Sleepiness detection for vehicle driver or machine operator

A vehicle driver or machine operator sleepiness monitor, configured as a self-contained module, for steering wheel or dashboard mounting, provides for individual driver/operator interrogation and response, combined with various objective sensory inputs on vehicle condition and driver control action, and translates these inputs into weighing factors to adjust a biological activity circadian rhythm reference model, in turn to provide an audio-visual sleepiness warning indication.

BACKGROUND OF THE INVENTION
 This invention relates to human sleepiness, drowsiness or (lack of)
 alertness detection and monitoring, to provide a warning indication in
 relation to the capacity or fitness to drive or operate (moving)
 machinery.
 Although its rationale is not fully understood, it is generally agreed that
 sleep is a powerful and vital, biological need, which--if ignored--can be
 more incapacitating than realised, either by a sleepy individual subject,
 or by those tasking the subject.
 As such, the invention is particularly, but not exclusively, concerned with
 the (automated) recognition of sleepiness and performance-impaired fatigue
 in drivers of motor vehicles upon the public highway.
 Professional drivers of, say, long-haul freight lorries or public transport
 coaches are especially vulnerable to fatigue, loss of attention and
 driving impairment.
 With this in mind, their working and active driving hours are already
 carefully monitored to ensure they are within prescribed limits.
 Road accidents, some with no apparent external cause, have been attributed
 to driver fatigue.
 Studies, including those by the Applicants themselves, (see the list of
 references at the end of this disclosure), into sleep-related vehicle
 accidents have concluded that such accidents are largely dependent on the
 time of day.
 Age may also be a factor--with young adults more likely to have accidents
 in the early morning, whereas older adults may be more vulnerable in the
 early afternoon.
 Drivers may not recollect having fallen asleep, but may be aware of a
 precursory sleepy state, as normal sleep does not occur spontaneously
 without warning.
 The present invention addresses sleepiness monitoring, to engender
 awareness of a state of sleepiness, in turn to prompt safe
 countermeasures, such as stopping driving and having a nap.
 Accidents have also been found to be most frequent on monotonous roads,
 such as motorways and other main roads.
 Indeed, as many as 20-25% of motorway accidents seem to be as a result of
 drivers falling asleep at the wheel.
 Although certain studies concluded that it is almost impossible to fall
 asleep while driving without any warning whatsoever, drivers frequently
 persevere with their driving when they are sleepy and should stop.
 Various driver monitoring devices, such as eyelid movement detectors, have
 been proposed to assess fatigue, but the underlying principles are not
 well-founded or properly understood.
 Sleepiness in the context of driving is problematic, because the
 behavioural and psychological processes which accompany falling asleep at
 the wheel may not typify the characteristics of sleep onset commonly
 reported under test conditions and simulations by sleep laboratories.
 Driving will tend to make a driver put considerable effort into remaining
 awake, and in doing so, the driver will exhibit different durations and
 sequences of psychological and behavioural events that precede sleep
 onset.
 As underlying sleepiness may be masked by this prefacing compensatory
 effort, the criteria for determining whether a subject is falling asleep
 may be unclear.
 Indeed, the Applicants have determined by practical investigation that
 parameters usually accepted to indicate falling asleep are actually not
 reliable as an index of sleepiness if the subject is driving.
 For example, although in general eye blink rate has a tendency to rise with
 increasing sleepiness, this rate of change is confounded by the demand,
 variety and so stimulus content or level of a task undertaken (eg
 driving), there being a negative correlation between blink rate and task
 difficulty.
 In an attempt to prevent sleep-related vehicle accidents, it is also known
 passively to monitor driver working times through chronological activity
 logs, such as tachographs. However, these provide no active warning
 indication.
 More generally, it is also known to monitor a whole range of machine and
 human factors for vehicle engineering development purposes, some merely
 for historic data accumulation, and other unsatisfactory attempts at
 `real-time` active warning.
 The Applicants are not aware of any practical implementation hitherto of
 sleepiness detection, using relevant and proven biological factors
 addressing inherent body condition and capacity.
 Studies and trials carried out by the Applicants have shown that there are
 clear discernible peaks of sleep-related vehicle accidents in the UK
 around 02.00-06.00 hours and 14.00-16.00 hours.
 Similar time-of-day data for such accidents have been reported for the USA,
 Israel and Finland.
 These sleep-related vehicle accident peaks are distinct from the peak times
 for all road traffic accidents in the UK--which are around the main
 commuting times of 08.00 hours and 17.00 hours.
 The term `sleepiness` is used herein to embrace essentially pre-sleep
 conditions, rather than sleep detection itself, since, once allowed to
 fall asleep, it may be too late to provide useful accident avoidance
 warning indication or correction.
 Generally, a condition or state of sleepiness dictates
 a lessened awareness of surroundings and events
 a reduced capacity to react appropriately; and
 an extended reaction time.
 It is known from sleep research studies that the normal human body
 biological or physiological activity varies with the time of day, over a
 24 hour, (night-day-night) cycle--in a characteristic regular pattern,
 identified as the circadian rhythm, biorhythm or body clock.
 The human body thus has a certain predisposition to drowsiness or sleep at
 certain periods during the day--especially in early morning hours and mid
 afternoon.
 This is exacerbated by metabolic factors, in particular consumption of
 alcohol, rather than necessarily food per se.
 SUMMARY OF THE INVENTION
 According to one aspect of the invention a monitor taking account of
 circadian and sleep parameters of an individual vehicle driver, and/or
 generic or universal human physiological factors, applicable to a whole
 class or category of drivers, is integrated with `real-time` behavioural
 sensing, such as of road condition and driver control action, including
 steering and acceleration, to provide an (audio-) visual indication of
 sleepiness.
 For safety and legislative reasons, it is not envisaged that, at least in
 the immediate future, an alert condition would necessarily be allowed
 automatically to override driver control--say by progressively disabling
 or disengaging the vehicle accelerator.
 Rather, it would remain a driver's responsibility to respond constructively
 to an alert issued by the system--which could log the issue of such
 warnings for future reference in assessing compliance.
 Overall system capability could include one or more of such factors as:
 common, if not universal, underlying patterns or sleepiness
 (pre-conditioning);
 exacerbating personal factors for a particular user--driver, such as recent
 sleep patterns especially, recent sleep deprivation and/or disruption;
 with a weighting according to other factors, such as the current time of
 day.
 Thus background circumstances, in particular a natural alertness `low
 point`--and attendant sleepiness or susceptibility to (unprompted)
 sleep--in the natural physiological biorhythmic or circadian cycle may
 pre-dispose a driver to sleepiness, exacerbated by sleep deprivation in a
 recent normal sleep period.
 If not circadian rhythm patterns themselves, at: least the ability of the
 body behaviour and activity to respond to the underlying pre-disposition
 or pre-condition, may be disturbed or frustrated by abnormal or changing
 shift: patterns, prefaced by inadequate acclimatisation.
 Thus, for example, in exercising vehicle control, aberrant driver steering
 behaviour, associated with degrees of driver sleepiness, could be
 recognised and corrected--or at: least a warning issued of the need for
 correction (by sleep restitution).
 Pragmatically, any sleepiness warning indication should be of a kind and in
 sufficient time to trigger corrective action.
 According to another aspect of the invention, a driver sleepiness,
 alertness or fitness condition monitor comprises a plurality of sensory
 inputs, variously and respectively related to, vehicle motion and steering
 direction, circadian or biorhythmic physiological patterns, recent driver
 experiences and preconditioning;
 such inputs being individually weighted, according to contributory
 importance, and combined in a computational decision algorithm or model,
 to provide a warning indication of sleepiness.
 Some embodiments of the invention can take into account actual, or
 real-time, vehicle driving actions, such as use of steering and
 accelerator, and integrate them with inherent biological factors and
 current personal data, for example recent sleep pattern, age, sex, recent
 alcohol consumption (within the legal limit), reliant upon input by a
 driver being monitored.
 Steering action or performance is best assessed when driving along a
 relatively straight road, such as a trunk, arterial road or motorway, when
 steering inputs of an alert driver are characterised by frequent, minor
 correction.
 In this regard, certain roads have characteristics, such as prolonged
 `straightness` and monotonous contouring or landscaping, which are known
 to engender or accentuate driver sleepiness.
 It is envisaged that embodiments of the steering detector will also be able
 to recognise when a vehicle is on such (typically straighter) roads.
 Some means, either automatically through a steering sensor, or even from
 manual input by the driver, is desirable for motorway as opposed to, say,
 town driving conditions, where large steering movements obscure steering
 irregularities or inconsistencies.
 Indeed the very act of frequent steering tends to contribute to, or
 stimulate, wakefulness. Yet a countervailing tendency to inconsistent or
 erratic steering input may prevail, which when recognised can signal an
 underlying sleepiness tendency.
 In practice, having recognised the onset of journeys on roads with an
 enhanced sleepiness risk factor, journey times on such roads beyond a
 prescribed threshold--say 10 minutes--could trigger a steering action
 detection mode, with a comparative test against a steering characteristic
 algorithm, to detect sleepy-type driving, and issue a warning indication
 in good time for corrective action.
 As another vehicle control condition indicator, accelerator action, such as
 steadiness of depression, is differently assessed for cars than lorries,
 because of the different spring return action.
 Implementation of semi-automated controls, such as cruise-controls, with
 constant speed setting capabilities, could be disabled temporarily for
 sleepiness monitoring.
 In assessing driver responses to pre-programmed device interrogation,
 reliance is necessarily placed upon the good intentions, frankness and
 honesty of the individual.
 A practical device would embody a visual and/or auditory display to relay
 warning messages and instructions to and responses from the user.
 Similarly, interfaces for vehicle condition sensors, such as those
 monitoring steering and accelerator use, would be incorporated.
 Furthermore, input (push-button) switches for driver responses could also
 be featured--conveniently adjacent the visual display.
 Input effort would be minimal to encourage participation, and questions
 would be straightforward and direct, to encourage explicit answers.
 Visual display reinforcement messages could be combined with the auditory
 output.
 Ancillary factors, such as driver age and sex, could also be input.
 An interface with a global positioning receiver and map database could also
 be envisaged, so that the sleepiness indicator could register
 automatically roads with particular characteristics, including a poor
 accident record, and adjust the monitoring criteria and output warning
 display accordingly.
 The device could be, say, dashboard or steering wheel mounted, for
 accessibility and readability to the driver.
 Ambient external light conditions could be sensed by a photocell. Attention
 could thus be paid at night to road lighting conditions.
 Vehicle driving cab temperature could have a profound effect upon
 sleepiness, and again could be monitored by a localised transducer at the
 driver station.
 The device could categorise sleepiness to an arbitrary scale. Thus, for
 example, the following condition levels could be allocated:
 ALERT
 A LITTLE SLEEPY
 NOTICEABLY SLEEPY
 DIFFICULTY IN STAYING AWAKE
 FIGHTING SLEEP
 WILL FALL ASLEEP
 Personal questions could include:
 QUANTITY OF SLEEP IN THE LAST 24 HOURS
 QUALITY OF THAT SLEEP IN THE LAST 24 HOURS
 Road conditions could include:
 MOTORWAY
 MONOTONOUS
 TOWN
 Night-time with no street lights could be given a blanket impairment rating
 or loading.
 Assumptions are initially made of no alcohol consumption whatsoever (ie
 legal limits disregarded).
 A circadian rhythm model allows a likelihood of falling asleep, or a sleep
 propensity, categorised between levels 1 and 4--where 4 represents very
 likely and 1 represents unlikely.
 The lowest likelihood of sleepiness occurs from mid morning to early
 afternoon.
 Thereafter a mid afternoon lull, or rise in likelihood of sleepiness to 3
 is followed by another trough of 1 in early evening, rising stepwise
 towards late night, through midnight and into the early hours of the
 morning.

DESCRIPTION OF THE PREFERRED EMBODIMENTS
 Referring to FIG. 1, a sleepiness monitor 10 is integrated within a housing
 11, configured for ease of in-vehicle installation, for example as a
 dashboard mounting, or, as depicted in FIG. 2, mounted on the steering
 wheel 12 itself. The monitor 10 may include a memory 10a and computer 10b.
 In a preferred variant, the monitor 10 could be self-contained, with an
 internal battery power supply and all the necessary sensors fitted
 internally, to allow the device to be personal to a driver and moved with
 the driver from one vehicle to another.
 An interface 19, for example a multi-way proprietary plug-and-socket
 connector, is provided in the housing, to allow interconnection with an
 additional external vehicle battery power supply and various sensors
 monitoring certain vehicle conditions and attendant driver control action.
 Thus a steering wheel movement sensor 13 monitors steering inputs from a
 driver (not shown) to steering wheel 12.
 The sensor 13 could be located within the steering wheel 12 and column
 assembly.
 More sophisticated integrated multi-channel, remote sensing is described
 later in relation to FIGS. 11 and 12.
 Similarly, an accelerator movement sensor 15 monitors driver inputs to an
 accelerator pedal 14.
 Alternatively, and again in a more sophisticated sensor variant, a dynamic
 accelerometer could be employed, as in FIGS. 11 and 12.
 The sensor 15 could be an accelerometer located within the housing 11 in a
 self-contained variant. Care is taken to obviate the adverse effects of
 vehicle vibration upon dynamic sensory measurements.
 Albeit, somewhat less conveniently, vehicle motion and acceleration could
 be recognised through a transmission drive shaft sensor 27, coupled to a
 vehicle road wheel 26 or by interfacing with existing sensors or control
 processors for other purposes, such as engine and transmission management.
 The trend to multiplex vehicle electrical supply systems, relaying data
 between vehicle operational modules, may facilitate such interconnection.
 More sophisticated sensors, with an ability for remote self-contained
 condition sensing, data accumulation and data transfer, data down-loading
 or data up-loading may be employed.
 Thus, for example, a steering wheel movement sensor module, the version of
 FIG. 20, may rely upon a wireless or contact-free linkage--such as
 magnetic flux coupling between magnetic elements on the wheel or shaft and
 an adjacent static inductive or capacitative transducer to register rate
 of change of wheel movement (as opposed to, say an average RMS computation
 of FIGS. 15A and 15B).
 Such remote sensing and data linkage obviates the need for major vehicle
 wiring harness alteration or supplement.
 Overall, the device could have an internal memory of speed and steering
 wheel movements and so the basis of a `performance history` of driver
 actions as a basis for decision upon issuing warning indication.
 The interface 19 would enable data to be down-loaded onto a PC via, say,
 the PC parallel port or over a radio or infra-red `wireless` link.
 A further photocell sensor 29 monitors ambient light conditions from the
 driving position and is calibrated to assess both day-night transitions
 and the presence or absence of street lighting at night.
 In the variants 10, 12 and 13A through 13D, multi-mode or multiple
 (independent) factor sensing is integrated within a common co-called
 `steering wheel adaptor` module 33.
 Reverting to the unit 10 of FIGS. 1 and 2, the housing 11 incorporates a
 visual display panel or screen 18, for relaying instructions and warning
 indications to the user.
 A touch-sensitive inter-actional screen could be deployed.
 Manual or automated adjustment for screen contrast according to ambient
 light conditions could be embodied.
 The variants of FIGS. 10, 12 and 13A through 13D allow for a simpler
 devolved display of certain operating criteria, by multiple LED's on a
 multi-mode sensor module 33.
 A loudspeaker 21 can relay reinforcement sound messages, for an integrated
 audio-visual driver interaction.
 Also to that end, in a more sophisticated variant--possibly merely as an
 ongoing research and development tool, a microphone 23 might be used to
 record and interpret driver responses, possibly using speech recognition
 software.
 Alternatively, interactive driver interrogation and response can be
 implemented by a series of push button switches 16 arrayed alongside the
 screen 18, for the input of individual driver responses to preliminary
 questions displayed upon the screen 18.
 Thus, for example, non-contentious factors, such as driver age and sex may
 be accounted for, together with more subjective review of recent sleep
 history.
 Questions would be phrased concisely and unequivocally, for ease and
 immediacy of comprehension and certainty or authenticity of response.
 Thus, for example, on the pivotal contributory factor of driver's recent
 sleep, the question:
 `How much sleep have you had in the last 24 hours` could be juxtaposed with
 a multiple choice on screen answer menu, such as:
 Choice of ONE answer . . .
 Little or none . . . [generating a weighting score of 2]
 Less than normal . . . [score 1]
 About the same as normal, undisturbed . . . [score 0]
 About the same as normal, but disturbed . . . [score 1]
 Other contributory factors include road conditions and vehicle cabin
 temperature.
 Road conditions would be assessed through the steering sensor 13, and
 through an initial input question upon road conditions.
 Thus, a dull, monotonous road would justify a weighting of plus 1 to all
 the circadian scores.
 On the other hand, town driving, promoting greater alertness from external
 stimuli, would merit a score of minus 1.
 Vehicle cabin temperature is taken into account, primarily to register
 excessively high temperatures which might induce sleepiness.
 Driver cab temperatures could be monitored with a temperature sensor probe
 31 (located away from any heater output vents).
 Thus, for example, a threshold of some 25 degrees C might be set, with
 temperatures in excess of this level triggering a score of plus 0.5.
 In normal operating mode, the monitor relies upon the working assumption
 that the driver has had little or no recent or material alcohol
 consumption.
 The physiological circadian rhythm `template` or reference model pre-loaded
 into the monitor memory is adjusted with the weighting scores indicated.
 If the cumulative score is equal to or greater than 3, the steering sensor
 is actively engaged and checked to determine the road conditions.
 The sleepiness scale values, reflected in the unweighted graph of FIG. 3,
 can broadly be categorised as:
 ALERT
 NEITHER ALERT NOR SLEEPY
 A LITTLE SLEEPY
 NOTICEABLY SLEEPY
 DIFFICULTY IN STAYING AWAKE
 FIGHTING SLEEP
 WILL FALL ASLEEP
 An internal memory module may store data from the various remote sensors
 13, 15, 27, 29, 31--together with models or algorithms of human body
 circadian rhythms and weighting factors to be applied to individual
 sensory inputs.
 An internal microprocessor is programmed to perform calculations according
 to driver and sensory inputs and to provide an appropriate (audio-)visual
 warning indication of sleepiness through the display screen 18.
 FIG. 2 shows a steering-wheel mounted variant, in which the housing 11 sits
 between lower radial spokes 17 on the underside of a steering wheel 12--in
 a prominent viewing position for the driver, but not obstructing the
 existing instrumentation, in particular speedometer, nor any air bag
 fitted.
 Ambient temperature and lighting could also be assessed from this steering
 wheel vantage point.
 This location also facilitates registering of steering wheel movement. With
 an internal accelerometer and battery, external connections could be
 obviated.
 Whilst a motor vehicle orientated monitor has been disclosed in the
 foregoing example, the operating principles are more widely applicable to
 moving machine-operator environments, as diverse as cranes, construction
 site excavators and drilling rigs--possibly subject to further research
 and development.
 FIGS. 4 through 9 show the respective steering `performances` of three
 individual subjects, designated by references S1, S2 and S3, under alert
 and sleepy (simulated) driving conditions.
 Each graph comprises two associated plots, representing steering wheel
 movement in different ways.
 Thus, one plot directly expresses deviations of steering wheel position
 from a straight-ahead reference position--allotted a `zero` value.
 This plot depicts the number of times a steering wheel is turned in either
 direction, over a given time period--allowing for a .+-.3% `wobble` factor
 as a `dead` or neutral band about the reference position.
 The other plot is an averaged value of steering wheel movement amplitude
 (ie the extent of movement from the reference position)--using the RMS
 (root mean squared) of the actual movements.
 Generally, the graphs reflect a characteristic steering performance or
 behaviour.
 In particular, as a person becomes sleepy, zero crossings are reduced in
 frequency, whereas RMS amplitudes increase and/or become more variable.
 Thus, FIG. 4 reflects steering behaviour of an alert subject S1.
 Collectively, the `zero-crossing` and `RMS` plots for alert subject S1
 reflect a generally continual and consistent steering correction.
 In contrast, the steering behaviour of a sleepy subject S1, reflected in
 FIG. 5, exhibits less frequent, erratic, exaggerated or excessive steering
 movement.
 FIG. 6 reflects steering behaviour for another alert subject S2, whilst
 FIG. 7 shows the corresponding readings when the same subject was sleepy.
 FIG. 8 reflects steering behaviour of yet another alert subject S3 and FIG.
 9 that of that subject S3 when sleepy.
 Each pair of graphs shows corresponding marked differences in steering
 behaviour between an alert and sleepy driver.
 This characteristic difference validates the use of actual or real-time
 dynamic steering behaviour to monitor driver sleepiness.
 In a practical system, using steering wheel movement to identify
 sleepiness, on the basis of such findings, it is preferred that, before
 presenting a sleepiness warning indication, at least two of the following
 three sleep categorising conditions of steering behaviour are present,
 namely:
 Fewer zero crossings;
 RMS amplitude high;
 RMS more variable.
 RMS averaging may be superseded by other sensing techniques, such as that
 of the magnetic flux-coupled, inductive sensor of FIG. 20, which can
 register more directly rate of change of steering wheel movement.
 Turning to refinement of practical implementation, FIG. 10 shows a block
 schematic overall circuit layout or principle elements.
 More specifically, the various sensing modes--including vehicle motion
 (linear acceleration), steering wheel angle, ambient light, temperature,
 are combined with an audio sounder and mark button in an integrated
 so-called `steering wheel adaptor` module 33.
 The sensor module 33 is connected through a cable way to an electronic
 interface 32, which in turn is configured for connection to a personal
 computer parallel port 39 through a cable link and a mains charger unit
 37.
 The orientation of the sensor module 33 in relation to reference axes for
 acceleration and steering wheel angular position are represented in FIGS.
 11 and 12.
 Angular sensing could be, say, through a variable magnetic flux coupling
 between magnets set on the steering wheel or column and on adjacent static
 mounts.
 FIGS. 13A through 13D show a master sensor unit 33 with a simplified LED
 warning indicator array. The detailed circuitry is shown in FIG. 20.
 Essentially, the steering sensor measures a change in inductance through an
 array of some three inductors L1, L2 and L3 through magnetic flux coupling
 changes caused by movement in relation to the magnetic field of a small
 magnet `M` static-mounted upon the steering column--at a convenient,
 unobtrusive location.
 The inductors L1, L2 and L3 are energised by a 32 kHz square wave generated
 by a local processor clock.
 Induced voltage is rectified, smoothed, sampled and measured by the local
 processor some 16 times per second.
 The processor analyses the results digitally to determine the extent of
 steering wheel movement.
 Calibration of the minimum and maximum voltages across each inductor as the
 magnetic field of the static magnet sweeps across them when the steering
 wheel is fully turned is undertaken by the local processor, so the
 mounting location of the static magnet is not overtly critical.
 Such inductive sensing is unaffected by road vibration, since both static
 magnet and inductors are subject to the same vibration and any effect
 cancelled out.
 The local processor feeds sensor data to an executive processor loaded with
 sleepiness detector algorithms, based upon such factors as circadian
 rhythm of sleepiness, timing and duration of sleep and ambient light, and
 which presents an overall indication of driver sleepiness level.
 The arrangement is capable of registering and measuring very small angular
 movements, such as might be encountered in corrective steering action at
 speed.
 FIGS. 14A through 15D relate to wheel movement sensing by a more direct
 computational technique, involving RMS averaging, compared with the direct
 rate of change capability of magnetic flux-coupled inductive sensing of
 the FIG. 20 circuitry.
 FIGS. 14A and 14B represent dynamic steering wheel movement sensing.
 FIGS. 15A and 15B represent respectively `raw` and adjusted wheel movements
 over time.
 FIGS. 15C represents a `zero crossings` count, derived from the adjusted
 plot of FIG. 15B.
 FIG. 15D depicts the `dead band` range of wheel movement allowed.
 FIGS. 16A and 16B respectively, represent `raw` and corrected plots of
 vehicle acceleration over time--allowing computation of an RMS average
 acceleration.
 FIG. 17 depicts a characteristic circadian sleepiness rhythm or pattern,
 with three sleepiness warning threshold levels.
 FIG. 18 represents a breakdown of system activity over (T=60 second)
 operational clock cycles--with a division between monitoring the various
 sensors over 50 seconds and 10 seconds process time allocation for
 parameter calculation, test and warning issue, display screen update,
 sensor data storage of calculated parameters.
 FIG. 19 represents data storage array allocation, for monitoring and
 learning of factors such as vehicle acceleration and wheel movement.
 FIG. 21 depicts the flow of information during the memory, operation
 control input, computational means, and the sleepiness warning indicator.
 Hardware considerations aside, an operation software protocol would involve
 a schema of factors, such as is represented in the Tables below which are
 generally self-explanatory and will not otherwise be discussed.
 Component List
 10 (sleepiness) monitor
 11 housing
 12 steering wheel
 13 steering position/movement sensor
 14 accelerator pedal
 15 accelerator position/movement sensor
 16 push-button switch
 17 steering wheel spokes
 18 display panel/screen
 19 interface connector
 21 loudspeaker
 23 microphone
 26 road wheel
 27 (drive) shaft sensor
 29 photocell sensor
 31 temperature probe
 33 multi-mode sensor
 32 electronic interface
 37 mains charger
 39 parallel data port
 LITERATURE REFERENCES
 J. Sleep Research 1994 vol 3 p195; `Accidents & Sleepiness`: consensus of
 Stockholm International Conference on work hours, sleepiness and
 accidents.
 J. Sleep Research 1995 suppl. 2 p23-29; `Driver Sleepiness`: J. A. Horne &
 L. A. Reyner
 British Medical Journal 4 March 1995 vol 310 p565-567; `Sleep related
 vehicle accidents`: J. A. Horne & L. A. Reyner
 Int J Legal Med 1998; `Falling asleep whilst driving: are drivers aware of
 prior sleepiness?: L. A. Reyner & J. A. Horne
 TABLE 1
 Acc # 1-Vehicle Motion
 Acc # 2-Wheel Angle
 Light Sensor - Ambient
 Temp Sensor - Ambient
 Sounder
 Mark Button
 TABLE 1
 Acc # 1-Vehicle Motion
 Acc # 2-Wheel Angle
 Light Sensor - Ambient
 Temp Sensor - Ambient
 Sounder
 Mark Button
 TABLE 1
 Acc # 1-Vehicle Motion
 Acc # 2-Wheel Angle
 Light Sensor - Ambient
 Temp Sensor - Ambient
 Sounder
 Mark Button
 TABLE 1
 Acc # 1-Vehicle Motion
 Acc # 2-Wheel Angle
 Light Sensor - Ambient
 Temp Sensor - Ambient
 Sounder
 Mark Button
 TABLE 5
 ##EQU1##
 TABLE 5
 ##EQU2##
 TABLE 5
 ##EQU3##
 TABLE 5
 ##EQU4##
 TABLE 9
 Engineering Scaling Factors
 K acc (mm/s/s/bit) Acceleration Channel
 K wheel (mm/s/s/bit) Steering Channel
 K light (Lx/bit) Light Channel
 K temp (mDegC/bit) Temp Channel
 ZeroLight (bit) Intercept adjust - Light
 ZeroTemp (bit) Intercept adjust - Temp
 Alpha (Deg) Steering Wheel Inclination from Vertical
 Hysterisis (Deg) Hesterisis factor - Zero X analysis
 TABLE 10
 Sleep Propensity Algorithm - Definition
 S mod = S circ + S zerox + S rms + S light + S temp +
 S sleep + S road + S trip
 Elemental Bound Limit
 S mod 0 &lt; S mod &lt; 1
 S circ 0 &lt; S circ &lt; 1
 S zerox = (F zerox/100) (Z ref-Z) 0 &lt; S zerox
 S rms = (F rms/100) (R-R ref) 0 &lt; S rms
 S light = (F light/100) (I ref -I) 0 &lt; S light
 S temp = (F temp/100) (T -T ref) 0 &lt; S temp
 S sleep = (F sleep/100) (H ref - (HXQ)) 0 &lt; S sleep
 S road = (F road/100) (G ref -G) 0 &lt; S road
 S trip = (F trip/100) .times. D 0 &lt; S trip
 TABLE 10
 Sleep Propensity Algorithm - Definition
 S mod = S circ + S zerox + S rms + S light + S temp +
 S sleep + S road + S trip
 Elemental Bound Limit
 S mod 0 &lt; S mod &lt; 1
 S circ 0 &lt; S circ &lt; 1
 S zerox = (F zerox/100) (Z ref-Z) 0 &lt; S zerox
 S rms = (F rms/100) (R-R ref) 0 &lt; S rms
 S light = (F light/100) (I ref -I) 0 &lt; S light
 S temp = (F temp/100) (T -T ref) 0 &lt; S temp
 S sleep = (F sleep/100) (H ref - (HXQ)) 0 &lt; S sleep
 S road = (F road/100) (G ref -G) 0 &lt; S road
 S trip = (F trip/100) .times. D 0 &lt; S trip
 TABLE 12
 Algorithm Weighting Factors - F
 Note: Factors are % S Unit per Parameter Unit
 F zerox (% S/#/min) Corrective Steering Reversal Rate Deficit - % Factor
 F rms (% S/Deg) RMS Corrective Steering Amplitude Surfit -
 % Factor
 F light (% S/kLx) Average Ambient Lighting Intensity Deficit -
 % Factor
 F temp (% S/DegC) Average Ambient Temperature Surfit - % Factor
 F sleep (%S/Hr) Prior to Good Hours Sleep Deficit - % Factor
 F road (% S/m/s/s) Road Activity Deficit - % Factor
 F trip (% S/Hr) Accumulated Trip Duration - % Factor
 TABLE 12
 Algorithm Weighting Factors - F
 Note: Factors are % S Unit per Parameter Unit
 F zerox (% S/#/min) Corrective Steering Reversal Rate Deficit - % Factor
 F rms (% S/Deg) RMS Corrective Steering Amplitude Surfit -
 % Factor
 F light (% S/kLx) Average Ambient Lighting Intensity Deficit -
 % Factor
 F temp (% S/DegC) Average Ambient Temperature Surfit - % Factor
 F sleep (%S/Hr) Prior to Good Hours Sleep Deficit - % Factor
 F road (% S/m/s/s) Road Activity Deficit - % Factor
 F trip (% S/Hr) Accumulated Trip Duration - % Factor
 TABLE 14
 Algorithm Dynamic Variables
 Z (#/min) Current Corrective Steering Zero X Rate
 R (Deg) Current RMS Corrective Steering Amplitude
 I (kLx) Current Ambient Lighting Intensity
 T (DegC) Current Ambient Temperature
 G (m/s/s) Current Road Activity - RMS Acceleration / Deceleration
 D (Hr) Accumulated Trip Duration
 H (Hr) Actual Hours of Prior Sleep
 Q (#) Prior Sleep Quality - Normalised Scale 0 . . . 1
 Qx (#) Prior Sleep Quality
 User Scale 1, 2, 3, 4, 5
 Q = Qx/5
 TABLE 14
 Algorithm Dynamic Variables
 Z (#/min) Current Corrective Steering Zero X Rate
 R (Deg) Current RMS Corrective Steering Amplitude
 I (kLx) Current Ambient Lighting Intensity
 T (DegC) Current Ambient Temperature
 G (m/s/s) Current Road Activity - RMS Acceleration / Deceleration
 D (Hr) Accumulated Trip Duration
 H (Hr) Actual Hours of Prior Sleep
 Q (#) Prior Sleep Quality - Normalised Scale 0 . . . 1
 Qx (#) Prior Sleep Quality
 User Scale 1, 2, 3, 4, 5
 Q = Qx/5
 TABLE 14
 Algorithm Dynamic Variables
 Z (#/min) Current Corrective Steering Zero X Rate
 R (Deg) Current RMS Corrective Steering Amplitude
 I (kLx) Current Ambient Lighting Intensity
 T (DegC) Current Ambient Temperature
 G (m/s/s) Current Road Activity - RMS Acceleration / Deceleration
 D (Hr) Accumulated Trip Duration
 H (Hr) Actual Hours of Prior Sleep
 Q (#) Prior Sleep Quality - Normalised Scale 0 . . . 1
 Qx (#) Prior Sleep Quality
 User Scale 1, 2, 3, 4, 5
 Q = Qx/5
 TABLE 17
 User Software Functions
 Set Display Parameters
 Enter New Values and &lt;RET&gt; or &lt;RET&gt; to bypass edit option.
 Display History (min) Graphic display history length - Last N minutes
 FSD (S) Graphic display full scale - S unit (0 . . . 1)
 TABLE 17
 User Software Functions
 Set Display Parameters
 Enter New Values and &lt;RET&gt; or &lt;RET&gt; to bypass edit option.
 Display History (min) Graphic display history length - Last N minutes
 FSD (S) Graphic display full scale - S unit (0 . . . 1)
 TABLE 19
 File Structure - Program Internal Format
 Note : These files in program internal readable format
 Configuration File - SLEET.CFG
 Save Set Values @ Program Shut Down
 Load Set Value @ Program Initalisation
 K acc (mm/s/s/bit)
 K wheel (mm/s/s/bit)
 K light (Lx/bit)
 K temp (mDegC/bit)
 K batt (mV/bit)
 ZeroLight (bit)
 ZeroTemp (bit)
 Hysterysis (Deg)
 Alpha (Deg)
 AlgorithmID
 UserID
 Circ[0] . . . [23] (S)
 FSD (0 . . . 1)
 DisplayHist (min)
 TABLE 20
 Algorithm Data File [ALGO]*.ALG
 F zerox (% S/#/min)
 F rms (% S/Deg)
 F light (% S/Klx)
 F temp (% S/DegC)
 F sleep (% S/Hr)
 F road (% S/m/s/s)
 F trip (% s/Hr)
 Z ref (#/min)
 R ref (Deg)
 I ref (KLx)
 T ref (DegC)
 H ref (Hr)
 G ref (m/s/s)
 Alarm1 (s)
 AIarm2 (s)
 Alarm3 (s)
 AlarmHoldoff (min)
 W limit (Deg)
 TABLE 20
 Algorithm Data File [ALGO]*.ALG
 F zerox (% S/#/min)
 F rms (% S/Deg)
 F light (% S/Klx)
 F temp (% S/DegC)
 F sleep (% S/Hr)
 F road (% S/m/s/s)
 F trip (% s/Hr)
 Z ref (#/min)
 R ref (Deg)
 I ref (KLx)
 T ref (DegC)
 H ref (Hr)
 G ref (m/s/s)
 Alarm1 (s)
 AIarm2 (s)
 Alarm3 (s)
 AlarmHoldoff (min)
 W limit (Deg)
 TABLE 22
 Data File Structure - Drive Mode Data File [XDRIVE]*.CSV
 Note: These files in external readable format - CSV
 DriveID
 File Ceation Date
 Start Time (Hr 0 . . . 23)
 Start Time (min 0 . . . 59)
 UserID
 AlgorithmID
 Alarm1 (s)
 Alarm2 (s)
 Alarm3 (s)
 AlarmHoldOff (min)
 W limit (Deg)
 H (Hr)
 Q (0 . . . 1)
 F zerox (% S/#/min)
 F rms (% S/Deg) Z (#/min)
 F light (% S/kLx) R (Deg)
 F temp (% S/DegC) I (KLx)
 F sleep (% S/Hr) T (DegC)
 F road (% S/m/s/s) G (m/s/s)
 F trip (% S/Hr) D (Hr)
 Z ref (#/min)
 R ref (Deg) S mod (S)
 I ref (Kix) S circ(S)
 T ref (DegC) S zerox (S)
 H ref (Hr) S rms (S)
 G ref (m/s/s) S temp (S)
 Minute Count (min) . . . Repeat 1 . . . N(min) S sleep (S)
 AlarmState S road (S)
 SteeringMode S trip (S)
 Acceleration [1](m/s/s) Wheel[1](Deg)
 DQC (Data Quality
 Code 0 . . . 255)
 Acceleration [50] Wheel[50]
 TABLE 23
 Data File Structure - Learn Mode Data File [XLEARN]*.CSV
 Note : These files in external readable format - CSV
 Data File Structure - User Data File [XUSER]*.CSV
 Note : These files in external readable format - CSV
 UserID
 File Creation Date
 UserName
 UserDoB
 UserSex
 TABLE 24
 Data File Structure - Algorithm Data File [XALGO]*.CSV
 Note : These files in external readable format - CSV
 Algorithm ID
 File Creation Date
 F zerox (% S/#/min)
 F rms (% S/Deg)
 F light (% S/kLx)
 F temp (% S/DegC)
 F sleep (% S/Hr)
 F road (% S/m/s/s)
 F trip (% S/Hr)
 Z ref (#/min)
 R ref (Deg)
 I ref (KLx)
 T ref (DegC)
 H ref (Hr)
 G ref (m/s/s)
 Alarm1 (s)
 AIarm2 (s)
 Alarm3 (s)
 AlarmHoldOff (min)
 W limit (Deg)