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

Published: 15 June 2020 Publication History

Abstract

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.

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MobiSys '20: Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services
June 2020
496 pages
ISBN:9781450379540
DOI:10.1145/3386901
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Author Tags

  1. behind-the-ear sensing
  2. cyber-physical systems
  3. drowsiness monitoring
  4. fatigue supervising
  5. microsleep detection
  6. wearable devices

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  • (2024)Stress-GPT: Stress detection with an EEG-based foundation modelProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3698121(2341-2346)Online publication date: 4-Dec-2024
  • (2024)Exploring the Feasibility of Remote Cardiac Auscultation Using EarphonesProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649366(357-372)Online publication date: 29-May-2024
  • (2024)EarSSR: Silent Speech Recognition via EarphonesIEEE Transactions on Mobile Computing10.1109/TMC.2024.335671923:8(8493-8507)Online publication date: Aug-2024
  • (2024)Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUsIEEE Transactions on Biomedical Circuits and Systems10.1109/TBCAS.2024.335750918:3(608-621)Online publication date: Jun-2024
  • (2024)PA2BLO: Low-Power, Personalized Audio Badge2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494427(154-163)Online publication date: 11-Mar-2024
  • (2024)A 15.4-ENOB, Fourth-Order Truncation-Error-Shaping NS-SAR-Nested ΔΣ Modulator With Boosted Input Impedance and Range for Biosignal AcquisitionIEEE Journal of Solid-State Circuits10.1109/JSSC.2023.330092859:2(528-539)Online publication date: Feb-2024
  • (2024)Acoustic-based Alphanumeric Input Interface for Earables2024 33rd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN61486.2024.10637602(1-9)Online publication date: 29-Jul-2024
  • (2024)Hardware-Assisted Privacy-Preserving Multi-Channel EEG Computational Headwear2024 IEEE 20th International Conference on Body Sensor Networks (BSN)10.1109/BSN63547.2024.10780473(1-4)Online publication date: 15-Oct-2024
  • (2024)Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiersScientific Reports10.1038/s41598-024-52551-014:1Online publication date: 5-Feb-2024
  • (2023)Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing DataJournal of Personalized Medicine10.3390/jpm1312170313:12(1703)Online publication date: 12-Dec-2023
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