Using Kinect to classify Parkinson's disease stages related to severity of gait impairment.

BACKGROUND: Parkinson'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. METHODS: In 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. RESULTS: The 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. CONCLUSIONS: In 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.

Web access to data in a mobile ECG monitoring system.

Cardiovascular diseases and, in particular, diseases related to arrhythmias are a problem that affects a significant percentage of the population, being one of the major causes of death in Europe. New advances in the fields of PDAs, mobile phones, wireless communications and vital parameter sensors have permitted the development of revolutionary medical monitoring systems, which strikingly improve the lifestyle of patients. However, not all those monitoring systems provide patients with real assistance - anywhere and at any time. We have developed a system that goes a step further than the previous approaches, being designed to capture, record and, as a distinctive feature, locally analyze the ECG signals in a PDA carried by the patient. In that sense, the system has a decision support module based on decision tree methods that can detect, with high precision, any arrhythmias that the user may be suffering. Alarms can then be activated in time to alert a medical center in order to provide the proper medical assistance. One of our aims when building the system has been to optimize limited and expensive resources like PDA memory size and wireless communication costs. Moreover, accessibility is also an important feature of the system that has been achieved by the development of web services to query the data computed in the PDA. In this way, authorized personnel (physicians and relatives) can easily obtain access to that data.

Architecture, cost-model and customization of real-time monitoring systems based on mobile biological sensor data-streams.

Innovation in the fields of wireless data communications, mobile devices and biosensor technology enables the development of new types of monitoring systems that provide people with assistance anywhere and at any time. In this paper we present an architecture useful to build those kind of systems that monitor data streams generated by biological sensors attached to mobile users. We pay special attention to three aspects related to the system efficiency: selection of the optimal granularity, that is, the selection of the size of the input data stream package that has to be acquired in order to start a new processing cycle; the possible use of compression techniques to store and send the acquired input data stream and; finally, the performance of a local analysis versus a remote one. Moreover, we introduce two particular real systems to illustrate the suitability and applicability of our proposal: an anywhere and at any time monitoring system of heart arrhythmias and an apnea monitoring system.