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WAKE: a behind-the-ear wearable system for microsleep detection

Pham, Nhat, Dinh, Tuan, Raghebi, Zohreh, Kim, Taeho, Bui, Nam, Nguyen, Phuc, Truong, Hoang, Banaei-Kashani, Farnoush, Halbower, Ann, Dinh, Thang and Vu, Tam 2020. WAKE: a behind-the-ear wearable system for microsleep detection. Presented at: MobiSys '20: 18th Annual International Conference on Mobile Systems, Applications, and Services, Toronto, Ontario, Canada, 15-19 June 2020. MobiSys '20: Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services. Association for Computing Machinery, pp. 404-418. 10.1145/3386901.3389032

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Microsleep, caused by sleep deprivation, sleep apnea, and narcolepsy, costs the U.S.'s economy more than $411 billion/year because of work performance reduction, injuries, and traffic accidents. Mitigating microsleep's consequences require an unobtrusive, reliable, and socially acceptable microsleep detection solution throughout the day, every day. Unfortunately, existing solutions do not meet these requirements. In this paper, we propose a novel behind-the-ear wearable device for microsleep detection, called WAKE. WAKE detects microsleep by monitoring biosignals from the brain, eye movements, facial muscle contractions, and sweat gland activities from behind the user's ears. In particular, we introduce a Three-fold Cascaded Amplifying (3CA) technique to tame the motion artifacts and environmental noises for capturing high fidelity signals. The behind-the-ear form factor is motivated by the fact that bone-conductance headphones, which are worn around the ear, are becoming widely used. This technology trend gives us an opportunity to enable a wide range of cognitive monitoring and improvement applications by integrating more sensing and actuating functionality into the ear-phone, making it a smarter one. Through our prototyping, we show that WAKE can suppress motion and environmental noise in real-time by 9.74-19.47 dB while walking, driving, or staying in different environments ensuring that the biosignals are captured reliably. We evaluated WAKE against gold-standard devices on 19 sleep-deprived and narcoleptic subjects. The Leave-One-Subject-Out Cross-Validation results show the feasibility of WAKE in microsleep detection on an unseen subject with average precision and recall of 76% and 85%, respectively.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: Association for Computing Machinery
ISBN: 978-1-4503-7954-0
Date of First Compliant Deposit: 14 August 2023
Date of Acceptance: 15 June 2020
Last Modified: 04 Sep 2023 16:15

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