In recent years, driver’s drowsiness is one of the main causes of traffic accidents, which can result in severe physical injury and serious economic loss. Fatigue of the driver is an important factor in road accidents, and fatigue detection has a significant influence on traffic safety. This article describes a drowsiness detection approach based on the combination of various multi-sensors. The present study proposed a method to detect the driver’s drowsiness that combines features of electrocardiography (ECG) and environmental factors, such as vehicle temperature and humidity, to improve detection performance. The activity of the autonomic nervous system which can be measured in heart rate variability (HRV) signals obtained from surface ECG, indicates changes during stress, extreme fatigue, and episodes of drowsiness. The combination of the multi-sensors feature of drowsiness is significant factors in determining the driver’s fatigue state and can use this information to transportation drowsy driving control center if necessary.
- Sensors data fusion
- Driver drowsiness detection
- Environmental sensors
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This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funder by the Korea government (MSIT) (2017-0-00336, Platform Development of Multi-log based Multi-Modal Data Convergence Analysis and Situational Response.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2018R1D1A1B07047112).
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Kim, S., Park, H., Lee, YT., Yoon, Y. (2020). Detecting Driver Drowsiness Based Fusion Multi-sensors Method. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_79
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