Patent Description:
Any discussion of the background art throughout the specification should in no way be considered as an admission that such art is widely known or forms part of common general knowledge in the field.

It is known to analyse neurological conditions from analysis of eye and/or eyelid movements. For example, <CIT> teaches a method and apparatus for measuring drowsiness based on the amplitude to velocity ratio for eyelids closing and opening during blinking as well as measuring duration of opening and closing. This enables an objective measurement of drowsiness. <CIT> discloses another system for detecting drowsiness based on eyelid movements.

The present inventors, through their research into relationships between eye and eyelid movement parameters and neurological conditions, have identified opportunities for probabilistic prediction and/or detection of additional neurological conditions via analysis of eyelid movement parameters.

It is an object of the present invention to overcome or ameliorate at least one of the disadvantages of the prior art, or to provide a useful alternative.

One embodiment provides a system configured to facilitate collection of blepharometric data from one or more subjects on a periodic basis thereby to enable extended time period analysis of subject neurological conditions, the system including:.

One embodiment provides a system wherein the sensor device is an image capture device.

One embodiment provides a system wherein the system includes an image processing system that is configured to: (i) detect presence of a human face; (ii) identify one or more eye regions in the human face; and (iii) based on identification of the one or more eye regions, generate blepharometric data representative of eyelid position against time.

One embodiment provides a system wherein the subject identification module, which is configured to identify a unique human subject from which a set of blepharometric data is collected by the sensor device, leverages a facial recognition process thereby to extract biometric facial information from one or more frames of image data collected via the image capture device.

One embodiment provides a system wherein the subject identification module is configured to identify a unique human subject from which a set of blepharometric data is collected by the sensor device via collection of biometric data.

One embodiment provides a system wherein the biometric data includes facial data.

One embodiment provides a system the subject identification module is configured to identify a unique human subject from which a set of blepharometric data is collected by the sensor device via user input of identifying credentials.

One embodiment provides a system wherein the subject identification module is configured to identify a unique human subject from which a set of blepharometric data is collected by the sensor device via communication with a user mobile device which includes a token representative of identifying credentials.

One embodiment provides a system wherein defining current blepharometric data for the human subject includes processing blepharometric data for a period or sub-period of continuous blepharometric data collection via the sensor device, thereby to extract a set of blepharometric data artefacts.

One embodiment provides a system wherein the blepharometric data artefacts include any one or more of the following:.

One embodiment provides a system wherein the memory module that is configured to maintain a record of historical blepharometric data for the identified human subject includes statistical information derived from processing of blepharometric data collected across a plurality of previous periods.

One embodiment provides a system wherein the blepharometric data collected across a plurality of previous periods is collected via a plurality of physically distinct collection systems.

One embodiment provides a system wherein the blepharometric data variation processing module is configured to identify a relationship between the current blepharometric data for the human subject and the record of historical blepharometric data for the identified human subject by processing methods including one or more of the following:.

One embodiment provides a system wherein identifying a relationship between the current blepharometric data for the human subject and the record of historical blepharometric data for the identified human subject thereby to identify a long-term trend in blepharometric data includes determining whether, in response to a current set of blepharometric data, there is an identified threshold trend in one or more of the user's observed blepharometric artefacts that satisfies a predefined profile that is representative of prediction of a neurological condition.

One embodiment provides a system wherein identifying a relationship between the current blepharometric data for the human subject and the record of historical blepharometric data for the identified human subject thereby to identify a threshold current point-in-time deviation from historical statistical data includes determining whether, the current set of blepharometric data alone or in combination with one or more recent sets of blepharometric data, display a threshold deviation in one or more of the user's observed blepharometric artefacts compared to historical averages, wherein that deviation is representative of prediction of a neurological condition.

One embodiment provides a system wherein the output module is configured to cause delivery of an output signal via in in-vehicle display.

One embodiment provides a system wherein the output module is configured to cause delivery of an output signal via an electronic message sent over a network.

One embodiment provides a system the vehicle is an automobile, and wherein the sensor device is mounted on or adjacent a dashboard or windscreen region.

One embodiment provides a system including multiple sensor devices each mounted in the vehicle positioned to enable monitoring eyelid movement by a respective passenger or operator of the vehicle.

One embodiment provides a system including the blepharometric data monitoring system.

One embodiment provides a device configured to facilitate collection of blepharometric data from one or more subjects on a periodic basis thereby to enable extended time period analysis of subject neurological conditions, the device including:.

One embodiment provides a device wherein the sensor device is an image capture device.

One embodiment provides a device wherein the device includes an image processing system that is configured to: (i) detect presence of a human face; (ii) identify one or more eye regions in the human face; and (iii) based on identification of the one or more eye regions, generate blepharometric data representative of eyelid position against time.

One embodiment provides a device wherein the subject identification module, which is configured to identify a unique human subject from which a set of blepharometric data is collected by the sensor device, leverages a facial recognition process thereby to extract biometric facial information from one or more frames of image data collected via the image capture device.

One embodiment provides a device wherein the subject identification module is configured to identify a unique human subject from which a set of blepharometric data is collected by the sensor device via collection of biometric data.

One embodiment provides a device wherein the biometric data includes facial data.

One embodiment provides a device wherein the subject identification module is configured to identify a unique human subject from which a set of blepharometric data is collected by the sensor device via user input of identifying credentials.

One embodiment provides a device wherein the subject identification module is configured to identify a unique human subject from which a set of blepharometric data is collected by the sensor device via communication with a user mobile device which includes a token representative of identifying credentials.

One embodiment provides a device wherein defining current blepharometric data for the human subject includes processing blepharometric data for a period or sub-period of continuous blepharometric data collection via the sensor device, thereby to extract a set of blepharometric data artefacts.

One embodiment provides a device the blepharometric data artefacts include any one or more of the following:.

One embodiment provides a device wherein the memory module that is configured to maintain a record of historical blepharometric data for the identified human subject includes statistical information derived from processing of blepharometric data collected across a plurality of previous periods.

One embodiment provides a device wherein the blepharometric data collected across a plurality of previous periods is collected via a plurality of physically distinct collection systems.

One embodiment provides a device wherein.

One embodiment provides a device wherein identifying a threshold trend includes identifying a threshold trend in one or more of the user's observed blepharometric artefacts that satisfies a predefined profile that is representative of prediction of a neurological condition.

One embodiment provides a device wherein identifying point-in-time statistical deviation includes determining whether, the current set of blepharometric data alone or in combination with one or more recent sets of blepharometric data, display a threshold deviation in one or more of the user's observed blepharometric artefacts compared to historical averages, wherein that deviation is representative of prediction of a neurological condition.

One embodiment provides a device wherein the output module is configured to cause delivery of an output signal via in in-vehicle display.

One embodiment provides a device wherein the output module is configured to cause delivery of an output signal via an electronic message sent over a network.

One embodiment provides a device wherein the vehicle is an automobile, and wherein the sensor device is mounted on or adjacent a dashboard or windscreen region.

One embodiment provides a device including multiple sensor devices each mounted in the vehicle positioned to enable monitoring eyelid movement by a respective passenger or operator of the vehicle.

One embodiment provides a device including the blepharometric data monitoring system.

One embodiment provides a system configured to facilitate analysis of subject neurological conditions, the system including:.

One embodiment provides a system wherein, for at least one of the sensor systems, the sensor system includes the sensor device including an image capture device that is configured to monitor blepharometric data.

One embodiment provides a system wherein the subject identification module is configured to identify a unique human subject from which a set of blepharometric data is collected by the sensor device via user input of identifying credentials.

One embodiment provides a system including a module configured to determine point-in-time statistical variations between the current blepharometric data for the human subject and the record of historical blepharometric data for the identified human subject.

One embodiment provides a system wherein identifying a threshold trend includes identifying a threshold trend in one or more of the user's observed blepharometric artefacts that satisfies a predefined profile that is representative of prediction of a neurological condition.

One embodiment provides a system wherein identifying point-in-time statistical deviation includes determining whether, the current set of blepharometric data alone or in combination with one or more recent sets of blepharometric data, display a threshold deviation in one or more of the user's observed blepharometric artefacts compared to historical averages, wherein that deviation is representative of prediction of a neurological condition.

One embodiment provides a system wherein the plurality of sensor systems include a selection of the following:.

One embodiment provides a system wherein the plurality of sensor systems include a plurality of in-vehicle blepharometric data monitoring systems.

One embodiment provides a system wherein, for at least a subset of the in-vehicle blepharometric data monitoring systems, the vehicle is an automobile, and wherein the sensor device is mounted on or adjacent a dashboard or windscreen region.

One embodiment provides a system wherein the system includes a cloud-based processing facility.

One embodiment provides a system configured to facilitate monitoring of subject neurological conditions, the system including:.

One embodiment provides a portable electronic device including:.

One embodiment provides a device wherein the first software application is a messaging application.

One embodiment provides a device wherein the first software application is a social media application.

One embodiment provides computer executable code that when executed causes delivery via a computing device of a messaging software application, wherein the computer executable code is additionally configured to collect data from a front-facing camera thereby to facilitate analysis of blepharometric data.

One embodiment provides computer executable code that when executed causes delivery via a computing device of a social media software application, wherein the computer executable code is additionally configured to collect data from a front-facing camera thereby to facilitate analysis of blepharometric data.

One embodiment provides computer executable code that when executed causes delivery via a computing device of a software application with which a user interacts for a purpose other than blepharometric data-based data collection, wherein the computer executable code is additionally configured to collect data from a front-facing camera thereby to facilitate analysis of blepharometric data.

The embodiments mentioned herein do not limit the scope of the invention, which is limited only by the appended claims.

As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

As used herein, the term "exemplary" is used in the sense of providing examples, as opposed to indicating quality. That is, an "exemplary embodiment" is an embodiment provided as an example, as opposed to necessarily being an embodiment of exemplary quality.

The present disclosure relates, in various embodiments, to extended monitoring and analysis of subject neurological factors via blepharometric data collection, for example including devices and processing systems configured to enable such extended monitoring. This may include hardware and software components deployed at subject locations (for example in-vehicle monitoring systems, portable device monitoring systems, and so on), and cloud-based hardware and software (for example cloud-based blepharometric data processing systems.

A human subject's involuntary blinks and eyelid movements are influenced by a range of factors, including the subject's behavioural state and brain function. For example, this has been used in the past for detection of drowsiness. More broadly, analysis of data derived from eye and eyelid movements can be performed thereby to identify data artefacts, patterns and the like, and these are reflective of the subject's behavioural state, brain function and the like.

The technology described herein is focussed on collection and analysis of "blepharometric data", with the term "blepharon" describing a human eyelid. The term "blepharometric data" is used to define data that describes eyelid movement as a function of time. For example, eyelid position may be recorded as an amplitude. Eyelid movements are commonly categorised as "blinks" or "partial blinks". The term "blepharometric data" is used to distinguish technology described herein from other technologies which detect the presence of blinks for various purposes. The technology herein is focussed on analysing eyelid movement as a function of time, typically measured as an amplitude. This data may be used to infer the presence of what would traditionally be termed "blinks", however it is attributes of "events" and other parameters identifiable in eyelid movements which are of primary interest to technologies described herein. Events and other parameters which are identified from the processing of blepharometric data are referred to as "blepharometric artefacts". These are referred to as "blepharometric artefacts", with such artefacts being identifiable by application of various processing algorithms to a data set that described eyelid position as a function of time (i.e. blepharometric data). For example, the artefacts may include:.

The determination of blepharometric artefacts may include any one or more of:.

Known eyelid movement monitoring systems (also referred to herein as blepharometric data monitoring systems) focus on point-in-time subject analysis. For example, commonly such technology is used as a means for assessing subject alertness/drowsiness at a specific moment, potentially benchmarked against known data for a demographically relevant population. There is a problem in that, for many neurological conditions, point-in-time assessment is inadequate. For example, many neurological conditions are degenerative and/or progressive, and for those and others point-in-time blepharometric data without historical baselines may be of limited usefulness. Currently, however, there is no practical way in which to collect blepharometric data for people, outside of requiring people subject themselves to specialist testing (which is expensive and for a bulk of the population likely unfeasible).

A solution proposed herein is to deploy blepharometric data collection systems in a range of human environments, being environments in which humans are commonly positioned suitably for blepharometric data collection. Examples considered herein are vehicles (for example cars, aeroplanes, trains, and the like), computing devices (for example smartphones, tablets, and PCs), and other locations. This allows long term blepharometric data collection on an individualised basis, allowing for better management of neurological health (and other factors such as safety). For instance, specific use cases might include providing warnings in advance of seizures, informing a person of a risk of a degenerative brain illness, detection of brain injuries from accidents and/or sporting activities, and personalised detection of unusual levels of drowsiness.

In terms of behavioural state, there are many factors that have an effect on involuntary eyelid movements, with examples including: a subject's state of physical activity; a subject's posture; other aspects of a subject's positional state; subject movement; subject activity; how well slept the subject happens to be; levels of intoxication and/or impairment; and others. In terms of brain function, factors that have effects on involuntary eyelid movements include degenerative brain injuries (e.g. Parkinson's disease) and traumatic brain injuries.

<FIG> illustrates a high-level methodology which is relevant to a range of embodiments discussed below. This methodology, depending on the specific hardware implementation used by a given embodiment, is optionally performed via software modules executing across a plurality of connected devices, for example including local devices (for example computing devices housed in a vehicle and/or user's mobile devices such as smartphones) and Internet-connected server devices (also referred to as "cloud" components). It should be appreciated that any computing devices and computer-executed methods configured for the purposes of enabling the overall performance of a methodology based on those described below by reference to <FIG> form embodiments of inventions for the purposes of this specification.

Block <NUM> represents a process including collecting data representative of eyelid movement (i.e. blepharometric data). For the majority of embodiments described below, this is achieved via a camera system having an image capture component that is positioned into a capture zone in which a subject's face is predicted to be positioned. For example, this may include:.

The data that is captured is not limited to data captured for the purposes of extended monitoring and analysis of subject neurological factors via blepharometric data collection. For example, in some embodiments that is one purpose, and there is an alternate purpose, which is optionally point-in-time based. For example, point-in-time drowsiness detection is relevant in many of the above scenarios. Furthermore, whilst embodiments below focus on individualized blepharometric data collection and/or monitoring, collected blepharometric data is optionally additionally collected for the purposes of group monitoring/analysis (including where blepharometric data is anonymised such that it is not attributable to a specific individual). For example, this may be used in the context of seating arrangements to assess overall student/viewer attention/drowsiness, or in the context of aeroplanes and other mass transport to perform analysis of passenger health factors.

Block <NUM> represents a process including identifying a subject from whom the blepharometric data collected at block <NUM> originates. This optionally includes:.

Identification of the subject is relevant for the purposes of comparing current blepharometric data with historical blepharometric data for the same subject. For example, in some embodiments an analysis system has access to a database of historical blepharometric data for one subject (for example where the system is installed in a vehicle and monitors only a primary vehicle owner/driver) or multiple subjects (for example a vehicle configured to monitor multiple subjects, or a cloud-hosted system which received blepharometric data from a plurality of networked systems, as described further below).

Block <NUM> represents a process including determination of blepharometric artefacts for a current time period. For example, the artefacts may include:.

The "current period" may be either a current period defined by a current user interaction with a blepharometric data monitoring system, or a subset of that period. For instance, in the context of a vehicle, the "current period" is in one example defined as a total period of time for which a user operates the vehicle and has blepharometric data monitored, and in another embodiment is a subset of that time. In some embodiments multiple "current periods" are defined, for example using time block samples of between two and fifteen minutes (which are optionally overlapping), thereby to compare blepharometric data activity during periods of varying lengths (which may be relevant for differing neurological conditions, which, in some cases, present themselves based on changes in blepharometric data over a given period of time).

The current blepharometric data may be used for point-in-time neurological conditional analysis, for example analysis of subject alertness/drowsiness, prediction of seizures, detection of seizures, and other such forms of analysis. Specific approaches for analysing blepharometric data thereby to detect/predict particular neurological conditions fall beyond the scope of the present disclosure.

Block <NUM> represents a process including identification of relationships between current blepharometric artefacts and historical blepharometric artefacts. This allows for artefacts extracted in the current blepharometric data to be given context relative to baselines/trends already observed for that subject. The concept of "identification of relationships" should be afforded a broad interpretation to include at least the following:.

In relation to onset of a neurological illness or degenerative condition, this may include either or both of short term onsets (e.g. onset of neurological diseases and neurological condition such as strokes and/or seizures and long term onsets (for example long term detection rather than the short term is more appropriate for examples such as Alzheimer's, Parkinson's, Multiple Sclerosis, and Muscular Dystrophy).

Block <NUM> represents a process including identification of presence of one or more blepharometric variation indicators, for example based on the identification of relationships at block <NUM>. These indicators may be used to allow data-based determination/prediction of the presence of: (i) onset of a neurological illness or degenerative condition; (ii) presence of a brain injury, including a traumatic brain injury; (iii) impairment by alcohol, drugs, or other physical condition; (iv) abnormal levels of drowsiness; (v) neurotoxicity or (vi) other factors. By way of example, rules are defined that associate a data relationship (for example deviation from baseline values, a trend identification, or the like) with a prediction on neurological condition. These may be defined, for example, using logical structures, such as.

It should be appreciated that these are examples only, and that the present disclosure is directed to hardware and software that enables the implementation of such analysis/alert processes, as opposed to those processes themselves.

Bock <NUM> represents a process including providing output to the human subject based on identified blepharometric variation indicators. This may include an instruction/suggestion to avoid a particular activity (such as driving), an instruction/suggestion to undertake a particular activity (such as medication, resting, walking around, or the like), or a suggestion to consult a medical expert about a potential neurological condition. The manner by which the output is delivered varies depending on both the nature of the alert/condition, and the hardware environment in place. Examples range from the sending of emails or other messages or the display of information on a local device (for example an in-vehicle display).

Various hardware/software embodiments configured to enable the above methodology are described below.

<FIG> illustrates an example in-vehicle blepharometric data monitoring system. Whilst it is known to provide a blepharometric data monitoring system in a vehicle for the purposes of point-in-time analysis of alertness/drowsiness, the system of <FIG> provides for substantial advances in ability to perform analysis of a user's neurological condition by way of providing a memory module that stores historical blepharometric data, and enables analysis of changes in blepharometric data for the user over time.

The system of <FIG> includes an image capture device <NUM>. This may include substantially any form of appropriately sized digital camera, preferably a digital camera with a frame rate of over <NUM> frames per second. Higher frame rate cameras are preferred, given that with enhanced frame rate comes an ability to obtain higher resolution data for eyelid movement.

Device <NUM> is positioned to capture a facial region of a subject. Device <NUM> is in one embodiment installed in a region of a vehicle in the form of an automobile, for example on or adjacent the dashboard, windscreen, or visor, such that it is configured to capture a facial region of a driver. In another embodiment device <NUM> is positioned on or adjacent the dashboard, windscreen, or visor, such that it is configured to capture a facial region of a front seat passenger. In another embodiment device <NUM> is positioned in a region such as the rear of a seat such that it is configured to capture a facial region of a back-seat passenger. In some embodiments a combination of these are provided, thereby to enable blepharometric data monitoring for both a driver and one or more passengers.

Although the system of <FIG> (and other systems) are described by reference to a vehicle in the form of an automobile, it will be appreciated that a system as described is also optionally implanted in other forms of vehicles, including mass-transport vehicles such as passenger airplanes, busses/coaches, and trains. In such embodiments there are preferably one or more analysis systems each supporting a plurality of image capture devices, each positioned to capture a respective passenger.

An in-vehicle image processing system <NUM> is configured to receive image data from image capture device <NUM> (or multiple devices <NUM>), and process that data thereby to generate blepharometric data. A control module <NUM> is configured to control device <NUM>, operation of image data processing, and management of generated data. This includes controlling operation of image data processing algorithms, which are configured to:.

Algorithms <NUM> optionally operate to extract additional artefacts from blepharometric data, for example amplitude-velocity ratios, blink total durations, inter-event durations, and the like. It will be appreciated, however, that extraction of such artefacts may occur in downstream processing.

A blepharometric data management module <NUM> is configured to coordinate storage of blepharometric data generated by algorithms <NUM> in user blepharometric data <NUM>. This includes determining a user record against which blepharometric data is to be recorded (in some cases there is only a single user record, for example where blepharometric data s collected only from a primary driver of an automobile). In some embodiments the function of module <NUM> includes determining whether a set of generated blepharometric data meets threshold data quality requirements for storage, for example based on factors including a threshold unbroken time period for which eyelid tracking is achieved and blepharometric data is generated.

Memory system <NUM> includes user identification data <NUM> for one or more users. As noted, in some embodiments system <NUM> is configured to collect and analyse blepharometric data for only a single user (for instance the primary driver of a vehicle) and includes identification data to enable identification of only that user. In other embodiments, system <NUM> includes functionality to collect and analyse blepharometric data for multiple users, and includes identification data to enable identification of any of those users (and optionally, as noted above, defining of a new record for a previously unknown user). The identification data may include login credentials (for example a user ID and/or password) which are inputted via an input device. Alternately, the identification data may be biometric, for example using facial recognition as discussed above or an alternate biometric input (such as a fingerprint scanner). In some embodiments this leverages an existing biometric identification system of the vehicle.

User blepharometric data <NUM> includes data associated with identified users, the data basing time coded thereby to enable identification of a date/time at which data was collected. The blepharometric data stored in data <NUM> optionally includes blepharometric data generated by algorithms <NUM> and further blepharometric data derived from further processing of that data, for example data representing average periodic IEDs and/or BTDs, and other relevant statistics which may be determined over time. In some embodiments data processing algorithms are updated over time, for example to allow analysis of additional biomarkers determined to be representative of neurological conditions which require extraction of particular artefacts from blepharometric data.

Analysis modules <NUM> are configured to perform analysis of user blepharometric data <NUM>. This includes executing a process including identification of relationships between current blepharometric artefacts (e.g. data recently received from system <NUM>) and historical blepharometric artefacts (e.g. older data pre-existing in memory system <NUM>). This allows for artefacts extracted in the current blepharometric data to be given context relative to baselines/trends already observed for that subject. The concept of "identification of relationships" should be afforded a broad interpretation to include at least the following:.

Analysis modules are optionally updated over time (for example via firmware updates or the like) thereby to allow for analysis of additional blepharometric artefacts and hence identification of neurological conditions. For example, when a new method for processing blepharometric data thereby to predict a neurological condition based on a change trend in one or more blepharometric artefacts, an analysis algorithm for that method is preferably deployed across a plurality of systems such as system <NUM> via a firmware update or the like.

System <NUM> additionally includes a communication system <NUM>, which is configured to communicate information from system <NUM> to human users. This may include internal communication modules <NUM> which provide output data via components installed in the vehicle, for example an in-car display, warning lights, and so on. External communication modules <NUM> are also optionally present, for example to enable communication of data from system <NUM> to user devices (for example via Bluetooth, WiFi, or other network interfaces), optionally by email or other messaging protocols. In this regard, communication system <NUM> is configured to communicate results of analysis by analysis modules <NUM>.

A control system <NUM> included logic modules <NUM> which control overall operation of system <NUM>. This includes execution of logical rules thereby to determine communications to be provide din response to outputs from analysis modules <NUM>. For example, this may include:.

It will be appreciated that these are examples only, and logic modules <NUM> are able to provide a wide range of functionalities thereby to cause system <NUM> to act based on determinations by analysis modules <NUM>.

It should be appreciated that the system illustrated in <FIG> provides technology whereby one or more digital camera is able to be installed in a vehicle, such as an automobile or mass transport vehicle, thereby to: (i) collect blepharometric data for an operator and/or one or more passengers; and (ii) enable determination of relationships between blepharometric data collected in a "current" period (for example a last data set, a last day, a last week, or a last month) with historical blepharometric data that is stored for that same user. This allows for functionalities including, but not limited to:.

<FIG> illustrates a further embodiment, which includes various common features with the embodiment illustrated in <FIG>. On general terms, in some embodiments, external communication modules <NUM> facilitate communication with a remote server device, which optionally performs additional blepharometric data analysis. In the example of <FIG>, external communication modules <NUM> enable communication between system <NUM> and a cloud-based blepharometric data analysis system <NUM>.

System <NUM> includes a control system <NUM> and logic modules <NUM> which are provided by computer executable code executing across one or more computing devices thereby to control and deliver functionalities of system <NUM>.

System <NUM> additionally includes a memory system <NUM>, which includes user identification data <NUM> and user blepharometric data <NUM>. The interplay between memory system <NUM> and memory system <NUM> varies between embodiments, with examples discussed below:.

System <NUM> additionally includes analysis modules <NUM>, which optionally perform a similar role a modules <NUM> in <FIG>. In some embodiments local and cloud analysis modules operate in a complementary factor, for example with analysis modules <NUM> performing relationship analysis relevant to point-in-time factors (for example an altered/non-standard neurological state for a user by comparison with historical baselines, which warrants immediate intervention) and analysis modules <NUM> performing what is often more complex analysis of trends over time (which may be representative of degenerative neurological illnesses and the like) and do not require local immediate intervention in a vehicle. It will be appreciated that there exist a range of approaches for sharing processing (and memory storage) functions between an in-vehicle system and a cloud system, and configuration of thee is optionally determined based on considerations such as network speeds/bandwidth, along with local memory and storage resource availability.

There are various advantages of incorporating a cloud-based system to operate with a plurality of in-vehicle systems, in particular an ability to maintain cloud storage of user identification data and user blepharometric data for a large number of users, and hence allow that data to "follow" the users between various vehicles over time. For example, a user may have a personal car with a system <NUM>, and subsequently obtain a rental car whilst travelling with its own system <NUM>, and as a result of cloud system <NUM> the rental car system: has access to the user's historical blepharometric data; is able to perform relationship analysis of the current data collected therein against historical data obtained from the cloud system; and feed into the cloud system the new blepharometric data collected to further enhance the user's historical data store.

<FIG> illustrates a further variation where a user has a smartphone device <NUM> that executes a software application configured to communicate with a given local in-vehicle system <NUM> (for example via Bluetooth or USB connection) and additionally with cloud system <NUM> (for example via a wireless cellular network, WiFi connection, or the like). This provides functionality for communication between system <NUM> and system <NUM> without needing to provide Internet connectivity to a vehicle (the in-vehicle system essentially uses smartphone <NUM> as a network device).

Using a smartphone device as an intermediary between system <NUM> and system <NUM> is in some embodiments implemented in a matter that provides additional technical benefits. For example:.

The use of smartphone <NUM> is also in some cases useful in terms of allowing users to retain individual control over their blepharometric data, with blepharometric data not being stored by an in-vehicle system in preference to being stored on the user's smartphone.

<FIG> illustrates a further variation in which communication between a local system <NUM> and cloud system <NUM> operates in a similar manner to <FIG>, but where a smartphone <NUM> is still present. In such arrangements, the smartphone is optionally used as an output device for information derived from blepharometric data analysis, and/or as a device to confirm identify and approval for blepharometric data collection. For example, in one embodiment a given system <NUM> identifies a user by way of biometric information (e.g. facial detection) using user identification data stored in system <NUM> of cloud system <NUM>, and a message is sent to smartphone <NUM> allowing the user to confirm that they are indeed in the location of the relevant system <NUM>, and providing an option to consent to blepharometric data monitoring.

A system such as that of <FIG> is also able to be integrated into other local systems thereby to provide control instructions to those systems in response to artefacts identified in blepharometric data. An example is provided in <FIG>, wherein an aircraft <NUM> an in-vehicle blepharometric data analysis system, which is fed data from image capture devices including devices installed in seat-backs (for example in a common housing to a seat-back display screen). System <NUM> is configured to feed data thereby to effect control instructions into an entertainment system <NUM> and a passenger health/comfort analysis system <NUM>.

In this example, each image capture device is provided in conjunction with a display screen that is configured to deliver audio-visual entertainment (for instance as is common in aeroplanes), monitoring of subject blepharometric data may be used to provide an enhanced experience with respect to audio-visual data. This may include, for example:.

It will be appreciated that provision of a system that enables collection and analysis of blepharometric data from multiple passengers in a mass-transit vehicle may have additional far-reaching advantages in terms of optimising passenger health and/or comfort during transportation.

In mass-transport embodiments, there is preferably a clear distinction between personalising heath data, which is maintained with privacy on behalf of the user, and non-personalising statistical data, which may be shared with other systems/people. For instance, an individual's neurological conditions are not made available to airline personnel, however data representative of drowsiness/alertness statistics in a cabin are made available to airline personnel.

<FIG> illustrates an exemplary framework under which a cloud based blepharometric data analysis system <NUM> operates in conjunction with a plurality of disparate blepharometric data monitoring systems <NUM>-<NUM>. Each of these systems is in communication with system <NUM>, such that user data (for example user blepharometric data comprising historical data) is able to be utilised for analysis even where a user's blepharometric data is collected from physically distinct monitoring systems. Analysis of blepharometric data (for example determination of relationships between current and historical data) may be performed at the cloud system <NUM>, at the local systems <NUM>-<NUM>, or combined across the cloud and local systems.

The local systems illustrated in <FIG> are:.

<FIG> also shows how system <NUM> is able to interact with a plurality of user mobile devices such as device <NUM>. User identification data <NUM> provides addressing information thereby to enable system <NUM> to deliver messages, alerts, and the like to correct user devices.

Beyond advantages of providing an ability to carry user blepharometric data baselines and data collection between physical collection systems, and added benefit of a system such as that of <FIG> is an ability to personalise condition prediction algorithms for individual users. This is achieved by: (i) identifying a personalised blepharometric biomarker for a given user, wherein that blepharometric artefact is representative of a particular neurological condition; and (ii) configuring the system such that whenever that particular user is identified, an analysis system executes a process configured to monitor for that biomarker (and perform a defined action in response). For example, in one example it is determined that a particular person displays a specific blepharometric biomarker (for example threshold spiking in negative inter event duration) in the lead-up to a seizure event; a process configured to monitor for that biomarker is initialised in response to identification of that person. For example, an analysis module of an in-vehicle device is configured for such monitoring once the person is detected, and provides a seizure warning when the biomarker is detected.

In embodiments where infrared reflectance oculography techniques are used, the blepharometric data is optionally defined by a reading made by an infrared reflectance sensor, and as such is a proxy for eyelid position. That is, rather than monitoring the actual position of an eyelid, infrared reflectance oculography techniques use reflectance properties and in so doing are representative of the extent to which an eye is open (as the presence of an eyelid obstructing the eye affects reflectivity). In some embodiments additional information beyond eyelid position may be inferred from infrared reflectance oculography, for example whether a subject is undergoing tonic eye movement. For the present purposes, "blepharometric data" in some embodiments includes infrared reflectance oculography measurements, and hence may additionally be representative of tonic eye movement.

<FIG> illustrates an example blepharometric data relationship analysis system, which may be incorporated into embodiments described above. In some cases, components/functionalities of this system are distributed across local and cloud-based processing systems.

One or more new sets of blepharometric data <NUM>, which may be defined via any collection system for instance as shown in <FIG>, are received by a new data processing module <NUM>. Module <NUM> is configured to perform data validation and/or data cleaning, thereby to ensure that the data is suitable for analysis and/or storage. For example, data displaying irregularities and/or having a sample time below a given threshold is excluded. A new data storage module <NUM> is configured to coordinate storage of the new set or sets of data <NUM>, following processing my module <NUM>, into a data store <NUM> containing historical blepharometric data for the user.

A statistical value determination module <NUM> applies an expandable set of processing algorithms to data in store <NUM> thereby to extract a range of statistical values (for example averages for blepharometric artefacts, optionally categorised based on collection conditions and other factors). These statistical values are stored in data store <NUM> thereby to maintain richer detail regarding baseline blepharometric data values for the user, preferably in a way that is tied to defined relationship analysis algorithms. That is, if an algorithm X to determine a condition Y relies on analysis of a blepharometric artefact Z, then module <NUM> is preferably configured to apply an algorithm configured to extract artefact Z from user blepharometric data.

A new data relationship processing module <NUM> is configured to identify relationships between new data <NUM> and historical data <NUM>. Data rules to facilitate the identification of particular relationships that are known to be representative (or predictively representative) of neurological conditions are defined in condition identification rules <NUM>. Rules <NUM> are periodically updated based on new knowledge regarding blepharometric/neurological condition research. For example, a given rule defines a category of relationship between one or more blepharometric data artefacts in new data <NUM> and one or more baseline values extracted from historical data in story <NUM> based on operation of module <NUM>.

In the case that a defined category of relationship is identified by module <NUM>, representative data is passed to an output rules module which contains logical rules that define how a user is to be notified (e.g. in-vehicle alert, message to smartphone app, or email), and in response a given output module <NUM> is invoked to provide the designated output.

A trend analysis module <NUM> is configured to continuously, periodically or in an event driven manner (for example in response to receipt of new blepharometric data) identify trends/changes in user blepharometric data. Again, data rules to facilitate the identification of particular trends that are known to be representative (or predictively representative) of neurological conditions are defined in condition identification rules <NUM>. Rules <NUM> are periodically updated based on new knowledge regarding blepharometric/neurological condition research. For example, a given rule defines a threshold deviation in one or more artefacts over a threshold time as being predictively representative of a neurological condition.

Again, the case that a defined category of relationship is identified by module <NUM>, representative data is passed to an output rules module which contains logical rules that define how a user is to be notified (e.g. in-vehicle alert, message to smartphone app, or email), and in response a given output module <NUM> is invoked to provide the designated output.

It will be appreciated that, in this manner, the system of <FIG> is configurable to monitor for a range of neurological conditions that are identifiable in blepharometric data based on point-in-time variations from known baselines that are generated and refined over extended period (i.e. based on a collection of time-separated data sets), and trends in blepharometric data over time (even where differences between consecutive data sets are relatively minor).

It will be appreciated that this form of data collection and analysis is of significant use in the context of predicting and understanding neurological conditions, for example in terms of: (i) identifying potential degenerative conditions and rates of onset; (ii) identifying point-in-time events that led to sudden changes in neurological conditions; (iii) monitoring long-term effects of contact sports (e.g. concussive brain injuries) for participants, (iv) personalising blepharometric data analysis for individual users.

Referring to <FIG> one embodiment provides a portable electronic device <NUM> including: a display screen <NUM>; and a front-facing camera <NUM>; wherein the portable electronic device is configured to concurrently execute, via software instructions <NUM> which execute on a processor of device <NUM>: (i) a first software application that provides data via the display screen; and (ii) a second software application that receives input from the front facing camera thereby to facilitate detection and analysis of blepharon data. For example, the first software application is in one embodiment a messaging application, and in another embodiment a social media application.

One embodiment provides computer executable code that when executed causes delivery via a computing device of a software application with which a user interacts for a purpose other than blepharon-based data collection, wherein the computer executable code is additionally configured to collect data from a front-facing camera thereby to facilitate analysis of blepharon data. The purpose may be, for example, messaging or social media.

Embodiments such as that of <FIG> provide for collection of blepharon data via a background software application executing on electronic device with a front-facing camera. This provides opportunities to analyse a device user's neurological condition, for example in the context of predicting seizures, advising on activities, diagnosing potential neurological illnesses, detecting drowsiness, and so on.

It will be appreciated that the above disclosure provides analytic methods and associated technology that enables improved analysis of human neurological conditions.

It should be appreciated that in the above description of exemplary embodiments of the invention, various features of the disclosure are sometimes grouped together in a single embodiment, FIG. , or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects.

Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the embodiment.

However, it is understood that embodiments of the disclosure may be practiced without these specific details.

Similarly, it is to be noticed that the term coupled, when used in the claims, should not be interpreted as being limited to direct connections only. The terms "coupled" and "connected," along with their derivatives, may be used. Thus, the scope of the expression a device A coupled to a device B should not be limited to devices or systems wherein an output of device A is directly connected to an input of device B. It means that there exists a path between an output of A and an input of B which may be a path including other devices or means. "Coupled" may mean that two or more elements are either in direct physical or electrical contact, or that two or more elements are not in direct contact with each other but yet still co-operate or interact with each other.

Claim 1:
A system (<NUM>) configured to facilitate analysis of subject neurological conditions based on analysis of involuntary blinks and eyelid movements, the system including:
an input module (<NUM>) configured to receive, from a plurality of sensor systems each respectively configured to enable collection of blepharometric data from a human subject, a set of sensor data including (i) blepharometric data; and (ii) subject identification data;
a subject identification module that is configured to identify a unique human subject from which a set of blepharometric data is collected by a given one of the sensor devices;
a blepharometric data input processing module configured to process the set of blepharometric data collected by the sensor device thereby to define current blepharometric data for the identified human subject, wherein the blepharometric data is representative of eyelid position as a function of time;
a memory module (<NUM>) that is configured to maintain a record of historical blepharometric data for the identified human subject, and for one or more additional human subjects; a blepharometric data variation processing module that is configured to identify threshold variations in blepharometric data over time based on repeated processing of the historical blepharometric data, as modified by newly received sets of blepharometric data including the current blepharometric data, thereby to identify presence of one or more blepharometric data variation indicators; and
an output module that is configured to provide a data output in response to identification of blepharometric data variation indicator;
wherein the memory module that is configured to maintain a record of historical blepharometric data for the identified human subject includes statistical information derived from processing of blepharometric data collected across a plurality of previous periods.