An experimental system was engineered and implemented in 100 copies inside a real banking environment comprising: dynamic handwritten signature verification, face recognition, bank client voice recognition and hand vein distribution verification. The main purpose of the presented research was to analyze questionnaire responses reflecting user opinions on: comfort, ergonomics, intuitiveness and other aspects of the biometric enrollment process. The analytical studies and experimental work conducted in the course of this work will lead towards methodologies and solutions of the multimodal biometric technology, which is planned for further development. Before this stage is achieved a study on the data usefulness acquired from a variety of biometric sensors and from survey questionnaires filled in by banking tellers and clients was done. The decision-related sets were approximated by the Rough Set method offering efficient algorithms and tools for finding hidden patterns in data. Prediction of evaluated biometric data quality, based on enrollment samples and on user subjective opinions was made employing the developed method. After an introduction to the principles of applied biometric identity verification methods, the knowledge modelling approach is presented together with achieved results and conclusions.
One of the first clinical signs differentiating the minimally conscious state from the vegetative state is the presence of smooth pursuit eye movements occurring in direct response to moving salient stimuli. Glasgow Coma Scale (GCS) is one of the most commonly used diagnostic tools for acute phase assessment of the level of consciousness, together with a neurological examination. These classic measures are limited to qualitative neurological examination without more quantitative measures provided from e.g., tasks with tracking position of the gaze. Among this and other limitations, it is prone to a relatively high rate of misdiagnosis. Here, we developed an interface for gaze tracking to enhance the assessment of consciousness in 10 patients with acquired brain injuries. According to the acute phase GCS assessment, nine of them were considered unaware and below the minimally conscious state. Chronic neurological examination confirmed six of them below the minimally conscious state. Our new Human Computer Interface (HCI) revealed that six patients were conscious enough to complete at least one of the gaze tracking tasks. Among these six patients, one was originally diagnosed as remaining in a vegetative state and one in coma. The patient diagnosed as remaining in a chronic vegetative state scored six GCS points acutely. Following assessment with our HCI the patient was re-diagnosed with a possible locked-in syndrome. Our HCI method provides a new complementary tool for clinical assessment of patients suffering from disorders of consciousness.
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