Space recognition properties of learning disabled (LD) children have been investigated by measuring finger trajectory when contradictive modalities are given. Elapsed time, trajectory length, mean speed and curvature ...
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ISBN:
(纸本)0780356756
Space recognition properties of learning disabled (LD) children have been investigated by measuring finger trajectory when contradictive modalities are given. Elapsed time, trajectory length, mean speed and curvature are calculated from trajectory data. Most LD children have difficulties in actions which need cooperation between different modalities.
The next generation of telecare systems will need to monitor and process vast amounts of personal data. These data might include both medical and lifestyle (or behavioral) information which may be highly sensitive. Th...
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ISBN:
(纸本)0780356756
The next generation of telecare systems will need to monitor and process vast amounts of personal data. These data might include both medical and lifestyle (or behavioral) information which may be highly sensitive. There is general unease with the concept of such datastreaming out of the home on a regular basis without the knowledge of the client. The CarerNet model is being developed with a distributed intelligence approach which can be used to manage and control the flow of personal data. By establishing a hierarchical layering of information zones based on intelligent nodes within the home, and by agreeing with the client a set of circumstances which warrant external intervention, it is possible to filter the flow of sensitive information between such zones. This reduces the intrusion while minimizing the quantity of data and the errors transmitted throughout the system.
It is of prime importance to ascertain the prognosis of patients who have experienced heart attack or unstable angina, as they are prone to developing serious adverse after-effects. The aim of this study was to run ma...
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ISBN:
(纸本)0780356756
It is of prime importance to ascertain the prognosis of patients who have experienced heart attack or unstable angina, as they are prone to developing serious adverse after-effects. The aim of this study was to run machine learning algorithms over a cardiac database to derive associations between various clinical and pathological parameters and the occurrence of future adverse consequences. The rules induced from the data were used to build an expert system for prediction of outcomes for unseen cases and it was ported on the web for use over the Internet.
This paper proposes a method for classifying the phonocardiogram (PCG) into three target classes (Type I: normal, Type II: innocent murmur, Type III: abnormal murmur). The method detects the presence of systolic murmu...
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ISBN:
(纸本)0780356756
This paper proposes a method for classifying the phonocardiogram (PCG) into three target classes (Type I: normal, Type II: innocent murmur, Type III: abnormal murmur). The method detects the presence of systolic murmur by the first self-organizing map with learning vector quantization algorithm. The second self-organizing map classifies the PCG into innocent and abnormal murmur. The effectiveness of the method was confirmed by applying the method to the data obtained from nation wide health screening for elementary school children conducted in Japan. The firststage of detecting the presence of systolic murmur achieves correcting rate of 99.4%, and correcting classification rates for innocent and abnormal murmur was 96.9% and 94.9% respectively. Furthermore, the proposed method not only has good performance of classifying the target classes, but also it could have ability to classify the systolic murmur into sub classes such as ejection murmur or musical murmur, etc. because of its structure of the system.
Traditionally, evaluations involving three-dimensional (3D) biomechanical analysis have produced prohibitive quantities of data which very few clinicians have been trained to interpret. What is needed is a clinician-a...
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ISBN:
(纸本)0780356756
Traditionally, evaluations involving three-dimensional (3D) biomechanical analysis have produced prohibitive quantities of data which very few clinicians have been trained to interpret. What is needed is a clinician-assistant system to aid clinicians in more effective synthesis of modeling tools with their clinical experience. The firststep in realizing this system - and the focus of this paper - is the development of a fuzzy EMG-to-muscle force estimator that captures dynamic muscle properties while providing robustness to partial data. The resulting force estimate is more accurate than simply smoothing EMG, and the robustness is an improvement over a dynamical nonlinear Hill muscle model.
Increasing numbers of older people are able to live independently in the community through the support of telecare systems. Those who are considered to be frail and at risk of sudden decline need 24 hour monitoring in...
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ISBN:
(纸本)0780356756
Increasing numbers of older people are able to live independently in the community through the support of telecare systems. Those who are considered to be frail and at risk of sudden decline need 24 hour monitoring in addition to automatic sensors to detect specific emergency conditions. MIDAS is a second generation telecare system designed to provide a person living alone with a high level of continuous monitoring and detection of need. It is based on a battery of non-intrusive activity sensors and environmental sensors which report to a local intelligence unit (LIU). The LIU processes the process the data in real-time in order to detect potential emergency situations using simple sequence and timing rules. It also builds up a lifestyle profile which enables social decline to be detected and predictions to be made of future problems such as falls or illness. The software may be altered to enable MIDAS to measure a cognitive function index or to provide an assessment of the activities of daily living. It may also provide security functions and a means of quality assuring the delivery of domiciliary care.
This article explores the use of single-channel EEG to predict the hand movement including grasping, opening, and holding. A feedforward neural network with the backpropagation learning rule was trained to discriminat...
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This article explores the use of single-channel EEG to predict the hand movement including grasping, opening, and holding. A feedforward neural network with the backpropagation learning rule was trained to discriminate between three different patterns of EEG using the mean absolute value (MAV), variance, and the relative power of the Beta band to the Alpha band as the features. It was found that 80% of the novel data and 98.7% of the trained data were classified correctly.
Presents the design principles for a graphical user interface for viewing mammograms interactively, in the context of self-paced, computer-aided instruction. Patient data, whole image and full resolution views, featur...
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Presents the design principles for a graphical user interface for viewing mammograms interactively, in the context of self-paced, computer-aided instruction. Patient data, whole image and full resolution views, feature highlighting, image processing and radiology and pathology reports, are all integrated into a self-contained package with intuitively understood graphical icons to permit rapid learning and comfortable use. The image database is searchable by patient, view, date, similarity of lesion appearance, and pathology so that different slices of the same data may be reviewed to consolidate knowledge and test understanding.
This paper presents Neural Adaptive Filters (NAF's) for estimating brainstem auditory evoked potential (BAEP). The method is evaluated by using simulation and human data. It was observed that a significant improve...
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This paper presents Neural Adaptive Filters (NAF's) for estimating brainstem auditory evoked potential (BAEP). The method is evaluated by using simulation and human data. It was observed that a significant improvement in waveform estimation, compared with the ensemble averaging and time varying adaptive filter (TVAF), can be achieved by the neural adaptive filters. In this work, a multilayer perceptron network (MLP) trained with the backpropagation learning rule and Radial Basis Function Network (RBFN) trained with stochastic gradient-based algorithm were employed for neural filter implementation. It was found that the RBFN give rises to improvements in BAEP estimation over the MLP.
We propose a novel neural computation paradigm inspired by Hebbian covariance adaptation, a preeminent model of learning and memory in the brain. We show that this computational algorithm affords stable self-tuning ad...
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We propose a novel neural computation paradigm inspired by Hebbian covariance adaptation, a preeminent model of learning and memory in the brain. We show that this computational algorithm affords stable self-tuning adaptive optimal control of general nonlinear dynamical systems with unknown disturbances. This biologically-inspired adaptive control paradigm offers a new approach to the modeling of physiological control systems as well as the design of intelligent systems with potential applications in robotics, chemical control and other biomedical and industrial control problems.
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