A systematic review of physiological signals based driver drowsiness detection systems.
Journal article
Saleem, A.., Siddiqui, H.U.R., Raza, M.A., Rustam, F., Dudley-Mcevoy, S. and Ashraf, I. (2022). A systematic review of physiological signals based driver drowsiness detection systems. Cognitive neurodynamics. 17 (5), pp. 1229-1259. https://doi.org/10.1007/s11571-022-09898-9
| Authors | Saleem, A.., Siddiqui, H.U.R., Raza, M.A., Rustam, F., Dudley-Mcevoy, S. and Ashraf, I. |
|---|---|
| Abstract | Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals. [Abstract copyright: © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.] |
| Keywords | Muscle response; Physiological signals; Brain function; Eye movement; Respiration rate; Heart rate; Driver drowsiness detection |
| Year | 2022 |
| Journal | Cognitive neurodynamics |
| Journal citation | 17 (5), pp. 1229-1259 |
| Publisher | Springer |
| ISSN | 1871-4080 |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11571-022-09898-9 |
| https://doi.org/9898 | |
| Publication dates | |
| Online | 22 Oct 2022 |
| Publication process dates | |
| Accepted | 14 Sep 2022 |
| Deposited | 11 Jan 2024 |
| Accepted author manuscript | |
| Accepted author manuscript | License File Access Level Open |
| Supplemental file | File Access Level Restricted |
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