Hu, J., Jiang, H., Liu, D., Xiao, Z., Dustdar, S., Liu, J., Min, G., & Morichetta, A. (2022). BlinkRadar: Non-Intrusive Driver Eye-Blink Detection with UWB Radar. In Proceedings of the IEEE 42nd International Conference on Distributed Computing Systems (ICDCS 2022) (pp. 1040–1050). IEEE. https://doi.org/10.1109/ICDCS54860.2022.00104
42nd IEEE International Conference on Distributed Computing Systems (ICDCS 2022)
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Event date:
10-Jul-2022 - 13-Jul-2022
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Event place:
Bologna, Italy
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Number of Pages:
11
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Publisher:
IEEE
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Keywords:
Drowsy driving detection; Eye Blink detection; RFID Signal
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Abstract:
The eye-blink pattern is crucial for drowsy driving diagnostics, which has become an increasingly serious social issue. However, traditional methods (e.g., with EOG, camera, wearable, and acoustic sensors) are less applicable to real-life scenarios due to the disharmony between user-friendliness, monitoring accuracy, and privacy-preserving. In this work, we design and implement BlinkRadar as a low-cost and contact-free system to conduct fine-grained eye-blink monitoring in a driving situation using a customized impulse-radio ultra-wideband (IR-UWB) radar which has superior spatial resolution with the ultra-wide bandwidth. BlinkRadar leverages an IR-UWB radar to achieve contact-free sensing, and it fully exploits the complex radar signal for data augmentation. BlinkRadar aims to single out the eye-blink induced waveforms modulated by body movements and vehicle status. It solves the serious interference caused by the unique characteristics of blinking (i.e., subtle, sparse, and non-periodic) and from the human target itself and surrounding objects. We evaluate BlinkRadar in a laboratory environment and during actual road testing. Experimental results show that BlinkRadar can achieve a robust performance of drowsy driving with a median detection accuracy of 92.2% and eye blink detection of 95.5%.
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Project (external):
NSFC NSFC NSFC Hunan Provincial Natural Science Foundation Hunan Provincial Natural Science Foundation