U.S. patent application number 13/351857 was filed with the patent office on 2012-07-12 for vigilance monitoring system.
This patent application is currently assigned to Compumedics Limited. Invention is credited to David BURTON.
Application Number | 20120179008 13/351857 |
Document ID | / |
Family ID | 25645979 |
Filed Date | 2012-07-12 |
United States Patent
Application |
20120179008 |
Kind Code |
A1 |
BURTON; David |
July 12, 2012 |
Vigilance Monitoring System
Abstract
The present invention pertains to a system and method of
monitoring the alertness or wakefulness of a driver. The monitored
parameters include cardiac, respiratory and movement parameters.
Sensors are located in various locations of the driver side section
to detect the vigilance of a driver. These sensors include pressure
sensors embedded in the seat and pedals, and a head band for
monitoring EEG, EMG and EOG signals.
Inventors: |
BURTON; David; (Camberwell,
AU) |
Assignee: |
Compumedics Limited
Abbotsford
AU
|
Family ID: |
25645979 |
Appl. No.: |
13/351857 |
Filed: |
January 17, 2012 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
10417247 |
Apr 15, 2003 |
8096946 |
|
|
13351857 |
|
|
|
|
09890324 |
Jul 26, 2001 |
6575902 |
|
|
PCT/AU99/01166 |
Dec 24, 1999 |
|
|
|
10417247 |
|
|
|
|
Current U.S.
Class: |
600/301 |
Current CPC
Class: |
A61B 5/18 20130101; B60L
3/02 20130101; B60T 7/12 20130101; A61M 2021/0083 20130101; A61M
2230/60 20130101; G08B 21/06 20130101; B60T 7/14 20130101; A61M
2230/10 20130101; A61M 21/02 20130101; B60L 2260/46 20130101; B60W
2040/0872 20130101; A61B 5/369 20210101; A61M 2021/0044 20130101;
A61M 2230/14 20130101; A61B 5/398 20210101; B60L 2200/26 20130101;
B60W 40/08 20130101; A61B 3/113 20130101; A61B 5/7232 20130101 |
Class at
Publication: |
600/301 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Foreign Application Data
Date |
Code |
Application Number |
Jan 27, 1999 |
AU |
PP 8325 |
Oct 29, 1999 |
AU |
PQ 3740 |
Claims
1-32. (canceled)
33. A driver vigilance monitor comprising: a memory device
containing driver-specific data; a sensor integrated into a
vehicle's seatbelt for detecting and outputting a value of a
physiological parameter; a sensor for detecting eye movement or
driver head position; and a processor for determining a driver's
vigilance state by comparing the driver-specific data in the memory
device with output received from the sensor integrated into the
vehicle's seatbelt and the sensor for detecting eye movement or
driver head position.
Description
[0001] The present invention relates to a vigilance monitoring
system. In particular the invention relates to a system for
monitoring, recording and/or analysing vigilance, alertness or
wakefulness and/or a stressed state of an operator of equipment or
machinery in a variety of situations including situations wherein
the degree of vigilance of the operator has implications for the
safety or well being of the operator or other persons. A typical
application may include monitoring the driver of a vehicle or pilot
of an aircraft, although the invention also has applications in
areas involving related occupations such as train drivers and
operators of equipment such as cranes and industrial machinery in
general, and where lack of operator vigilance can give rise to
harmful social or economic consequences.
[0002] The system of the present invention will be described herein
with reference to monitoring a driver of a vehicle nevertheless it
is not thereby limited to such applications. For example, other
applications may include monitoring routine, acute or sub-acute
physiological parameters of a person or subject in a home, work,
clinic or hospital environment. The monitored parameters may
include cardiac, respiratory and movement parameters as well as
parameters relating to apnea events, subject sleep states or sudden
death syndrome on-set.
[0003] The monitoring system is designed, inter alia, to provide
non-invasive monitoring of a driver's physiological data including
movement activity, heart activity, respiration and other
physiological functions. The monitored physiological data may
undergo specific analysis processing to assist in determining of
the driver's state of vigilance. The system is designed to detect
various states of the driver's activity and detect certain
conditions of driver fatigue or relaxation state that could lead to
an unsafe driving condition or conditions.
[0004] The system of the present invention includes means for
gathering movement data associated with the driver. The movement
gathering means may include a plurality of sensors such as touch
sensitive mats placed in locations of the vehicle that make contact
with the driver, such as the seat, steering wheel, pedal(s), seat
belt or the like. Each location may include several sensors or mats
to more accurately monitor movements of the driver.
[0005] Signals from the various sensors/mats may be processed and
analysed by a processing means. The processing means may include a
digital computer. The processing means may be programmed to
recognize particular movement signatures or patterns of movement,
driver posture or profile and to interpret these to indicate that
vigilance has deteriorated or is below an acceptable threshold. The
processing means may include one or more algorithms.
[0006] The sensors or mats may include piezoelectric,
electrostatic, piezo ceramic or strain gauge material. The latter
may be manufactured by separating two conductive materials such as
aluminium foil with an electrolyte material which is capable of
passing AC but not DC current. In one form the sensors or mats may
include Capacitive Static Discharge (CSD) or Polyvinylidene
fluoride (PVDF) material. The sensors/mats may be covered with a
non-obtrusive, flexible surface which is capable of detecting
pressure and/or monitoring electrophysiological activity.
[0007] The pressure detecting capability may be used for detecting
driver movement. The or each sensor may produce an output signal
that represents the magnitude of the pressure or force that is
applied to the sensor. The or each pressure signal may thus
represent an absolute or quantitative measure of pressure applied
to the sensor. The electrophysiological activity may include
electrical signals generated by the body of the driver eg.
electrical muscle activity and/or pulse activity.
[0008] The sensors or mats may be located in various parts of a
vehicle. The seat of the driver may be divided into several
sections such as upper or back and lower or seat. The upper or back
section may include sensors in the top edge, centre and base. The
lower or seat section may include sensors in the front edge, centre
and rear. The or each sensor may include CSD or PVDF material.
[0009] The steering wheel may include a plurality of sensors. The
steering wheel may be divided into eight zones such as upper, upper
left, upper right, left, right, lower left, lower right and lower.
At least one sensor may be associated with each zone. The or each
sensor may include CSD or PVDF material.
[0010] The floor covering such as carpet may include a plurality of
sensors. The floor covering or carpet may be divided into a
plurality of zones. At least one sensor may be associated with each
zone. The or each sensor may include CSD or PVDF material.
[0011] The accelerator, clutch and brake pedals may include a
plurality of sensors. Each pedal may be divided into a plurality of
zones such as upper, middle and lower. At least one sensor may be
associated with each zone. The or each sensor may include. CSD,
PVDF or other movement sensitive material.
[0012] The seat belt may include one or a plurality of sensors. In
one form a sensor or sensors may be embedded in the fixed (i.e.
non-retractable) section of the seat belt. The or each sensor may
include CSD or PVDF material.
[0013] In some embodiments a head tilt device incorporating a
positional switch or the like may be associated with the drivers
cap, glasses or goggles or may be arranged to clip over the drivers
ear or glasses. The head tilt device may be adapted to provide a
signal or data which alters in accordance with the position of the
driver's head. Alternatively a radio tracking device may determine
and track a subject's head movements.
[0014] In critical applications of vigilance monitoring including
applications involving pilots of aircraft, persons responsible for
navigating/controlling shipping and drivers of road or rail
transport it may be desirable to utilize more comprehensive methods
of vigilance monitoring. The latter may include techniques used in
conventional sleep monitoring. A head band and/or chin band sensor
may be used to monitor EEG, EMG and EOG signals. The head band
sensor may include separate left and right frontal zones and left
and right eye zones. The sensor may include CSD or PVDF material or
other material sensitive to measuring patient skin electrical
surface variations and/or impedance.
[0015] Various sensors/techniques may be adapted for monitoring eye
movement including those based on reflected light, electric skin
potential, contact lenses, limbus tracking, video imaging and
magnetic induction. The sensors/techniques may include EOG
electrodes, infrared detection of eye movements and/or video
tracking and processing of eye movements. The sensors/techniques
may be adapted for monitoring the left eye only or the right eye
only or both eyes.
[0016] Raw data which is collected from the various sensors
positioned around the vehicle may be filtered and amplified prior
to processing and analysis. A significant purpose of the processing
and analysis is to determine the driver's state of vigilance,
alertness or wakefulness. In some embodiments, the system may be
adapted to effect remedial action, ie. the system may take steps to
alert the driver or to actively intervene in the control of the
vehicle, when it is deemed that such action is warranted or
desirable.
[0017] Processing of data may be performed in several stages,
including primary, secondary and tertiary analysis.
[0018] Primary analysis refers to processing of raw data from the
various sensors. This raw data may be filtered and amplified prior
to analog to digital conversion. Primary analysis may be adapted to
determine valid body movements of the driver as distinct from
spurious signals and artefacts due to environmental factors
including noise.
[0019] Valid body movements may be determined by applying a
combination of processing techniques including: [0020] 1. signal
threshold detection whereby signals below or above a pre-determined
threshold are ignored and/or classified as noise or artefact,
[0021] 2. frequency filtering whereby high-pass, low-pass and notch
filters are adapted to remove noise and artefact signals, [0022] 3.
signal compression whereby data is minimized by presenting main
data points such as signal peaks, troughs, averages and zero
crossings. [0023] 4. half period, amplitude analysis of signals,
including analysis as disclosed in AU Patent 632932 entitled
"Analysis System for Physiological Variables", assigned to the
present applicant, the disclosure of which is incorporated herein
by cross reference.
[0024] Threshold detection may facilitate distinguishing random and
non-significant electrical noise (typically spikes of small
duration) relative to signals representing valid or actual body
movements. Threshold detection may apply to both amplitude and
duration of the signals. The relevant threshold(s) may be
determined from clinical trials and/or historical data. Where the
detection is based on amplitude it may be determined in both
negative and positive phases of the signal. Amplitude detection may
be based on a measurement of the peak-to-peak signal.
Alternatively, the positive and negative peak amplitudes can be
measured separately. Threshold detection may be combined with a
form of zero-base line detection so that electronic offsets do not
adversely affect the accuracy of threshold detections. Each body
movement which exceeds the predetermined amplitude and/or duration
may be classified as an event for further processing.
[0025] Secondary analysis may be adapted to process the results of
primary analysis. Secondary analysis may process data for the
purpose of presentation and/or display. Data may be displayed or
printed in a tabular, graphical or other format which facilitates
interpretation of the data. One purpose of the representation
and/or display is to represent a driver's state of vigilance and/or
fatigue. In one form each event identified during primary analysis
may be counted for a fixed period of time or epoch. The fixed
period of time may be 30 seconds or 60 seconds, or other period
which is adequate for determining body movement trends. The count
value or number of valid body movements in a given period (eg. 30
seconds) may be represented in a display as, say, the length of a
vertical bar.
[0026] Where it is desired to display the energy or power
associated with valid body movements in a particular epoch or time
period, the average amplitude associated with each event may be
indicated by the length of the vertical bar whilst the count value
or number of valid body movements for each epoch may be represented
by colouring the vertical bar. For example the colours green, blue,
yellow, orange, red may indicate count values or movement numbers
in ascending order ie. green indicating the lowest movement number
for a particular epoch and red indicating the highest movement
number for a particular epoch. Alternatively, data may be displayed
on a 3 dimensional graph wherein for example the x dimension of the
graph represents time or epochs, the y dimension represents the
average amplitude, while the z dimension represents the number of
events during a particular epoch. The above display techniques, may
facilitate interpretation of the number of valid body movements and
the amplitude of those movements and association of this data with
the driver's activity or state of vigilance, alertness or
wakefulness.
[0027] It may also be relevant to measure the period of each
individual body movement as this may provide an indication of the
energy that is associated with the movement. For example, if a
driver squeezes the steering wheel in a rapid response as distinct
from gripping the wheel as part of a focussed steering manoeuvre,
the pattern of signals in each case will be different. The rapid
response may appear as a small cluster of movements/signals or as a
single movement/signal with a relatively short duration or period
of time. In contrast, the steering manoeuvre may appear as a larger
cluster of movements/signals over a relatively longer period of
time or as a single movement/signal having a relatively long
duration.
[0028] The type of signal which may be expected (cluster or single
movement/signal) will depend in part upon the type of sensor. For
example, piezo ceramic or PVDF sensors may emit fewer clusters of
signals but may emit signals with larger time periods in relation
to the actual period of the movement which is being monitored. A
capacitive electrostatic sensor is more likely to emit clusters of
"spikes" being relatively short period signals. It may be necessary
to record the energy level of each movement as this energy level
may fall below a certain threshold when the driver is in a fatigued
state. If, for example the driver has relaxed, then the energy of a
body movement in the actions of driving may be significantly more
subdued than in the case where the driver is alert, and his muscle
activity is significantly greater. Therefore it may be useful to
measure and record each and every body movement. This data could be
displayed on high-resolution graphs where for example the X-axis
represents 1/2 second periods and 960 lines make up each continuous
section--or 480 seconds (8 minutes). The largest amplitude signal
in each 1/2 second period could then be displayed on the X-Axis.
The Y-Axis on the other hand could be represented by a scale of
amplitudes representing each body movement. This graph would be
more precise in representing the actual signal level of each
body-movement and the subsequent muscle status for a driver.
[0029] It may also be useful to detect events that are represented
by groups of movements, where, for example, the groups of movements
may be indicative of a driver activity of interest. Detection of
groups of movements may include user configurable or preset values
for; [0030] the maximum time between consecutive body-movements in
order to qualify as being counted as part of a periodic
body-movement. [0031] the number of consecutive body-movements that
are required to qualify for a periodic movement. [0032] the time
period during which this number of body-movements must exist in
order to qualify as a periodic body-movement.
[0033] Periodic measurement analysis can detect, for example,
absence of movements which can be associated with a driver's
fatigue.
[0034] Tertiary analysis may be adapted to process the results of
secondary analysis. One purpose of tertiary analysis is to
determine the status of a drivers state of vigilance, alertness or
wakefulness. Tertiary analysis may process the results of secondary
analysis to produce intermediate data and/or indicate trends in the
data. The intermediate data and trends may be used to provide
summary reports and further tabular and/or graphic representations
of a drivers status or condition. The intermediate data may be
processed by one or more vigilance algorithms to determine the
status of a driver's vigilance, alertness or wakefulness.
Intermediate data of various types may be derived and the vigilance
algorithm(s) may make use of such data to determine the status of
the driver. The intermediate data may include: [0035] Rate of
change of body movement detections [0036] Rate of change of body
movement amplitudes [0037] Area under curve of time versus body
movement, for various sequential epochs to detect trends of subject
movement changes (amplitude or number of movements) [0038]
Correlation of sensor data for patterns of amplitude, energy and
body movement changes that can be associated with driver fatigue
[0039] Change in frequency of body movement signals [0040] Change
in amplitude periods of body movement signals [0041] Change in
phase relationships of body movement signals [0042] Relative phase
relationship between each section and other types of sensor
sections.
[0043] Following tertiary analysis the vigilance algorithm(s) may
be adapted to correlate the intermediate data and/or apply
combinational logic to the data to detect patterns of movement (or
lack thereof) which, based on historical data or clinical trials,
indicates that the driver is or may be excessively relaxed or is
below an acceptable threshold of vigilance, alertness or
wakefulness.
[0044] The vigilance algorithm(s) may incorporate one or more look
up tables including reference movement data and default values
associated with acceptable and unacceptable levels of driver
fatigue. Histograms including movement histograms of the kind
described in AU Patent 632932 based on the work of Rechitschaffen
and Kayles (R & K) may be used as well as tables showing
weighted values and actual movement data for each sensor.
[0045] The vigilance algorithm(s) may determine a vigilance
probability factor (0-100%) as a function of weighted movement data
values.
[0046] Upon detecting a vigilance probability factor which is below
an acceptable threshold, the system may be arranged to intervene in
the control of the vehicle or to alert the driver of the vehicle
and/or other vehicles. Vehicle control intervention may include
restriction of speed, controlled application of brakes, cutting-off
fuel and/or disabling the accelerator pedal. Driver alerting
intervention may include use of sprays designed to stimulate the
driver, vibrating the steering wheel, seat belt or floor area in
the vicinity of the driver, an audible alarm and/or use of bright
cabin lights. The driver can also be alerted by winding down the
driver window and/or other effective alerting methods as may be
applicable to each individual driver. Drivers of other vehicles may
also be alerted by means of flashing hazard lights and/or sounding
of a siren. Vehicle control intervention may be integrated with and
form part of a vehicle control system or it may be interfaced to an
existing vehicle control system. Vehicle control intervention may
be interfaced with GSM or other communication systems to provide
early warning indication that a driver or operator of equipment is
in a stressed, fatigued or other undesirable condition that may be
detected.
[0047] To assist differentiating normal and acceptable driver
vigilance from fatigued or inappropriate driver conditions,
calibration of the various sensor and transducer outputs is
possible. Calibration can set the system's detection parameters in
accordance with varying driver movement and other driver signals.
Calibration is beneficial because driver sensor and signal outputs
will vary with different drivers. Background noise will also vary
with different vehicles. The need for calibration may be
proportional to the critical nature of the driving or dependent on
the level of accuracy required for fatigue monitoring and
detection.
[0048] The need for calibration may to some extent be removed by
utilizing artificial intelligence to distinguish baseline
conditions for a drivers normal wakeful state to facilitate
subsequent analysis and determining when a driver's state indicates
fatigue or lapse of vigilance. Artificial intelligence may be
embodied in one or more automated systems including one or more
mathematical algorithms. Artificial intelligence includes the
systems ability to self-learn or teach itself conditions associated
with the driver which constitute normal or alert driving as
distinct from conditions which constitute abnormal or fatigued
driving.
[0049] Artificial intelligence may allow the driver of a specific
vehicle to select a mode of operation during which the driver's
movements during normal or wakeful driving are monitored and
diagnosed in order to determine typical thresholds and correlations
between various sensors, for the purpose of determining true
fatigue states of the driver as distinct from alert states of the
driver. Artificial intelligence may also facilitate adaptation of
the vigilance algorithm(s), to the specific vehicle's background
noise characteristics.
[0050] Artificial intelligence may include different response
patterns for correlating movement data from the various' sensors
for distinguishing valid driver movements from environmental
vibrations and noise. These may be classified and described by, for
example, a look up table that records expected patterns or
combinations of signals for different cases of environmental noise
as distinct from driver generated signals. For example, if the
driver moves his hand, signals from sensors in the steering wheel
and arm sections of the seat may correlate according to a specific
pattern. Alternatively, if the vehicle undergoes a severe or even
subtle vibration due to road or engine effects, a broader range of
sensors may be similarly affected and this may be manifested as
amplitudes which follow predetermined correlation patterns. Signals
from the sensors may increase in strength or amplitude according to
the proximity of the source of the sound or vibrations. Where the
source of the vibration is localized, this may manifest itself as a
pattern of similar waveforms across the various sensors which
reduce progressively in amplitude as the sensors distance from the
source increases. For example, if the source of the vibration is
road noise, the floor sensors may register maximum amplitude
whereas the steering wheel sensors which are furthest from the road
noise may register minimum amplitude.
[0051] The phase relationship of vibrations from various sources
may also provide some guide as to the likely source of the
vibrations. For example, if the vibrations emanate from the
driver's movement then it is more likely that several signals with
similar phase may be detected. On the other hand, if the signals
have varying phase relationships, then it is more likely that the
source of the vibrations giving rise to these signals is random as
may be expected if the vibrations emanate from the vehicle
environment.
[0052] Similar phase signals arising from driver movements may be
distinguished from similar phase signals arising from artefacts or
the vehicle environment by relating the environmental noise to
sensors located near sources of expected noise in the vehicles, eg.
engine noise, wheel noise, and other vibrations and noise. This may
be detected by carefully locating microphones and vibration sensors
in the vehicle.
[0053] Cancellation of environmental noise can be assisted by
monitoring signals from the microphones and sensors with a view to
applying the most effective signal cancellation techniques in order
to reduce as much as possible the artefact or noise effects or
unwanted signals within the vehicle environment.
[0054] One example of the application of noise cancellation
techniques includes detection of the various road bumps and
ignoring the effect of these bumps on the data being analysed from
the various vehicle sensors of interest.
[0055] Another example of the application of noise cancellation
techniques includes detection of various engine noises and
application of a signal of opposite phase to the motor noise in
order to cancel the artefact. One example of phase cancellation
techniques which may be adopted is disclosed in PCT application
AU97/00275, the disclosure of which is incorporated herein by
cross-reference.
[0056] Other examples of noise cancellation include filtering
wherein highpass, lowpass and notch filters may be used to assist
artefact removal.
[0057] Artificial intelligence may learn to ignore periodic signals
from sensors in the vehicle as these are likely to arise from
mechanical rotations within the vehicle, thus improving the
separation of artefact signals from signals of interest, such as
signals which indicate true driver movement.
[0058] Artificial intelligence may also learn to recognize changes
in the driver's state which reflect changes in driver vigilance or
wakefulness. Points of calculation and analysis of sensor data for
the purpose of comparison and correlation with previously monitored
data may include: [0059] spectral analysis of signals with a range
of consecutive time periods; 1/2 period time amplitude analysis of
signals and other techniques used in conventional sleep analysis as
disclosed in AU Patent 632932; [0060] calculation of the number of
movements per consecutive periods of time, [0061] wherein the
consecutive periods of time may typically be, 1 second or 1/2
second; [0062] calculation of average signal levels during periods
of, say, 20 or 30 seconds; [0063] calculation of total "area under
the curve" or integration of sensor signals for a period of, say,
20 or 30 seconds; [0064] correlation and relationship between
various combinations of input sensor channels; [0065] ECG heart
rate and respiration signals, the latter signals providing an
indication of the driver's wakeful state, as heart-rate and
respiration signals during the sleep state are well documented in a
number of medical journals.
[0066] Artificial intelligence may be applied in conjunction with
pressure sensors in vehicle seats and/or seat belts to control air
bag deployment. In this way air bag deployment may be restricted
for children or validated with different crash conditions for
children and adults. For example, if a child is not detected as
being thrust forward by means of pressure data received from
seat/seat belt sensors, deployment of air bags and possible air bag
injury to the child may be avoided.
[0067] Deployment of air bags may generally be validated more
intelligently by analysing data relating to passenger or driver
posture, movement, thrust, body movement, unique driver or
passenger `yaw` etc.
[0068] The system may include means for testing a driver's response
times. Such tests may, if carried out at regular intervals,
pre-empt serious driving conditions as can be brought about by
driver fatigue or a lapse in vigilance. The testing means may be
adapted to provide a simple method for prompting the driver and for
testing the driver's response time. The response test may, for
example, request the driver to respond to a series of prompts.
These prompts may include requesting the driver to squeeze left or
right hand sections of the steering wheel or squeeze with both left
and right hands at the same time in response to a prompt. The means
for prompting the driver may include, for example, LEDs located on
the dash of the vehicle or other position that the driver is
visually aware of. A left LED blinking may for example, prompt the
driver to squeeze the left hand on the steering wheel. A right LED
blinking may prompt the driver to squeeze the right hand on the
steering wheel. The centre LED blinking may prompt the driver to
squeeze both hands on the steering wheel. Alternatively two LEDs
could be used in the above example, except that both LEDs blinking
may prompt the driver to squeeze with both hands.
[0069] The drivers response or level of alertness may be detected
by measuring the response time of the driver, where the response
time is measured as the time between illumination of an LED and a
correct response with the hand or hands. In a case where an
inappropriate response time is detected (potentially signalling
driver fatigue or onset of driver fatigue) the system can verify
the results and alert the driver. The system may also determine the
accuracy of the driver's responses to ascertain the status of the
driver's vigilance.
[0070] A further example of means for testing the driver's response
may include means for flashing random numbers on the windscreen.
The driver may be prompted to respond by squeezing the steering
wheel a number of times as determined by the number flashed. The
numbers may be flashed on the screen at different locations
relative to the steering wheel with the position of the hands on
the wheel responding to the side of the screen where the flashes
were detected. This type of test should be conducted only when the
driver is not turning, changing gear, braking or performing other
critical driving functions.
[0071] It is desirable to ensure that the driver response tests are
not anticipated by the driver to more accurately detect the
driver's state of vigilance. It is of course also important that
the selected method of testing driver response, does not in any way
distract the driver or contribute to the driver's lapse in
concentration.
[0072] The system may be built into a vehicle sun visor as a visual
touch screen display allowing a comprehensive visualisation of a
drivers activity. The touch screen may include a color display for
displaying movement/pressure outputs associated with each sensor. A
display of the status of a plurality of sensors may provide a
visual indication of a relaxed versus an active driver state.
[0073] According to one aspect of the present invention, there is
provided apparatus for determining a vigilance state of a subject
such as a driver of a vehicle or the like, said apparatus
including:
[0074] means for monitoring one or more physiological variables
associated with said subject;
[0075] means for deriving from said one or more variables data
representing physiological states of said subject corresponding to
the or each variable; and
[0076] means for determining from said data when the vigilance
state of said subject is below a predetermined threshold.
[0077] According to a further aspect of the present invention,
there is provided a method for determining a vigilance state of a
subject such as a driver of a vehicle or the like, said method
including the steps of:
[0078] monitoring one or more physiological variables associated
with said subject;
[0079] deriving from said one or more physiological variables data
representing physiological states of said subject corresponding to
the or each variable; and
[0080] determining from said data when the vigilance state of said
subject is below a predetermined threshold.
[0081] Preferred embodiments of the present invention will now be
described with reference to the accompanying drawings
wherein:--
[0082] FIG. 1 shows a block diagram of a vigilance monitoring
system according to the present invention;
[0083] FIG. 2 shows a flow diagram of an algorithm for processing
data from sensors associated with a vehicle and driver;
[0084] FIG. 3A shows a simplified block diagram of a system for
cancelling environmental noise from driver interfaced sensors;
[0085] FIG. 3B shows waveforms associated with the system of FIG.
3A;
[0086] FIG. 4A shows a flow diagram of a movement processing
algorithm according to the present invention;
[0087] FIG. 4B shows examples of data reduction and syntactic
signal processing associated with a sample signal waveform;
[0088] FIG. 5 shows sample outputs of data following secondary
analysis by the system of FIG. 4A;
[0089] FIG. 6 shows an embodiment of steering wheels sensors;
[0090] FIG. 7 shows a block diagram of a vigilance monitoring
system utilizing video data;
[0091] FIG. 8 shows a flow diagram of an algorithm suitable for
processing video data;
[0092] FIGS. 9 and 10 show examples of data produced by the system
of FIGS. 7 and 8;
[0093] FIG. 11 is a flow diagram of the main vigilance processing
algorithm;
[0094] FIG. 12 is a block diagram of a vehicle monitoring system
according to the present invention;
[0095] FIG. 13 shows one form of transducer for monitoring posture
of a driver or equipment operator;
[0096] FIG. 14 shows a block diagram of an embodiment of an anti
snooze device according to the present invention;
[0097] FIG. 15 shows a calibrate mode algorithm; and
[0098] FIG. 16 shows a main relax detection algorithm.
[0099] Referring to FIG. 1, block 12 shows a plurality of sensors 1
to 11 associated with a vehicle and driver. The or each sensor may
include piezoelectric or electrostatic material such as CSD or PVDF
material. The material can be divided into plural sections of the
driver's seat, for example. The various sensors are summarized
below.
1. Upper Driver Seat Sensor
[0100] Drivers seat top edge of upper section [0101] Drivers seat
centre of upper section [0102] Drivers seat base of upper
section
2. Lower Driver Seat Sensor
[0102] [0103] Drivers seat front edge of lower section [0104]
Drivers seat centre of lower section [0105] Drivers seat rear of
lower section
3. Driver Seat-Belt Sensor
[0105] [0106] Driver's seat-belt upper section [0107] Driver's
seat-belt lower section
4. Driver's Head Tilt Per Driver Cap or Similar Sensor
[0108] The driver's head tilt per driver cap or a device to clip
over drivers ear or as part of driving goggles or glasses. These
sensors can be, for example, positional switch devices. The output
from these positional devices is amplified, filtered and finally
data acquisitioned and analysed. This sensor device is designed to
output a signal or digital data which changes state in accordance
with the tilt of the driver's head. By calibration of the system in
accordance with normal driving conditions this output can correlate
the normal driving condition with the fatigued driver
condition.
5. Driver Headband Sensor
[0109] The driver headband sensors can be, for example, a
Capacitive Static Discharge Material (CSDM) or PVD Material (PVDM)
that can be divided into the various sections (as listed below) of
the driver's headband sensor. The output from the various sensors
is amplified, filtered and finally data acquisitioned and analysed.
The headband material can contain conductive sections designed to
pick-up the patient's electro-encephalograph (EEG) signals.
Driver Headband Sensor
[0110] Driver headband left frontal [0111] Driver headband right
frontal [0112] Driver headband left eye [0113] Driver headband
right eye EEG, EMG and'EOG parameters monitored in critical driving
conditions. In some critical applications of vigilance monitoring,
such as pilots of aircraft, personnel responsible for navigating
and controlling ships, drivers of road or rail transport or
passenger vehicles, it can be appropriate to apply more
comprehensive methods of vigilance monitoring. These more
comprehensive monitoring techniques can include techniques for
analysing the frequency composition of a subjects EEG physiological
data. Half Period Amplitude analysis (AU patent 632932) or spectral
analysis can be applied in order to determine if the subject is
entering a trance or non-vigilant state or if the subject is
becoming drowsy. This type of sleep staging can be derived in real
time to facilitate determination of the subject's state of
vigilance. If the subject is detected as being in a risk category
the present system will alert the driver in order to prevent a
potential vehicle accident due to the driver's lapse in
concentration. One method of electrode attachment, but not limited
to, could be the application of a headband by the driver where this
head-band and/or chin-band could connect the EEG, EMG and EOG
signals to the monitoring device for purpose of analysing the
signals for determination of the subjects state of wakefulness.
6. Driver Eye Sensor
[0114] Various techniques can be applied for the purpose of eye
movement monitoring including; [0115] Techniques based on reflected
light. [0116] Techniques based on electric skin potential. [0117]
Techniques based on Contact lenses [0118] Techniques based on
Limbus tracking [0119] Techniques based on video imaging [0120]
Techniques based on Magnetic Induction [0121] Driving goggles or
glasses with infra-red detection capability for monitoring driver's
eye movements, or EOG signal pick up via electrodes.
Driver Eye Detection Sensor Types;
[0121] [0122] Driver's eyes left [0123] Driver's eyes right [0124]
sources of eye movements can include EOG electrodes, Infrared
detection of eye movements, or video tracking and processing of eye
movements.
7. Driver Steering Wheel Sensor
[0125] The driver steering wheel or other steering device sensors
can be, for example, a CSDM or PVD material that can be divided
into the various sections (as listed below) of the driver's
steering wheel or other steering device. The output from the
various sensors is amplified, filtered, and finally data
acquisitioned and analysed.
Driver Steering Wheel Sensor Types;
[0126] Drivers steering wheel top left section [0127] Drivers
steering wheel top right section [0128] Drivers steering wheel
bottom left section [0129] Drivers steering wheel bottom right
section
[0130] An alternative form of steering wheel sensor is shown in
FIG. 6.
8. Driver Carpet Region Sensor
[0131] The driver carpet sensors can be, for example, a Capacitive
Static Discharge Material (CSDM) or PVD Material (PVDM) that can be
divided into the various sections (as listed below) of the driver's
carpet area. The output from the various sensors is amplified,
filtered and finally data acquisitioned and analysed.
9. Driver Accelerator Sensor
[0132] The driver accelerator sensors can be, for example, a
Capacitive Static Discharge Material (CSDM) or PVD Material (PVDM)
that can be divided into the various sections (as listed below) of
the accelerator pedal. The output from the various sensors is
amplified, filtered and finally data acquisitioned and
analysed.
Driver Accelerator Pedal Sensor Types;
[0133] Drivers accelerator pedal top section [0134] Drivers
accelerator pedal center section [0135] Drivers accelerator pedal
bottom section 10. Driver Clutch Pedal (where Applicable) Sensor
The driver clutch sensors can be, for example, a Capacitive Static
Discharge Material (CSDM) or PVD Material (PVDM) that can be
divided into the various sections (as listed below) of the driver's
clutch pedal (where applicable). The output from the various
sensors is amplified, filtered and finally data acquisitioned and
analysed.
Driver Clutch Pedal Sensor Types;
[0135] [0136] Drivers clutch pedal (if applicable) top section;
[0137] Drivers clutch pedal (if applicable) center section [0138]
Drivers clutch pedal (if applicable) bottom section
11. Driver Brake Pedal Sensor
[0139] The driver brake sensors can be, for example, a Capacitive
Static Discharge Material (CSDM) or PVD Material (PVDM) that can be
divided into the various sections (as listed below) of the brake
pedal. The output from the various sensors is amplified, filtered
and finally data acquisitioned and analysed.
Brake Pedal Sensor Types;
[0140] Drivers brake pedal top section [0141] Drivers brake pedal
center section [0142] Drivers brake pedal bottom section
[0143] Other sensors are referred to in block 13, including
steering wheel movement and direction sensors and sensors for
detecting environmental noise and vibrations.
[0144] The outputs from the various sensors are amplified and
filtered in block 14 in preparation for analog to digital
conversion in block 15. The sensor signals are input in digital
form to block 16. Block 16 includes a central processing unit and
one or more algorithms for processing the digital signals. Block 16
also makes use of the vigilance processing algorithm(s) in block
17. The vigilance processing algorithm(s) in block 17 are adapted
to determine the status of the driver state of vigilance, alertness
or wakefulness. This status may be expressed as a vigilance factor
(0-100%). Upon detecting a vigilance factor which is below an
acceptable threshold, the central processing unit may alert the
driver of the vehicle and/or other vehicles. The driver alert means
may include:
To Alert External Drivers;
[0145] Flashing Hazard Lights [0146] Sounding of siren
Internal Vehicle Driver Alert Systems;
[0146] [0147] Scent sprays which are designed to activate the
drivers vigilance state [0148] Vibration modulation for driver--can
include vibration of steering wheel or floor area to alert driver
[0149] Vibration modulation for driver seat-belt [0150] Vibration
modulation for driver steering wheel [0151] Audible alarm system at
frequencies and durations or sequence of durations as tested be
most effective in alerting the driver [0152] Cabin bright lights
designed to avoid driving hazard but tested for improving driver
vigilance
[0153] Upon detecting a vigilance factor which is below an
acceptable threshold, the central processing unit may intervene in
the control of the vehicle. Vehicle intervention may enable the
vehicle to be brought into a safe or safer status. Vehicle
intervention may include speed restriction or reduction or complete
removal of fuel supply. In some circumstances the accelerator pedal
may need to be disabled, for example when a driver has his foot
depressed on the accelerator pedal and is in an unsafe or fatigued
state.
Where a driver is detected as ignoring or not responding to
response requests or appropriate acknowledgement that the driver is
in a vigilant state, the vehicle may have its horn or hazard
flashing lights activated to warn other drivers, and/or have its
fuel injection de-activated, and/or speed reduced by gentle and
controlled safe braking. Where a driver is detected as suffering
from fatigue and is not responding to response tests, the vehicle
may have its fuel supply reduced, and/or its speed reduced by
gentle and controlled safe braking, to a safe cruising speed. The
driver may then be prompted again before the vehicle undergoes
further intervention.
[0154] Another option for vehicle intervention is to provide a form
of ignition override, as used in some alcohol based systems. In
this type of system the vehicle ignition or starting process may be
inhibited by an inappropriate driver state which in the present
case may be drowsiness or excessive fatigue.
[0155] In many modern vehicles vehicle intervention options may be
instigated by an onboard computer or electronic interface eg. by
communication with the speed controller or fuel injection logic.
The computer system, may include intelligence to arbitrate the most
appropriate intervention sequence or process to minimize risk to
the vehicle driver or its passengers.
[0156] FIG. 2 shows a flow diagram of an algorithm for processing
data from sensors associated with the vehicle and driver. Block 20
shows a plurality of arrows on the left representing data inputs
from various sensors associated with a vehicle, following
conversion to digital data. The digital data is input to block 21
which determines whether the data conforms to valid amplitude
thresholds stored in block 22. Signals beyond the thresholds are
classified as noise or artefact and are ignored. The data is then
input to block 23 which detects whether the data conforms to valid
time duration thresholds stored in block 24. Signals beyond the
thresholds are classified as invalid and are ignored. The
thresholds stored in blocks 22 and 24 are, for the purpose of the
present embodiment, determined empirically from experimental
trials. The data is then input to block 25 for signal compression.
The role of block 25 is to simplify further processing by
presenting the data in a minimized form. This is done by syntactic
processing whereby main data points only of the signals such as
various peaks, troughs and zero crossings or central points
defining peaks of the signals are presented for further processing.
The data is then input to block 26 where it is categorized and
summarized in terms of amplitude or power range, number of
movements per second or other epoch, and phase relationships
between the signals. The data may be displayed on tabular or
graphical form and/or may be subjected to further automated
processing to determine vigilance status.
[0157] FIG. 3A shows a block diagram of a system for removing
environmental noise from driver interfaced sensors. Block 30
represents various sensors for monitoring driver movements and
block 31 represents sensors for monitoring environmental vibration
and noise and vehicle artefacts.
[0158] Blocks 32 and 33 represent circuits for amplifying and
filtering signals from blocks 30 and 31 respectively. Block 34
represents analogue to digital converters for converting the
signals from blocks 32 and 33 into digital form for processing via
the digital signal processor in block 35. Block 35 includes an
algorithm for performing signal cancellation as illustrated in FIG.
3B.
[0159] In FIG. 3B waveform A represents a signal from a driver
interfaced sensor or sensors (Block 30 of FIG. 3A). Waveform B
represents a signal from a sensor or sensors associated with the
vehicle engine and road noise pickup locations (Block 31 of FIG.
3A). Waveform C represents a signal after it is processed by Block
35. It may be seen that the signal represented by waveform C is
obtained by cancelling or subtracting the signal represented by
waveform B from the signal represented by waveform A. The signal
represented by waveform C is a true or valid movement signal which
is not corrupted by environmental noise.
[0160] FIG. 4A shows a flow diagram of a movement processing
algorithm according to the present invention. Referring to FIG. 4A,
signals from sensors 1 to 11 shown in block 12 of FIG. 1 are
filtered, then referenced to period and amplitude threshold values
before being converted to syntactic data. The syntactic data is
correlated for determination of certain combinations of sensor
movement signals indicating that the driver is in a vigilant or
wakeful state. When a sensor signal or any combination of sensor
signals are analysed as being void of subject movement, this may be
interpreted as an indication the driver is suspected of being in a
non-vigilant or fatigued state. Analysis of the fatigued state is
determined by certain expected patterns from the various sensor
signals. Such patterns include very little movement from the
steering Wheel and very little movement from the seat sensors,
indicating that the driver may be excessively relaxed and subject
to fatigue, or at risk of fatigue on-set. The functions of blocks
40 to 61 are as follows:
Block 40
[0161] FIG. 1 shows how the analog signals from sensors 1 to 11
are: converted to a digital signal (FIG. 1, block 15); input to the
central processing unit (FIG. 1, block 16); and processed by a
vigilance processing algorithm (FIG. 1, block 17). The start of the
algorithm in FIG. 4A represents the start of a process, which is
repeated many times for each input sensor 1 to 11 (FIG. 4A shows
the process for sensors 1, 2, 3). This process analyses data from
each input sensor for the purpose of final determination of the
driver's vigilance state, and whether this state warrants an alarm
alert in order to assist in preventing a potential accident.
Blocks 41 to 46
[0162] Signal A/D Data Output. The analog signal from each sensor
is amplified, filtered and then converted to a digital signal in
preparation for signal processing.
Variables A,C,E-U
[0163] Variables A,C,E,-U provide to the processing algorithms
threshold amplitude and period values to allow sensor signal data
reductions to be determined and to allow data reduction and
syntactic signal processing. The variables (A,C,E-U) are determined
via controlled studies from experimental and research data. FIG. 4B
shows examples of: (1) signals components which are ignored due to
being below a minimum amplitude threshold, (2) syntactic data where
the signal is represented by troughs and peaks of the signal, and
(3) high frequency component being ignored due to being below a
minimum period threshold. The latter recognizes relatively lower
frequencies which are typically due to driver movements. Inputs
from Sensors 4 to 11, Subject to System Configuration Input from
each of the vehicles sensors is amplified, filtered and then analog
to digital converted, in preparation for signal processing. This is
performed by blocks similar to blocks 41 to 46. Inputs from more
than 11 sensors can be catered for if required.
Block 47
A,C,E,-U
[0164] Variable data via default table (as determined by clinical
data and/or neuro node self learning and adjustment), resulting
from customisation to specific subject's driving characteristics
and system adaptation. Variables: B,D,F,-V. By comparing the sensor
data to various amplitude thresholds and pulse periods, it is
possible to ignore data that is likely to be noise or artefact and
include data that is distinguishable as movement data from a
driver. The movement data is distinguished by measuring the
amplitude and period characteristics of the sensor signal. Movement
data is also distinguished by comparing signal patterns and
characteristics of sensors to patterns and characteristics of
typical driver's movements (as determined by comparative data used
for correlating against current data, this data being derived from
system self-learning and/or calibration processes.)
Block 48
[0165] Is peak to peak amplitude of sensor output greater than
threshold variable A ? Retain time reference and value of each
signal excursion of input sensor exceeding amplitude reference
A.
Block 49
[0166] Is peak to peak amplitude of sensor output greater than
threshold variable C ? Retain time reference and value of each
signal excursion of input sensor exceeding amplitude reference
C.
Block 50
[0167] Is peak to peak amplitude of sensor output greater than
threshold variable E ? Retain time reference and value of each
signal excursion of input sensor exceeding amplitude reference
E.
Block 51
[0168] Is peak to peak amplitude of sensor output greater than
threshold variable B ? Retain time reference and value of each
signal excursion of input sensor exceeding amplitude reference
B.
Block 52
[0169] Is peak to peak amplitude of sensor output greater than
threshold variable D ? Retain time reference and value of each
signal excursion of input sensor exceeding amplitude reference
D.
Block 53
[0170] Is peak to peak amplitude of sensor output greater than
threshold variable F ? Retain time reference and value of each
signal excursion of input sensor exceeding amplitude reference F.
Storage of Input Sensors Period and Amplitude with Time Reference
The syntactic data from the full range of sensors is stored in
random access memory for the purpose of processing and
determination of a subject's vigilant state. The syntactic data is
also archived to allow post analysis report and validation or
review of driver fatigue and performance. This can be particularly
useful where truck drivers and other critical transport or
passenger drivers are required to be checked for performance and
vigilance compliance.
Block 54
[0171] Longer-term data storage is designed to log the driver's
movement data from each of the sensors. This stored data can be
accessed at a later stage in order to review the driver's
performance history in regards to movement analysis and subsequent
vigilance.
Block 55
[0172] Short term direct access storage used for storing parameters
such as the past 10 minutes of syntactic data for each sensor
channel, in order to correlate the various data from each sensor or
channel and compare this data combination to pre-defined sets of
rules designed to describe combinations of sensor outputs which are
typical of driver fatigue conditions.
Block 56
[0173] Store syntactic representation of sensor signal exceeding
threshold A and 8, with timer reference, amplitude and pulse
width.
Block 57
[0174] Store syntactic representation of sensor signal exceeding
threshold C and D, with timer reference, amplitude and pulse
width.
Block 58
[0175] Store syntactic representation of sensor signal exceeding
threshold E and F, with timer reference, amplitude and pulse
width.
Block 59
[0176] Driver specific profile and calibration data can be stored
for later correlation reference. By correlating with various
thresholds or reference conditions the system is able to determine
interaction to sensors when a particular driver's conditions is
similar to pre-stored reference characteristics. This comparative
data is stored as data in look up tables. The data can consist of
frequency and/or amplitude characteristics for a range of driver
states or alternatively the data can consist of samples of data
(with acceptable variations to the samples of data) that exist for
a range of driver states.
Block 60
[0177] Vehicle output signals. These include steering wheel
movements, direction of steering wheel movements, speed of vehicle,
change of speed of vehicle, engine vibration and noise, road
vibration and noise. By processing driver steering wheel
adjustments and comparing these adjustments with the various sensor
signals and correlation of various sensor signals, it is possible
to determine the probability that the driver is in a state of
fatigue and the degree of driver fatigue. The vehicle signals are
also analysed in order to assist in noise cancellation (ie vehicle
noise as opposed to driver movement) and more accurate
identification of valid driver movements).
Block 61
[0178] Correlate all channels of sensor activity and determine if
driver fatigue is a probability and what level of driver fatigue is
detected. Look up table of specific driver calibration values and
reference states is used to determine actual driver state and level
of fatigue of driver, along with probability of data accuracy.
Standard reference data tables and default values are also used for
determination of driver fatigue. See sample R&K style
histograms, movement histograms and tables showing weighted value
of each sensor and actual movement detection from each sensor to
determine fatigue probability as a function of movement detection
with appropriate weighting.
[0179] FIG. 5 shows typical samples of processed data following
secondary analysis for sensor signals 1 to 4. The data shows in
graphical form the number of valid movements detected for each
sensors 1 to 4 during successive time intervals n, n+1, n+2 . . . .
Tertiary analysis may be performed on this data which would allow
simple to view correlation between the various sensors. The samples
shown in FIG. 5 demonstrate an example (dotted line) where the
various sensors all experience obvious movement detection.
[0180] The steering wheel sensors shown in FIG. 6 are divided into
eight sections as follows:
[0181] Top 62, top left 63, top right 64, left 65, right 66, bottom
left 67, bottom right 68 and bottom 69.
[0182] Sensors 62-69 are linked via eight cables to output pins 1
to 8 respectively. A common connection to each sensor is linked by
cables to output pin 9. Alternative configurations are possible
with more or less sensors and with the option of sensor arrays on
both the upper and lower surfaces of the steering wheel grip
surface. The outputs represented by pins 1 to 9 are connected to
analogue signal conditioning circuits and via analogue to digital
convertors to digital signal processing circuits as described
above.
[0183] It is desirable to measure pressure of a driver's hand or
hands on the steering wheel at all times. The pressure may be
compared to previous values and/or calibrated values to determine
whether a pattern of increased or decreased pressure reflects
driver fatigue onset.
[0184] If the driver's state of consciousness or concentration
changes due to fatigue onset or the like, the system may calculate
and deduce an appropriate point at which the driver should be
alerted. The appropriate point may be determined from a combination
of pre-calibrated data for a specific driver and/or pre-programmed
patterns, states or trends in the data including relative and
absolute pressure values obtained from a set or subset of vehicle
sensors.
[0185] FIG. 7 shows a block diagram of a vigilance monitoring
system utilizing video data. Block 70 represents a video CCD
(charge coupled device) camera which may be located on the drivers
visor, dash-board or other suitable location to enable video
monitoring of the driver's eyes. An infra-red lens may be utilized
to facilitate reliable night video monitoring capability. The
output of the video camera is passed to block 71. Block 71 is an
analog to digital converter for digitizing the video signal prior
to processing via block 72. Block 72 is a central processing unit
and includes a video processing algorithm. The video processing
algorithm has eye recognition software designed to identify eyes in
contrast to other parts of the drivers face. Eyes are detected
using special processing software that allows the driver's eyes to
be analysed. This analysis includes determining the area of the
eye's opening and correlating the eye's opening area to previous
similar measurements. In this way eye processing can determine
whether a driver's eyes are remaining open as would be expected in
an alert state or whether the current eye opening of the driver is
relatively less (when compared to earlier eye opening
measurements). Rates or degrees of eye closure are able to be
detected and continually monitored in this manner.
The video processing algorithm also detects blink rate and possibly
eye movements to determine whether the drivers eyes appear to be
alert or possibly fixed in a dangerous "trance state" as may be
apparent during lapses of driver vigilance. Block 73 represents
outputs of block 72 including [0186] eyes blink rate [0187] eyes
closure, calculated as a percentage ratio of current eyes open area
to previously calculated maximal eyes open area. [0188] eyes focus
factor, determined by measuring number of eye movements per second,
extent of eye movements (ie small eye movements or larger eye
movement deflections) [0189] the nature of eye movements can
reflect appropriate patterns of movement of a driver's eyes such as
focus on sections of the road for an appropriate time as well as
inappropriate patterns of movement associated with fatigue or lack
of vigilance [0190] type of eye movements, ie vertical, horizontal,
stare The above measures may be gauged against actual trials in
order to determine relevant indices that correlate to a driver's
fatigued state.
[0191] FIG. 8 shows a flow diagram of an algorithm suitable for
processing video data. The functions of blocks 80 to 94 are as
follows:
Block 80
START
Block 81
[0192] CAPTURE EYE VIDEO DATA--Capture current video frame.
Digitise video frame of subject's eyes. Eye data can be captured
via one or more of the following means: CCD video camera,
Electro-oculogram data capture means via subject worn headband,
direct electrode attachment, driver glasses, head-cap or movement
sensors, infrared or other light beam detection means.
Block 82
Apply Eye Data Processing and Determine Left & Right Eye
Opening Area and Blink Events.
[0193] Apply edge detection, signal contrast variation and shape
recognition, amongst other processing techniques to determine the
border of the subject's eye lids. Determine area of each of the
subject's eye openings, height of each eye opening, blink events
for each eye, blink rate and time reference associated with each
blink event.
Block 83
Correlate Current and Past Video Captured Eye Movement Data
[0194] Correlate current eye position data with previous position
eye data. Review eye position trend data and determine trends and
patterns of eye movements that indicate on-set of or driver fatigue
state. Patterns include: [0195] states of staring or trance like
states indicating loss of road concentration. [0196] slowly rolling
eye movements (typical of sleep onset). [0197] eye focus directions
and association of these directions with driver fatigue Process
digitised video frame and detect subject's left and right eye
movement patterns and activity of eyes and association of this
activity with driver fatigue. Compare current blink rates, past
blink rates and look-up table blink rate characteristics,
thresholds for various fatigue on-set and fatigue blink rates and
blink characteristics associated with various driver states.
Compare current eye opening area with thresholds for fatigue and
fatigue on-set conditions to determine vigilant driver eye opening
status versus fatigued driver eye opening status.
Block 84
[0198] Look Up Table with Characteristic Patterns of; [0199] eye
movements and threshold data for fatigued versus vigilant subjects.
[0200] Blink rate typical thresholds and characteristics [0201] Eye
opening typical and default thresholds [0202] Eye movement typical
and default characteristics for driver fatigue on-set.
Block 85
[0203] Store subject's left & right eye opening area, eye
opening height, blink rates, eye position and eye movements
together with time reference.
Block 86
[0204] Calibration data derived from subject and vehicle
calibration procedures. [0205] determination of fatigue on-set
blink rates thresholds. [0206] Determination of eye opening fatigue
on-set thresholds. [0207] Determination of eye position, movement
characteristics and activity characteristics for fatigue on-set
thresholds. [0208] EOG patterns for wake, drive activity, fatigue
on-set, fatigue. [0209] Trance and hypnotic EOG eye
characteristics.
Block 87
[0210] Fatigue threshold time period variable X set from default
values, subject calibration or system
self-learning/calculation.
Block 88
[0211] Is mean of eye opening area below "fatigue mean eye opening
threshold X "?
Block 89
[0212] Fatigue threshold time period variable Y set from default
values, subject calibration or system
self-learning/calculation.
Block 90
[0213] Is time duration below mean eye opening fatigue threshold
(X) greater than Y ?
Block 91
[0214] Blink rate fatigue characteristics set from default values,
subject calibration or system self-learning/calculation.
Block 92
[0215] Does blink rate and characteristics comply with fatigue
blink rate characteristics ?
Block 93
[0216] Apply eye data processing and determine left & right
opening area and blink events. Correlate current and past video
captured eye movement data. Detection of fatigue eye opening on-set
and detection of fatigue blink rate on-set.
Block 94
[0217] Eye movement fatigue determination diagram. FIGS. 9 and 10
show examples of eye opening and eye position data produced by the
system of FIGS. 7 and 8. FIG. 11 is a flow chart of the main
vigilance processing algorithm. The functions of blocks 95 to 99
are as follows:
Block 95
Main Vigilance Processing Algorithm
[0218] Vigilance Movement Processing Algorithm. (see FIG. 4A)
[0219] Vigilance Eye Status Processing Algorithm. Probability of
Driver Fatigue and Degree of Vigilance Determination Algorithm
(correlates subject Movement Status and Eye Processing Status).
Block 96
[0220] LED indicator display panel.
Block 97
[0221] Eye Status Vigilance factor 0-100%.
Block 98
Movement Vigilance Factor
[0222] 0-100%--displayed as bar graph, meter or other means.
Block 99
[0223] Vigilance probability Factor 0-100%
[0224] FIG. 12 is a block diagram of a vehicle monitoring system
according to the present invention. FIG. 12 is an overview of a
system which utilizes many of the features discussed herein. The
functions of blocks 100 to 118 are as follows:
Block 100
[0225] Driver EEG sensors--direct attach electrode, headband,
wireless electrode, driver cap and other EEG signal pick-up
means.
Block 101
[0226] Driver EEG sensors--direct attach electrode, headband,
wireless electrode, driver cap and other EEG signal pickup
means.
Block 102
[0227] Driver Motion, Movement and Physiological Parameter
sensors.
Block 103
[0228] Driver Eye movement Detection via electrode, driver
glasses/goggles, infrared or other light beam means of tracking
detection or other means.
Block 104
[0229] Vehicle status interface; speed, direction, accelerator
position, break position, indicators, lights amongst other vehicle
status data.
Block 105
[0230] In phase signal detection and processing. Applies processing
which determines patterns of in-phase signal occurrence and
associates these with driver or background noise as originating
source.
Block 106
[0231] Anti-phase signal detection and processing. Applies
processing which determines patterns of anti-phase signal
occurrence and associates these with driver or background noise as
originating source.
Block 107
Vehicle Background Noise Processing Algorithm.
[0232] Vehicle background and Environmental Noise Sensors to allow
noise cancellation, filtering and reduction. These sensors include
microphone and vibration sensors located at strategic positions in
order to pick up background vehicle noise such as road noise and
engine noise. Fourier transform and frequency analysis of
background noise assists in selection of digital filtering
characteristics to most effectively minimise vehicle environmental
noise and assist in distinguishing driver related fatigue
monitoring signals. System will continually "self-learn" various
vehicle background and threshold noise levels, frequency and other
characteristics in order to determine changing vehicle noise
conditions and subsequent noise cancellation or capability to
ignore unwanted vehicle noise while processing "real" driver
movement and physiological signals and subsequent fatigue status.
Artificial intelligence; Signal characteristics as generated by a
range of varying road conditions can be programmed into the system.
The input data relating to various road conditions thereby provides
a means to further distinguish wanted driver related signals from
unwanted background noise signals.
Block 108
[0233] Driver EEG sensors--direct attach electrode, Algorithm
Block 109
[0234] Driver EEG sensors--direct attach electrode, algorithm
Block 110
[0235] Driver Motion, Movement, Physiology algorithm
Block 111
Driver Eye Movement Detection Algorithm
Block 112
[0236] Vehicle status interface Algorithm
Block 113
[0237] Driver Fatigue Processing Algorithm. Correlation with
previous driver fatigue conditions together with comparison of
outputs for each of above listed fatigue algorithms (Driver EEG,
motion, eye, vehicle status).
Block 114
[0238] Driver vigilance interactive response testing.
Block 115
[0239] Driver alert and alarm systems for re-instatement of
vigilance.
Block 116
[0240] Driver vehicle car intervention to reduce or limit speed and
other means of increasing vehicle safety and reducing vulnerability
to driver fatigue status.
Block 117
[0241] Vehicle fatigue display systems for displaying to the driver
the current fatigue status or early warning indicators of fatigue
status.
Block 118
[0242] System communication storage and printing peripheral
interface. Data storage, reporting processing, reporting print
interface, wireless and wire connected interfaces, for real-time or
post communication of fatigue data and fatigue status information.
System can include GSM, cellular phone, satellite or other means of
moving vehicle tracking and data exchange in real-time or at any
required later stage. This information transfer can be an effective
means for trucks and other vehicles to have their driver status
processed and reviewed, as appropriate and as required.
[0243] FIG. 13 shows one form of transducer for monitoring posture
of a driver or equipment operator. FIG. 13 shows a webbed structure
comprising strips or elements of flexible PVDF or Piezo material
separated by flexible insulation material terminated at A, B, C, D,
E, F, G and H. Output signals from the respective strips are
buffered, amplified, filtered and then analog to digital converted
to data. This data may be processed to determine an actual position
of pressure applied to the above structure. By analysing the two
main co-ordinates and the amplitudes of signals associated with
those co-ordinates, the exact position of pressure applied by the
vehicle driver or equipment operator may be determined.
[0244] The position where greatest pressure is applied is defined
by the intersection of web strip pairs (eg. Band F) which produce
the greatest signal amplitude. The position may be described by
coordinates reflecting the web strip pairs (eg. B,F) which produce
the greatest signal amplitude. The above transducer may be used in
conjunction with the movement sensors described herein to provide a
further layer of positional information relating to applied
pressure for each sensor. This information may be important in
circumstances where a driver's pressure to the steering wheel or
the driver's pattern of hand placement (with respective applied
pressure) varies in accordance with alertness and drowsiness.
[0245] The posture of the driver or equipment operator may be
monitored, stored, correlated with various threshold states and/or
displayed in meaningful graphic or numerical form. The threshold
states may be derived by way of calibration for each specific
driver's posture profile under various states of fatigue and/or
stress states and conditions.
[0246] The anti-snooze device shown in FIG. 14 includes sensors
(block 120) connected to an acquisition and processing means (block
121). Block 122 includes monitoring means designed to amplify,
filter and digital to analog convert driver sensor signals in
preparation for digital signal processing. The digital signal
processing means (block 121) includes a calibration algorithm as
shown in FIG. 15 and a main relax detection algorithm as shown in
FIG. 16.
[0247] The driver can select the relax calibration function, then
take on the driving posture that would most closely represents a
relaxed or possibly fatigued driving state and the system will then
monitor and store the minimum threshold of driver activity over a
period of approximately but not limited to 10 seconds, as a relaxed
driver reference level.
[0248] The driver can select an active calibration function, then
take on the driving posture that would most closely represents
normal driving state and the system will then monitor and store the
minimum threshold of driver activity over a period of approximately
but not limited to 10 seconds, as an active driver reference
level.
[0249] The relaxed and active driver reference levels stored in the
system may be displayed on the visual touch screen display for
various sensors. The system may perform a validation function by
replaying the drivers relaxed and active reference levels on the
touch screen. This allows easy comparison to be made with actual
sensor levels when the driver adopts postures representing
normal/fatigued states and serves to validate the correctness of
the stored reference levels.
[0250] The driver can also select a sensitivity function which may
determine how close to the relaxed level the driver needs to be
before the anti-snooze system alerts the driver. By viewing the
anti-snooze device screen the driver can relax or adopt normal
vigilant driving posture and adjust sensitivity control so that the
anti-snooze device appears to track and detect the drivers relaxed
state. The anti-snooze device has the ability to act as a self
warning aid by simply alerting the driver when his posture or
driving vigilance is deteriorating. If, for example, a drivers
steering wheel grip erodes or undergoes fatigue, the anti-snooze
system can be calibrated to detect this condition and alert the
driver.
[0251] It is possible for the driver to have calibration data
determined by an off-road simulator that more accurately defines
the characteristics of each specific drivers activity variations
and physiological variations during dangerously relaxed or fatigued
driving conditions. The calibration data can be up-loaded to the
anti-snooze device to provide more accurate relaxed and active
reference levels. The calibration data may also provide more
accurate means of determining the relative effect that each
individual sensor has during a drivers transition from active and
alert to drowsy and fatigued. The effects of each sensor may be
recorded and this data may assist in more accurate anti-snooze
detection.
[0252] During calibration modes the system may detect the drivers
hand pressures via the steering wheel sensors, the drivers
respiration and ECG via the seatbelt sensors, and the drivers
posture and movement via the seat sensors.
[0253] The anti-snooze system may continually monitor and average
the signal amplitudes of all sensors, while comparing the current
levels of sensor amplitude with the calibrated levels. The system
may also compare current movement sensor patterns to reference
data. This reference data can represent certain threshold levels
calibrated to each individual driver or general reference
conditions. The various sensors may be weighted in accordance with
their respective importance in determining whether a driver's
current state of activity is below the threshold or appropriately
close to the relaxed mode calibrated reference level to warrant
that the driver be alerted.
[0254] If the driver is detected as being within the range of
sensor amplitudes and activity to warrant being alerted, the
anti-snooze device can restrict the speed of the vehicle or slowly
bring the vehicle to a stand still in order to reduce the
likelihood of an accident. This ability to restrict the vehicle's
speed could be overridden by the driver as is possible in
"auto-cruise" devices currently available on many vehicles.
[0255] The techniques and methodologies may include relatively
complex neurological waveform analysis techniques, video tracking
of driver eye motions, sophisticated noise cancellation and simpler
driver interactive processes such as sensitizing the steering
wheel, seat-belt, gear-stick and other driver cabin regions.
[0256] One application for the present invention may include a
truck driver vigilance monitoring (TDVM) system. This system may be
designed around the "dead-man" handle concept as applied
successfully in trains. A variation of this system may provide
visual cues and driver vigilance response testing.
[0257] The TDVM system may include pre-programmed Light Emitting
Diode (LED) displays to be activated in various sequences and at
various frequencies and durations. The truck driver can be visually
prompted by way of these LEDS to press the steering wheel according
to whether the left or right or both LEDS are flashed. The response
time and accuracy of the driver's response to the prompts may be
measured and relayed back to a remote monitoring control
station.
[0258] Various drivers will have calibrated "vigilant response
times and accuracy levels" which can be compared to actual current
response times. Where appropriate, an alarm can be activated, if
the response times indicate fatigue on-set or a potentially
dangerous state.
[0259] The sequences and durations can be validated in accordance
with clinical trials to provide an effective method of vigilance
detection. Sequences and patterns of visual truck cabin prompts can
be established to minimize driver conditioning. Frequency of
vigilance test prompts can be determined in accordance with
requirements as determined via field studies.
[0260] Safety considerations to avoid driver distraction by the
proposed monitoring system may be implemented. Techniques such as
utilization of "busy" response prompts especially designed within
the system to alert the monitoring control unit that the driver is
vigilant but unable to respond at the time due to driving
demands.
[0261] The TDVM system may include the following components:
1. Analysis Software This software may include a processing
algorithm(s) designed to evaluate various driver prompts and
response times. Evaluation of these response times may produce a
probability factor associated with driver vigilance for each
specific driver. Analysis capability of driver response times may
be an important element of the system. Accuracy of vigilance
probability outcome, clinical analysis and scientific validation
associated with this process may determine effectiveness of the
monitoring system.
2. Truck-Cabin Steering-Wheel Physiological Movement
Transducer.
[0262] This device may adapt to the truck steering wheel and
provide output signals subject to a particular zone of the steering
wheel, which has been activated by applying various degrees of
pressure to the steering wheel.
3. Controller Unit & Monitoring Device (CU&MD).
[0263] This device may provide a communication link and data
management for interfacing the truck's CU&MD to a remotely
located monitoring station. This device may also provide the
transducer interface and transducer signal recording and detection
capabilities. This device may also output control to the driver
indicator LEDS and record and transmit vigilance response times to
the remote monitoring station.
4. Vigilance LED Display.
[0264] This device may be interfaced to the CU&MD unit and may
provide visual response prompt to the truck driver.
5. Remote Recording, Monitoring and Analysis System.
[0265] This system may facilitate a remote operators visual alarms
when vigilance response times are outside acceptable thresholds.
This system may also provide communication links to the truck. This
system may also provide analysis and system reporting to allow
real-time tracking of vigilance performance and vigilance alarm
status.
[0266] Finally, it is to be understood that various alterations,
modifications and/or additions may be introduced into the
constructions and arrangements of parts previously described
without departing from the spirit or ambit of the invention.
* * * * *