Fatigue and distraction detection

A review of commercially available devices to detect fatigue and distraction in drivers
Hermens, F.

A substantial portion of work-related deaths are due to road crashes during the course of work or on the way to and from work, and fatigue and distraction are known risk factors for such road crashes. While fatigue management programmes (e.g., maximum driving and minimum resting hours) are at the core of preventing fatigue related crashes, not all crashes may be prevented with such traditional measures. To reduce the risks of fatigue and distraction, devices have therefore been developed to warn drivers before starting their journey or while driving. The present report, commissioned by Shell Global Solutions International B.V., BP International Limited, Total S.A., and Chevron Services Company, provides a detailed comparison of around 100 such technologies and systems, with the overall aim to provide a recommendation on which devices to consider for further testing or use.

The first step in the comparison was the compilation of the list of devices. Devices were added that (1) were brought forward by the commissioners of this review, (2) were described in past reviews (mostly related to fatigue detection), (3) were found in an internet search for devices that specifically target distraction, or (4) were suggested by colleagues or suppliers. In a second step, devices were screened to determine (i) whether sufficient information was available to rate the device, (ii) whether the device was still on the market, and (iii) whether the device exclusively served to detect fatigue and / or distraction (or the consequences of fatigue and distraction) instead of a different general purpose (e.g., eye tracking) or a different purpose (e.g., illegal substance use).

In a third and important step, devices were rated on eight criteria: validity, intrusiveness, availability, robustness, sustainability, acceptability, cost, and compatibility with other devices in the vehicle or used by the driver. It was also determined whether the device would be portable, detect fatigue and distraction or fatigue as a ‘stand-alone’ device, in a non-intrusive way, or whether it would involve wearing a sensor, whether it would provide real-time feedback, and what kind of feedback it would provide. To make these judgments various sources were used: (a) the website of the supplier, (b) the scientific literature (Google scholar search, past reviews), (c) online articles (e.g., blogs, online newspapers, news websites), (d) online videos ( from suppliers and users), (e) reviews from users on commercial websites and forums, and (f) direct contact with suppliers.

While past reviews tended to classify devices into those that test fatigue before driving (fitness-for-duty tests), systems that monitor the driver, and systems that monitor driving performance, a more fine-grained distinction proved more appropriate in the present context. The present review therefore distinguishes devices that use (1) heart rate measurements, (2) head nodding, (3) EEG recordings, (4) measurements to test fitness-for-duty, (5) computer vision monitoring the road, (6) computer vision monitoring the driver, (7) computer vision monitoring both the road and the driver, (8) the closure of the eyelids (PERCLOS), (9) eye movements, (10) steering movements, (11) computational models to predict fatigue, (12) measurement of body temperature, (13) skin conductance, (14) video recordings and human analysis, (15) activity tracking in combination with a fatigue model. Based on the criteria, a subset of these systems was selected.

Judged strictly on the eight criteria outlined above, fitness-for-duty tests achieved the best scores, specifically for validity, cost, and acceptability. These tests, however, do not provide real-time feedback and only focus on fatigue. The only systems that detect distraction directly are the computer vision systems and eye trackers, which both use one or more cameras to monitor the driver’s face (which may lead to privacy concerns and, hence, acceptability issues). Computer vision systems have the advantage (over eye trackers) that they can also monitor for phone use, smoking, emotion (e.g., road rage), eating and drinking, which may also affect driving. Indirect measurements of distraction may be obtained from computer vision systems that monitor the road, and systems that monitor steering movements, but whether these systems can provide feedback with sufficient time left to prevent a crash, is unclear. Computer vision systems, however, suffer from a lack of scientific evidence of their validity and robustness, and suppliers are often hesitant to share their own test results, because of fierce competition in the market.

If the dominant goal is to detect fatigue, dry EEG systems that can be embedded inside a cap may provide a suitable alternative, as they have been tested in the scientific literature and show good validity, and there are suggestions that these systems may be sufficiently comfortable. Systems that monitor for eyelid closure have also been tested extensively in the literature and show good validity, but may be outperformed by computer vision and eye tracking systems that can also measure distraction. If computer vision systems, EEG systems and eyelid closure systems are found to cause too many acceptability issues, more elaborate fatigue predictions than from fitness-for-duty tests may be obtained from activity and sleep trackers that are combined with a fatigue model. A system that monitors steering movements in addition to a range of other variables, could provide a further alternative if the additional variables can be shown to compensate for poor validity of steering movements alone in real-world conditions.

Together, the results suggest that there is not a single system that outperforms all other systems on all criteria. For optimal monitoring for fatigue and distraction, a combination of systems or system features may therefore be needed. Selection of a particular system will also depend on user preferences. Before selecting a specific device, it is recommended to compare a range of devices in real-world conditions. Depending on the number of devices one would want to compare, the findings suggest that systems to consider are: (1) B-alert or Smart Cap (EEG), (2) Mobileye (monitoring the road), (3) Guardian, Eyesight and Stonkam (monitoring the driver, either higher-cost, or lower-cost), and (4) Nauto and Streamax (monitoring the road and the driver), PVT, OSPAT and/or FIT (fitness-for-duty). On the reserve list would be Readiband or Cat Smartband (activity trackers), Optalert (PERCLOS) and Bosch (steering movements).

Report number
SWOV, Den Haag

SWOV publication

This is a publication by SWOV, or that SWOV has contributed to.