Mine workers in developing countries depend on unreliable public transport systems which add additional commuting hours to their workday.
David Edwards PhD, Global Mining Safety Solutions Manager at Caterpillar Inc. compares it to asking a drunk person if they believe they are too intoxicated to drive.
Most mines and commercial truck fleets rely on soft controls such as procedures and other counter-measures to manage fatigue.
Common counter-measures that could potentially alleviate fatigue and improve alertness levels in haul truck drivers include; rest days, sleep management, well-designed shift work schedules and structured breaks during the shift, health screening and counselling, education programmes, food and fluid intake and devices for measuring driver's alertness.
[4] The National Highway Traffic Safety Administration (NHTSA) conservatively estimates that 100,000 police-reported crashes are the direct result of driver fatigue each year.
[5] The complex interaction of the major physiological factors responsible for sleepiness – circadian rhythms and the homeostatic drive for sleep – pose formidable technical challenges to the design and development of fatigue detection systems.
The technology must be robust and capable of high accuracies in diverse operational environments with constantly changing conditions and varying customer needs.
User acceptance is influenced by the following factors:[8][7] There were significant advancements in fatigue monitoring technology the past decade.
These innovative technology solutions are now commercially available and offer real safety benefits to drivers, operators and other shift workers across all industries.
The technology has the potential to be a highly accurate tool for detecting the early stages of fatigue in drivers and minimise the likelihood of incidents.
A dramatic reduction in size, weight and cost of EEG instrumentation and the potential to communicate wirelessly with other digital systems paved the way to extend the technology to previously unsuspected fields, such as entertainment, bio-feedback and support for learning and memory training.
Apart from developing practical wearable technology, the universal mapping of EEG information to a useful measurement is required for accurate fatigue monitoring in an operating environment.
Although EEG analysis is well advanced, scientists found that due to natural physiological person-to-person variations, rigorous rules to interpret brain activity cannot effectively be applied to the entire population.
The implication is that the result gets progressively universal and significant as more data from a wider range of individuals are included in the algorithm.
[11] Various real-time operator drowsiness detection systems use PERCLOS assessment and proprietary developed software to determine the onset of fatigue.
Each technology developer use a unique set-up and combination of hardware to improve the accuracy and ability to track eye movement, eyelid behaviour, head and face poses under all possible circumstances.
Although studies confirmed a correlation between PERCLOS and impairment, some experts are concerned by the influence which eye-behaviour unrelated to fatigue levels may have on the accuracy of measurements.
Dust, insufficient lighting, glare and changes in humidity are non-fatigue related factors that may influence operator eye-behaviour.
[10] The computer vision system utilises an unobtrusive dashboard mounted camera and two infra-red illumination sources to detect and track the facial features of the operator.
The system analyses eye closures and head poses to determine early onset of fatigue and distraction.
The technology utilises the mobile phone camera which is mounted in a stand on the cab dashboard to monitor operator eye movement.
[14] Deep learning techniques do not require separate feature selection steps to identify eye, mouth or head positions and have the potential to further increase prediction accuracy.
App-based technologies have also been released that do not use cameras, but instead leverage the Bowles-Langley Test (BLT)[15] through a simple 60-second game-like experience.
The Vido is a smart Bluetooth headset that detects signs of drowsiness through eye and head motion to alert users.
These systems could facilitate autonomous braking in the case of drowsiness or distraction, when a driver physically does not act quickly enough.
Driver Alert Control was the first fatigue detection system developed by a car manufacturer, and has been on the market since 2007.