{"title":"In-vehicle driver recognition based on hand ECG signals","authors":"H. Silva, A. Lourenço, A. Fred","doi":"10.1145/2166966.2166971","DOIUrl":null,"url":null,"abstract":"We present a system for in-vehicle driver recognition based on biometric information extracted from electrocardiographic (ECG) signals collected at the hands. We recur to non-intrusive techniques, that are easy to integrate into components with which the driver naturally interacts with, such as the steering wheel. This system is applicable to the automatic customization of vehicle settings according to the perceived driver, being also prone to expand the security features of the vehicle through the detection of hands-off steering wheel events in a continuous or near-continuous manner. We have performed randomized tests for performance evaluation of the system, in a subject identification scenario, using closed sets of up to 5 subjects, showing promising results for the intended application.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IUI. International Conference on Intelligent User Interfaces","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2166966.2166971","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 38
Abstract
We present a system for in-vehicle driver recognition based on biometric information extracted from electrocardiographic (ECG) signals collected at the hands. We recur to non-intrusive techniques, that are easy to integrate into components with which the driver naturally interacts with, such as the steering wheel. This system is applicable to the automatic customization of vehicle settings according to the perceived driver, being also prone to expand the security features of the vehicle through the detection of hands-off steering wheel events in a continuous or near-continuous manner. We have performed randomized tests for performance evaluation of the system, in a subject identification scenario, using closed sets of up to 5 subjects, showing promising results for the intended application.