A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems
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In a pilot study (n = 10), a single-channel electroencephalogram and IMU sensor detected five different levels of drowsiness with an average of 95.2% accuracy.
“To explore the use of wearable devices to detect drowsiness, one recent pilot study (n = 10) used a single-channel electroencephalogram and IMU sensor to detect five different levels of drowsiness with an average of 95.2% accuracy [91].”
From Wearables and the Medical Revolution (2018) · cites this paper as [91]
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References 72 · Cited by 69
Research works printed but not linked: 3
- Q. Ji, Z. Zhu, P. Lan, Real-time nonintrusive monitoring and prediction of driver fatigue, IEEE transactions on vehicular tech- nology 53 (4) (2004) 1052-1068.
- A. Rahman, et al., Real time drowsiness detection using eye blink monitoring, in: NSEC, IEEE, 2015, pp. 1-7.
- J. Xu, R. F. Yazicioglu, B. Grundlehner, P. Harpe, K. A. A. Mak- inwa, C. V. Hoof, A 160uw 8-channel active electrode system for eeg monitoring, IEEE Transactions on Biomedical Circuits and Systems 5 (6) (2011) 555-567.
- Authors
- Kartsch, V. J., Benatti, S., Schiavone, P. D., Rossi, D., & Benini, L.
- Affiliations
- University of Bologna (IT)
- ETH Zurich (CH)
- Corresponding author
- Kartsch, Victor Javier
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 43
- Pages
- 66-76
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2770426008
- Cited by
- 69
- References
- 72
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 2 · 2023: 9 · 2022: 11 · 2021: 10 · 2020: 18 · 2019: 8 · 2018: 5
- FWCI
- 11.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 91 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Information Fusion (acceptedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by-nc-nd)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- Funders
- Swiss National Science Foundation (162524)
- ERC (ERC-AdG-291125)
- European Union’s Horizon 2020 research and innovation programme (732631)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- drowsiness detection, sensor fusion, wearable devices, ultra-low-power systems, EEG, inertial measurement unit, motion sensing, ARM Cortex-M4, energy optimization, battery life
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