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

@article{Pham2020WAKEAB,
  title={WAKE: a behind-the-ear wearable system for microsleep detection},
  author={Nhat Pham and Tuan Dinh and Zohreh Raghebi and Taeho Kim and Nam Bui and Phuc Nguyen and Anh-Hoang Truong and Farnoush Banaei Kashani and Ann C. Halbower and Thang N. Dinh and Tam N. Vu},
  journal={Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services},
  year={2020}
}
  • Nhat Pham, Tuan Dinh, +8 authors Tam N. Vu
  • Published 15 June 2020
  • Computer Science
  • Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services
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… 
BlinkListener: "Listen" to Your Eye Blink Using Your Smartphone
  • Jialin Liu, Dong Li, Lei Wang, Jie Xiong
  • Computer Science
    Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
  • 2021
TLDR
This research presents a probabilistic architecture for Ubiquitous Network and Service Software that automates the very labor-intensive and therefore time-heavy and expensive and therefore expensive and expensive process of developing and maintaining networks.
Earable Computing: A New Area to Think About
TLDR
This position paper argues that earphones hold the potential for major disruptions in mobile, wearable computing, and aims to discuss this landscape, including some challenges, opportunities, and applications.
Hearables, in-ear sensing devices for bio-signal acquisition: a narrative review
TLDR
The challenges and capabilities of hearables used to monitor human physiological signals are analyzed to improve practicability and implementation, wireless connectivity, battery life, impact of motion/environmental artifacts and comfort need to be addressed going forward.
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
TLDR
These algorithms are parallelized and optimized for a parallel ultra-low power (PULP) platform, enabling 300h of continuous monitoring on a 300 mAh battery, in a wearable form factor and power budget, and pave the way for the implementation of affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity.
Wearability and Comfort of Earables During Sleep
TLDR
The results suggest that available form factors are mostly not suitable for sleep and future earable designs can increase comfort by placing rigid parts behind the ear and using soft materials at the concha and in the ear canal.
eBP: an ear-worn device for frequent and comfortable blood pressure monitoring
TLDR
The key novelty of eBP includes a light-based inflatable pulse sensor which goes inside the ear, a digital air pump with a fine controller, and BP estimation algorithms that eliminate the need of blocking the blood flow inside the ears.
Epileptic Seizure Detection and Experimental Treatment: A Review
TLDR
This article discusses recent advances in seizure sensing, signal processing, time- or frequency-domain analysis, and classification algorithms to detect and classify seizure stages, and explains the fundamentals of brain stimulation approaches, including transcranial magnetic stimulation, and how to use them to treat seizures.
Opportunities in the Cross-Scale Collaborative Human Sensing of 'Developing' Device-Free and Wearable Systems
TLDR
A future of IoT human sensing systems that achieves seamless sensing across multiple scales through collaborative information inference by both categories of modalities is envisioned.

References

SHOWING 1-10 OF 140 REFERENCES
Fast Template Matching
  • 2009
Drowsy Driver Detection Through Facial Movement Analysis
TLDR
The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects, which is the highest prediction rate reported to date for detecting real drowsiness.
Procedure manual for polysomnography. https://tinyurl.com/ yc9ptdjz
  • [Online; accessed Apr
  • 2020
Ten20 conductive paste
  • https://www. weaverandcompany.com/products/ten20/. [Online; accessed Apr
  • 2020
Achieving a Fully Differential Output Using Single-Ended Instrumentation Amplifiers
  • 2019
Achieving a Fully Differential Output Using Single-Ended Instrumentation Amplifiers, 2019
  • https://www.analog.com/en/analog-dialogue/raqs/ raq-issue-161.html
  • 2019
Automatic detection of microsleep episodes with feature-based machine learning.
TLDR
The automatic detection of MSEs was successful for the EEG-based definition of M SEs, with good performance of all algorithms applied.
Fatigue − You're More Than Just Tired
  • 2019
Fatigue − You’re More Than Just Tired, 2019
  • https://www.nsc. org/work-safety/safety-topics/fatigue
  • 2019
Inter-individual variability of EEG features during microsleep events
Abstract This paper examines the question of how strongly the spectral properties of the EEG during microsleep differ between individuals. For this purpose, 3859 microsleep examples were compared
...
1
2
3
4
5
...