Detection of microsleep events with a behind-the-ear wearable system

Pham, Nhat, Dinh, Tuan, Kim, Taeho, Raghebi, Zohreh, Bui, Nam, Truong, Hoang, Nguyen, Tuan, Banaei-Kashani, Farnoush, Halbower, Ann, Dinh, Thang, Nguyen, Phuc and Vu, Tam 2023. Detection of microsleep events with a behind-the-ear wearable system. IEEE Transactions on Mobile Computing 22 (2) , pp. 841-857. 10.1109/TMC.2021.3090829

Preview

PDF - Accepted Post-Print Version Download (11MB) | Preview

Abstract

Every year, the U.S. economy loses more than $ 411 billion because of work performance reduction, injuries, and traffic accidents caused by microsleep. To mitigate microsleep's consequences, an unobtrusive, reliable, and socially acceptable microsleep detection solution throughout the day, every day is required. Unfortunately, existing solutions do not meet these requirements. In this paper, we propose WAKE, a novel behind-the-ear wearable device for microsleep detection. By monitoring biosignals from the brain, eye movements, facial muscle contractions, and sweat gland activities from behind the user's ears, WAKE can detect microsleep with a high temporal resolution. We introduce a Three-fold Cascaded Amplifying (3CA) technique to tame the motion artifacts and environmental noises for capturing high fidelity signals. 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 using 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 percent, respectively.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1536-1233
Date of First Compliant Deposit:	14 August 2023
Date of Acceptance:	1 June 2021
Last Modified:	13 Dec 2023 17:05
URI:	https://orca.cardiff.ac.uk/id/eprint/161752

Actions (repository staff only)

Edit Item