper, we show the steps that we have followed to build the algorithm that classifies beats and rhythms, and the obtained results, which show a competitive accuracy. Moreover, we also show the feasibility of incorporating the built algorithm into the PDA.
Patients suspected of suffering sleep apnea and hypopnea syndrome (SAHS) have to undergo sleep studies such as expensive polysomnographies to be diagnosed. Healthcare professionals are constantly looking for ways to improve the ease of diagnosis and comfort for this kind of patients as well as reducing both the number of sleep studies they need to undergo and the waiting times. Relating to this scenario, some research proposals and commercial products are appearing, but all of them record the physiological data of patients to portable devices and, in the morning, these data are loaded into hospital computers where physicians analyze them by making use of specialized software. In this paper, we present an alternative proposal that promotes not only a transmission of physiological data but also a real-time analysis of these data locally at a mobile device. For that, we have built a classifier that provides an accuracy of 93% and a receiver operating characteristic-area under the curve (ROC-AUC) of 98.5% on SpO(2) signals available in the annotated Apnea-ECG Database. This local analysis allows the detection of anomalous situations as soon as they are generated. The classifier has been implemented taking into consideration the restricted resources of mobile devices.
Telerehabilitation systems that support physical therapy sessions anywhere can help save healthcare costs while also improving the quality of life of the users that need rehabilitation. The main contribution of this paper is to present, as a whole, all the features supported by the innovative Kinect-based Telerehabilitation System (KiReS). In addition to the functionalities provided by current systems, it handles two new ones that could be incorporated into them, in order to give a step forward towards a new generation of telerehabilitation systems. The knowledge extraction functionality handles knowledge about the physical therapy record of patients and treatment protocols described in an ontology, named TrhOnt, to select the adequate exercises for the rehabilitation of patients. The teleimmersion functionality provides a convenient, effective and user-friendly experience when performing the telerehabilitation, through a two-way real-time multimedia communication. The ontology contains about 2300 classes and 100 properties, and the system allows a reliable transmission of Kinect video depth, audio and skeleton data, being able to adapt to various network conditions. Moreover, the system has been tested with patients who suffered from shoulder disorders or total hip replacement.
Background/Objective: The objective of this ex post facto study is to analyze both the direct relationships between perceived social support, self-concept, resilience, subjective well-being and school engagement. Method: To achieve this, a battery of instruments was applied to 1,250 Compulsory Secondary Education students from the Basque Country (49% boys and 51% girls), aged between 12 and 15 years (M = 13.72, SD =1.09), randomly selected. We used a structural equation model to analyze the effects of perceived social support, self-concept and resilience on subjective well-being and school engagement. Results: The results provide evidence for the influence of the support of family, peer support and teacher support on self-concept. In addition, self-concept is shown as a mediating variable associated with resilience, subjective well-being and school engagement. Conclusions: We discuss the results in the context of positive psychology and their practical implications in the school context.
BackgroundParkinson’s Disease (PD) is a chronic neurodegenerative disease associated with motor problems such as gait impairment. Different systems based on 3D cameras, accelerometers or gyroscopes have been used in related works in order to study gait disturbances in PD. Kinect Ⓡ has also been used to build these kinds of systems, but contradictory results have been reported: some works conclude that Kinect does not provide an accurate method of measuring gait kinematics variables, but others, on the contrary, report good accuracy results.MethodsIn this work, we have built a Kinect-based system that can distinguish between different PD stages, and have performed a clinical study with 30 patients suffering from PD belonging to three groups: early PD patients without axial impairment, more evolved PD patients with higher gait impairment but without Freezing of Gait (FoG), and patients with advanced PD and FoG. Those patients were recorded by two Kinect devices when they were walking in a hospital corridor. The datasets obtained from the Kinect were preprocessed, 115 features identified, some methods were applied to select the relevant features (correlation based feature selection, information gain, and consistency subset evaluation), and different classification methods (decision trees, Bayesian networks, neural networks and K-nearest neighbours classifiers) were evaluated with the goal of finding the most accurate method for PD stage classification.ResultsThe classifier that provided the best results is a particular case of a Bayesian Network classifier (similar to a Naïve Bayesian classifier) built from a set of 7 relevant features selected by the correlation-based on feature selection method. The accuracy obtained for that classifier using 10-fold cross validation is 93.40%. The relevant features are related to left shin angles, left humerus angles, frontal and lateral bents, left forearm angles and the number of steps during spin.ConclusionsIn this paper, it is shown that using Kinect is adequate to build a inexpensive and comfortable system that classifies PD into three different stages related to FoG. Compared to the results of previous works, the obtained accuracy (93.40%) can be considered high. The relevant features for the classifier are: a) movement and position of the left arm, b) trunk position for slightly displaced walking sequences, and c) left shin angle, for straight walking sequences. However, we have obtained a better accuracy (96.23%) for a classifier that only uses features extracted from slightly displaced walking steps and spin walking steps. Finally, the obtained set of relevant features may lead to new rehabilitation therapies for PD patients with gait problems.
The goal of this paper is to show the main fe atures of KiReS, a telerehabilitation system based on Kinect for Windows, that offers, for both, users and physiotherapists some specific elements that make it more fr iendly to them. From the point of view of users, they can see in two 3D avatars how an exercise must be executed and how they execute it respectively. This fe ature can help them improve exercises performance. Moreover during the rehabilitation session they will always see an informative list that shows the exercises to be done in the session. From the point of view of physiotherapists the system allows them on the one hand, to define customized rehabilitation therapies. That can be done by defining different exercises that combine pre-defined movements. Moreover, they can add tests oriented to specific illnesses so that users themselves evaluate their physical state. On the other hand, they can create new exercises just performing those exercises in fr ont of the system and recording them. Those fe atures, not fu lly supported by already existing telerehabilitation systems, provide an added value that is well valued by both groups. Moreover, a prototype of KiReS is in operation, and allowed us to test its suitability fr om the point of view of real time performance as well as fr om the point of view of usability.
The evolving telecommunications industry combined with medical information technology has been proposed as a solution to reduce health care cost and provide remote medical services. This paper aims to validate and show the feasibility and user acceptance of using a telerehabilitation system called Kinect Rehabilitation System (KiReS) in a real scenario, with patients attending repeated rehabilitation sessions after they had a Total Hip Replacement (THR). We present the main features of KiReS, how it was set up in the considered scenario and the experimental results obtained in relation to two different perspectives: patients' subjective perceptions (gathered through questionnaires) and the accuracy of the performed exercises (by analysing the data captured using KiReS). We made a full deployment of KiReS, defining step by step all the elements of a therapy: postures, movements, exercises and the therapy itself. Seven patients participated in this trial in a total of 19 sessions, and the system recorded 3865 exercise executions. The group showed general support for telerehabilitation and the possibilities that systems such as KiReS bring to physiotherapy treatment.
Los hábitos de vida saludables guardan relación con el autoconcepto físico y también con el bienestar psicológico, al menos durante los años de la adolescencia. Esto es lo que confirman los datos que se presentan en este estudio, cuya muestra la componen 539 estudiantes de entre 12 y 23 años. Los participantes cumplimentaron el Cuestionario de Autoconcepto Físico (CAF), un cuestionario sobre conductas saludables de los escolares y el Cuestionario de Bienestar Psicológico (EBP). Los resultados sugieren diversos flancos por donde orientar una intervención psicosocial que ayude a promover el desarrollo personal y la convivencia social, desde una triple conexión: el estilo de vida, el bienestar psicológico y el autoconcepto físico.
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