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.
Survivability is a crucial property for those systems-such as critical infrastructures or military Command and Control Information Systems-that provide essential services, since the latter must be operational even when the system is compromised due to attack or faults. This article proposes a model-driven method and a tool-MASDES-to assess the survivability requirements of critical systems. The method exploits the use of (1) (mis)use case technique and UML profiling for the specification of the survivability requirements and (2) Petri nets and model checking techniques for the requirement assessment. A survivability assessment model is obtained from an improved specification of misuse cases, which encompasses essential services, threats and survivability strategies. The survivability assessment model is then converted into a Petri net model for verifying survivability properties through model checking. The MASDES tool has been developed within the Eclipse workbench and relies on Papyrus tool for UML. It consists of a set of plug-ins that enable (1) to create a survivability system view using UML and profiling techniques and (2) to verify survivability properties. In particular, the tool performs model transformations in two steps. First, a model-to-model transformation generates, from the survivability view, a Petri net model and properties to be checked in a tool-independent format. Second, model-to-text transformations produce the Petri net specifications for the model checkers. A military Command and Control Information Systems has been used as a case study to apply the method and to evaluate the MASDES tool, within an iterative-incremental software development process.
The early diagnosis of ischemic events may prevent irreversible damage to the heart muscle. Mobile IntroductionMyocardial ischemia is one of the diseases with highest incidence rate in the industrialised countries. Physiologically, is identified by an insufficient oxygenated blood supply compared to current myocardial demand. This event is reflected in a ECG signal as anomalous variations during the ventricular re-polarization. Although the ECG signal analysis is not the most accurate method that exists to detect the ischemic events, it is without question, the least invasive and costly one, and still maintain a high sensitivity level in the detection.Moreover, prolonged, severe or repeated ischemic episodes can lead to irreversible damages to the cardiac tissue. Therefore it comes up the importance of the early detection of such kind of episodes.The latest technological advances in the communication and mobile devices have led to significant progresses of the mobile computing area and give the possibility of new any time and anywhere telemonitoring solution. The combination of real time ischemia detection methods with mobile computing techniques may give the solution to the early detection of ischemic events through innovative telemonitoring systems.The aim of this paper is to present the structure and the validation results of a detection algorithm of transient ischemic events based on ECG signal analysis. The algorithm can be embedded in a mobile such as a PDA and can be executed in real time. Those requirements involve some rules that we had in mind during the development of the algorithm: 1. to be simple enough and with a low computation cost to fulfill the time restrictions of a real time execution in a device with limited computation capabilities and memory capacity (PDA); 2. to generate a minimal number of false alarms 3. to be highly sensitive to ischemic episodes and above all to the most dangerous ones. We start this paper by mentioning some related work, followed by a description of the transient ischemia detection algorithm. We finish presenting the performances of the algorithm and conclusions. Related work and materialsThe ischemic transient events are associated with variations (elevations or depressions) of the ST segment in the ECG signal. Nevertheless, similar variations may be also produced by diurnal changes, postural changes, changes in ventricular conduction which make difficult the distinction between ischemic and non-ischemic events. There are several distinct algorithms that address the automatically ischemic detection in the ECG signals applying different methods that consider time domain analysis, KL transform, neural network or fuzzy logic. Nevertheless only a few of them deal explicitly with non-ischemic events such as axis shift events (an interesting reference of the existing ischemia algorithms can be found in [1]). Moreover the majority of the existing algorithms are difficult to be adjusted for real time execution and up to our knowledge, only a few algorithms specia...
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