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Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a...
World Health Organization informed that traffic accidents potentially become the 5th leading cause of death if there is no effective way to restrict drunk driving. It is reported that 51 million people are injured or dead because of the traffic accidents every year. These traffic accidents lead to the expenditures of $500 billion dollars. Among these traffic accidents, drunk driving is one of the...
Worldwide, more than 50 million people are injured in each year because of traffic accidents and their expenditure costs 1% to 3% of the world's GDP. Drowsy driver is one of the leading causes of the traffic accidents. To reduce the accidents, drowsy driver detection (DDD) using penalized cross-correlation kernel (KPC) has been developed. The cross-correlation is to measure the similarity of ECG signals...
Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy,...
Wrist pulse signal is believed to contain critical information of the patients' health condition. This project aims to analyze the time series wrist pulse signals in order to distinguish patients suffering from various symptoms with healthy people. In this paper, the four inflammation symptoms tackled in this project are Appendicitis (A), Acute Appendicitis (AA), Pancreatitis (P) and Duodenal Bulb...
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