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Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the key landmarks to construct the midsagittal reference plane. In this paper, we propose a learning-based approach to automatically detect the Dent-landmark in the 3D cone-beam computed...
In order to denote the abnormalities of the patients, we propose a novel approach to detect anomaly in biomedical monitoring using density ratio values as the Patient Status Index (PSI). The key idea of the proposed method is to define the ratio of training and testing data densities, where training dataset only consist of normal data and testing dataset consist of both normal and abnormal data, and...
Falling in the home is one of the major challenges to independent living among older adults. The associated costs, coupled with a rapidly growing elderly population, are placing a burden on healthcare systems worldwide that will swiftly become unbearable. To facilitate expeditious emergency care, we have developed an artificially intelligent camera-based system that automatically detects if a person...
This manuscript proposes a particle swarm-based feature extraction to monitors brain activity with the goal of identifying correlate cognitive states and intensity of a task. This in turn would allow us to develop a pattern recognition system that will classify such cognitive states and thus to redistribute the workload to other subjects. In this abstract, we present a recognition system that employ...
Epilepsy is one of the most common brain disorders in the world. The spontaneous seizure onset influences the daily life of epilepsy patients. The studies on feature extraction and feature classification from Electroencephalography(EEG) signal in seizure prediction methods have shown great improvement these years. However, the variation issue of EEG signal (being awake, being asleep, severity of epilepsy,...
A primary difficulty in physiological monitoring is detecting changes of health status for patients. In order to address this difficulty, we propose a new framework in patient-specific physiological monitoring by defining a density ratio using the training density and testing density to denote the changes of patient status, such as health, sub-health and abnormalities. We use a Least Square-based...
This paper presents a method to automatically recognize events and driving activities during the use of a powered wheelchair (PW). The method uses a support vector machine classifier, trained from sensor-based data from a datalogging platform installed on the PW. Data from a 3D accelerometer positioned on the back of the PW were collected in a laboratory space during PW driving tasks. 16-segmented...
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