inside the cab. the device has a large field of view to accommodate operator head movements. the equipment uses eye-tracking software with a structured illumination approach that depends on the high contrast between the pupils and the face to identify and track the operator's pupils. alternatively, flexible and mobile tracking systems provide head and face tracking which include eye, eyelid and gaze tracking. these systems now provide real time feedback without the use of wire, magnets or headgear. 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. this system may therefore be prone to higher rates of false alarms and missed instances of impairment. facial features tracking 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 fatigue detection algorithm calculates aveclos. this is the percentage of time the eyes are fully closed during a one-minute interval. the technology was developed for the domestic and commercial markets and is currently being tested in a volvo demonstration vehicle. mobile platform recently, fatigue detection system software has been modified to run on android mobile phones. the technology utilises the mobile phone camera which is mounted in a stand on the cab dashboard to monitor operator eye movement. the developers of the system preferred to use eyelid movement technique. the robust system is capable of tracking fast head movements and facial expressions. external illumination is limited which reduce operator interference. other potential techniques were found to have drawbacks with the application of the specific hardware. yawning detection makes it difficult to precisely detect lip positions. detection of head nodding requires electrodes to be fixed to the scalp. further, deep learning methods for action recognition have also been successfully applied on mobile devices. 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) through a simple 60-second game-like experience. companies who have released fatigue impairment apps with this type of technology include predictive safety, based in denver, colorado, usa and aware360 based in calgary, alberta, canada. driver drowsiness detection the technologies discussed in previous sections, opened up the automotive safety landscape for various manufacturers to add new safety features to their production models. the drivers of the development of these features can be contributed as either regulatory pressure or the enhancement of the value offering of their product through added features. new developments in the car industry is as follows: further development is undertaken by nvidia, the chip supplier to audi, mercedes, tesla and others. nvidia is developing the co-pilot, an artificial-intelligence tool that can learn the behaviours of individual drivers and determine abnormal behaviour. for early drowsiness detection, plessey semiconductors developed sensors, to be placed in a seat, that monitor changes in heart rate. bosch, a german supplier of technology to many automotive companies, is developing a camera-based system that will monitor head and eye movements, as well as, body posture, heart rate and body temperature. valeo, another supplier of automotive technology, is developing an infrared camera system that will monitor children in the rear seat as well as the driver's shoulder, neck and head movements, looking for deviations from the norm. mercedes's attention assist monitors a driver's behaviour for the first 20 minutes behind the wheel to get a baseline of behaviours. then, the system checks those against as many as 90 indexes, such as steering wheel angle, lane deviation and external factors such as wind gusts and pothole avoidance. the application for these systems are not only limited to car manufacturers, but third party technological companies as well. these companies have developed hardware like the anti sleep pilot and vigo. anti-sleep pilot is a danish device that can be fitted to any vehicle that uses a combination of accelerometers and reaction tests. the vido is a smart bluetooth headset that detects signs of drowsiness through eye and head motion to alert users. by 2013 it was estimated that about 23% of new registered cars had various degrees of drowsiness detection systems in place. the importance of these systems can be contributed to safety regulatory bodies including these systems in their rating systems. regulatory systems like the euro ncap system primarily focuses on occupant safety ratings, pedestrian rating and child occupant ratings through the release of an overall 5-star rating. in 2009 a new category was added in the form of euro ncap advance safety assist systems, the euro ncap advanced reviews active safety monitoring systems of new car models and aims to provide car buyers with clear guidance about the safety benefits offered by these new technologies. here is a list of some advanced safety systems recently developed by car manufacturers. steering pattern monitoring, vision enhancements and autonomous emergency braking primarily uses steering input from electric power steering system, radar systems and cameras. these systems could facilitate autonomous braking in the case of drowsiness or distraction, when a driver physically does not act quickly enough. it also has the facility of autonomous driving in the prevention of an accident, when the driver reacts too slowly or not at all. vehicle position in lane monitoring uses lane monitoring camera and radar sensors. these systems can assist and warn you when you unintentionally leave the road lane or when you change lane without indication, commonly due to fatigue. these features are commonly referred to as blind spot monitoring, lane keep assist or lane departure monitoring. driver eye/face monitoring requires a camera watching the driver's face, referred to as attention assist, these systems detect and warns drivers to prevent them falling asleep momentarily whilst driving. physiological measurement requires body sensors for measure parameters like brain activity, heart rate, skin conductance and muscle activity. it is not limited to car drivers only. studies have also been done in assessing neuro-physiological measurements as a method to improve alertness of aircraft pilots. volkswagen vw has incorporated a system to assist drivers in the physical and mental well being when behind the wheel. the system monitors driver behavior closely, noting deviations that may be warning signs to driver fatigue. volvo volvo has developed driver alert control, a system that detects fatigued drivers and warns them before they fall asleep behind the wheel. driver alert control was the first fatigue detection system developed by a car manufacturer, and has been on the market since 2007. stanford research in 2009 stanford university researched automatic fatigue detection systems, concluding that technology relying on eyelid movement can be effective in determining driver fatigue in automobiles, but more research needs to be completed to improve accuracy.