<\/div>\n
Abstract:<\/h2>\nBackground:<\/h3>\n
Impaired driving is one of the main causes of motor-vehicle accidents which occur primarily due to physical and mental fatigue, alcohol or drug consumption. It is almost impossible for drivers to recognize the symptoms of drowsiness in its later stages. However, it can be prevented easily through early-stage recognition if the driver is notified or stopped. Several different techniques have been developed for precise detection of drowsiness and impairment. This abstract reviews various aspects and how each method should be improved for better performance and efficiency.<\/p>\n
Aims:<\/h3>\n
The main aim of this review is to evaluate the existing methods and algorithms for detecting the symptoms of drowsiness\/impairment. The ultimate goal is to propose the suitable replacement for detecting driver drowsiness in the early stage and create alarming system before the drivers get drowsy\/impaired.<\/p>\n
Methods:<\/h3>\n
The performance of different methods are compared and their pros and cons regarding the mitigation of the risk of motor-vehicle accidents is investigated. Then, the recommended and possible solutions to improve the current drowsiness detection methods are proposed. Various items such as detecting feature, test environment, and analysis methods are compared in this review and with future goal of this study.<\/p>\n
Results:<\/h3>\n
The main techniques currently used in evaluating drowsiness\/impairment are categorized as non-intrusive and intrusive. The first category includes physical and vehiclular feature-based methods. Their performance have been tested and verified in both lab and field environments generated satisfactory results. However, their main drawback is that they are only able to identify impaired driving and produce an alarm signal after the person has already entered into the state of drowsiness or unconsciousness. The second category includes physiological feature-based methods. The results of these methods are mostly based on the studies conducted in the lab as well.<\/p>\n
Discussion:<\/h3>\n
In order to detect driver drowsiness effectively and accurately, physiological features are needed to be monitored in a long term within the real environment. Thus far, there is no reliable field studies to support the idea of long term continuous remote monitoring of drivers using biometric sensors in the real-life environment and to identify impaired drivers and inform them as early as possible. Therefore, our future study is focusing on a new approach for early stage recognition of drowsiness using continuous\/real time biometric data. Reviewing the previous data and presenting our methodology and the pilot study at the time of the conference will allow us to receive feedback and to move forward in the right direction.<\/p>\n
Conclusions:<\/h3>\n
In this study, the accuracy and efficiency of the existing driver drowsiness detection methods were investigated and compared.
\nDifferent physiological parameters such as ECG, EEG, EMG, and EOG are used for identifying drowsy driver in the early stage. The results showed that ECG leads to more accurate and reliable results among all the detection features for detecting drowsiness\/impairment. Using the previous methodologies, we are proposing an approach for real time and continuous data collection in the field using the ECG signals.<\/p>\n
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Abolhasannejad, Abolhasannejad<\/p>\n","protected":false},"author":163,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_post_transparent":"default","_kad_post_title":"default","_kad_post_layout":"default","_kad_post_sidebar_id":"","_kad_post_content_style":"default","_kad_post_vertical_padding":"default","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[344,346],"tags":[386,368],"class_list":["post-17440","post","type-post","status-publish","format-standard","hentry","category-carsp-acpser-pri-virtual-conference-virtuelle-2021","category-research-and-evaluation","tag-fitness-to-drive","tag-risky-driving"],"acf":[],"_links":{"self":[{"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/posts\/17440","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/users\/163"}],"replies":[{"embeddable":true,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/comments?post=17440"}],"version-history":[{"count":5,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/posts\/17440\/revisions"}],"predecessor-version":[{"id":21117,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/posts\/17440\/revisions\/21117"}],"wp:attachment":[{"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/media?parent=17440"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/categories?post=17440"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/tags?post=17440"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}