System and method configured for identifying, monitoring, predicting and / or detecting traumatic brain injuries in an active environment

Abstract

The present invention relates to a system and method configured for identifying, monitoring, predicting and / or detecting brain injury to a human participant in a live active environment, the system comprising: a database configured to maintain a profile record for the participant, at least one physiological sensor to measure one or more physiological characteristics of the participant, at least one biomechanical sensor to measure impact forces experienced by the participant, and processing means configured to: generate a participant impact profile record by combining data from the profile record for the participant, the physiological data and the impact force data, and compare the participant impact profile record generated with previously modelled participant impact profile records, or use one or more modelling algorithms, to obtain output brain stress and strain data. The brain stress and strain data is included as fields in the participant impact profile record to generate an enhanced participant impact profile record and used determine whether an alert criterion for a head injury risk event is met by comparing it with a database of stored clinical injury profile records. Means for outputting a substantially instantaneous alert to one or more remote computing devices is provided if an alert criterion for a head injury is met.

Description

[0001] “SYSTEM AND METHOD CONFIGURED FOR IDENTIFYING, MONITORING, PREDICTING AND / OR DETECTING TRAUMATIC BRAIN INJURIES IN AN ACTIVE ENVIRONMENT”

[0002] The present invention relates to a system and method configured for identifying, monitoring, predicting and / or detecting traumatic brain injuries, including concussions, other mild traumatic brain injuries, and cumulative damage resulting from such injuries in human participants, and particularly, although not limited to, human participants in active environments including those engaged in sporting activities, industrial settings and / or the military.

[0003] In sporting activities, such as rugby, soccer, boxing, mixed martial arts, American football etc traumatic brain injuries (TBI), including concussions and mild traumatic brain injuries (mTBIs), are a significant concern for player safety. Similar issues of concern exist in industrial settings, the military and other active environments.

[0004] Currently, in many sports a standard-of-care exists where observers / players are aware of events likely to have caused mTBIs and are aware of the symptoms of an mTBI. In such sports, the affected players are removed from play for a head injury assessment (HIA). Failing the HIA results in removal of the player from the game and a return-to-play protocol is typically followed over the following weeks.

[0005] However, research indicates that 90% of athletes who sustain a concussion do not exhibit any immediate, observable symptoms, making it difficult to diagnose these injuries in real time.

[0006] These hidden concussions, along with the cumulative effects of smaller, sub-concussive impacts, may lead to long-term neurological conditions, such as dementia, reduced cognitive functioning, and chronic traumatic encephalopathy (CTE). Athletes who continue to play, even for a few minutes after sustaining a concussion, may take twice as long to recover as those who come off the field of play immediately.

[0007] Current diagnostic tools thus only become useful after visible symptoms emerge or after a suspected event has occurred. This results in delayed interventions and increases the risk of long-term damage from repeated, undiagnosed head impacts.

[0008] For example, in the game of rugby, recent developments have seen the introduction of instrumented mouthguards (IMGs) at the higher levels of the sport. When an impact over aspecific threshold is measured, that player is removed from play for a HIA and subsequent treatment.

[0009] Such an approach to player safety has many limitations including:

[0010] The player must be wearing a functioning instrumented mouthguard.

[0011] The over-threshold impact must be noticed and acted upon.

[0012] The threshold is a universal value without consideration of the individual’s characteristics.

[0013] The player may have suffered multiple sub-threshold impacts causing cumulative injury which will not be flagged.

[0014] Accordingly, identifying, monitoring, predicting and / or detecting concussions and other TBIs early, particularly in cases where traditional symptom-based diagnostics are insufficient is of great importance to player safely.

[0015] Moreover, there is a need to improve the way in which cumulative damage relating to concussions and other TBIs in players is identified, monitored, and / or detected.

[0016] Furthermore, it is important that the results of any analysis relating to a potential concussions and other TBIs are able to be delivered to professionals in a quasi-real-time manner so that immediate and appropriate action and interventions may be taken “on the field of play”, and it is to be noted that current computational modelling approaches, such as those using finite element analysis, are too computationally resource intensive and complex for ‘live’ implementations.

[0017] It is therefore an object of the present invention to provide a system and method that goes at least some way toward overcoming the above problems and / or which will provide the public and / or industry with a useful alternative.

[0018] Further aspects of the present invention will become apparent from the ensuing description which is given by way of example only.

[0019] According to the invention, there is provided a system configured for identifying, monitoring, predicting and / or detecting brain injury to a human participant in a live active environment, the system comprising:a database configured to maintain a profile record for the participant, the profile record having data corresponding to the participant’s history of prior head injuries, and personal characteristics including one or more of: age, height, weight, head mass, Cumulative Playing Time, Cumulative Impacts (g), Cumulative Impacts (n), Biomarkers, and gender,

[0020] at least one physiological sensor communicatively coupled to the participant to measure one or more physiological characteristics of the participant, the at least one physiological sensor configured to transmit data relating to the measured one or more physiological characteristics as physiological data to the database,

[0021] at least one biomechanical sensor communicatively coupled to the participant to measure impact forces experienced by the participant, the at least one biomechanical sensor configured to transmit data relating to the measured impact forces as impact force data to the database,

[0022] processing means configured to:

[0023] generate a participant impact profile record by combining data from the profile record for the participant, the transmitted physiological data and the impact force data, and

[0024] compare the participant impact profile record generated with previously modelled participant impact profile records to determine an optimally matching record for the participant impact profile record, whereby, if it is determined that there is an optimally matching record then the processing means is further configured to output brain stress and strain data associated with that optimally matching record, and if it is determined that there is not an optimally matching record then the processing means is configured to estimate and output brain stress and strain data using one or more modelling algorithms,

[0025] incorporate the brain stress and strain data as fields in the participant impact profile record to generate an enhanced participant impact profile record and store the enhanced participant impact profile record to the database,determine whether an alert criterion for a head injury risk event is met by comparing the enhanced participant impact profile record with a database of stored clinical injury profile records,

[0026] in which an alert criterion is met if a stored clinical injury profile record is located that optimally matches the enhanced participant impact profile record, and

[0027] if an optimally matching stored clinical injury profile record is not located for the enhanced participant impact profile record, the processing means is configured to use a machine learning algorithm to classify a head injury risk level and assess if the alert criterion is met or not, and store in the enhanced participant impact profile record data relating to the classified head injury risk level, and

[0028] means for outputting a substantially instantaneous alert to one or more remote computing devices if the alert criterion is met.

[0029] The present invention provides a multi-sensor system that combines biomechanical and physiological monitoring to improve TBI detection of participants in an environment having risks associated with head injuries, such as a sporting activity, an industrial setting and / or a military setting.

[0030] By correlating impact forces with physiological responses, the invention provides an improvement in the assessment of head injuries. A combination of sensors to capture impact data and physiological data relating to measured body responses thus enhances the detection and analysis of concussive and sub-concussive events in near real time.

[0031] The present invention gathers kinetic data from a biomechanical sensor, physiological data from a physiological sensor, and optionally, video data from the active participant, transmits this data to a remote processor, such as a pitch side monitor, where it is synthesised or combined with the participant’s existing data, such as their age, height and other physical characteristics.

[0032] Generating a participant impact profile record by combining data from the profile record for the participant, the transmitted physiological data and the impact force data provides a data pairing for a participant and a specific impact event (i.e. the transmitted physiological data and theimpact force data).

[0033] The kinematic, physiological, and optional video data together with a participants known profile data are provided as an input record to generate immediate outputs in the form of brain stress and strain data. This is achieved by comparing the input record with records in a database of prior modelled and stored participant / impact pairings.

[0034] Brain stresses may be understood to be the internal forces acting within brain tissue due to loads or impacts, while brain strains are the resulting deformations or changes in shape of the brain tissue caused by those stresses. They are commonly discussed as VMS (von Mises stress), the combined internal stress in brain tissue, and MPS (maximum principal strain), the largest tensile strain experienced by the brain tissue, often expressed as a percentage. These are typical outputs of finite element analysis and other computationally intensive modelling processes

[0035] A risk assessment follows by incorporating the stress and strain data with the participant impact profile record thereby generating an enhanced participant impact profile record and comparing this with stored clinical injury profile records such that if the analysis indicates that an individual’s current status or their status trajectory is similar-enough to clinically proven injury profiles, an alert is raised in quasi real-time, facilitating appropriate intervention, acting as a diagnostic adjunct, and protecting users’ brain health.

[0036] The present invention, by providing analysis to determine whether an individual’s current status or their status trajectory is “similar-enough” to clinically proven injury profiles” provides a method to reach a substantially near real time determination that an intervention is needed. This is a surrogate for highly complex; time and resource intensive computations combined with manual human input and assessment.

[0037] The alert may be an audible or visible alert, and may be in the form of a device screen message, an SMS, email, or any other type of alert capable of relaying that the alert criteria for a head injury risk event has occurred and action should be taken, such as removing the participant from the active environment for assessment or whatever intervention is deemed necessary by coaching and medical staff.

[0038] The present invention thus provides an explicit, sports-runtime surrogate model of a headFinite Element model or other computational model in conjunction with player characteristics. It may be used live on the sideline or as a team management tool to estimate brain stress and strain metrics and concussion risk from sports impact data and player characteristics.

[0039] Preferably, the at least one biomechanical sensor is one or more or a combination of: a force sensitive resistor sensor, an accelerometer sensor and / or a gyroscope sensor.

[0040] Preferably, the system further comprises at least one video camera means communicatively coupled to the participant to capture video and image data, the at least one video camera means configured to record and transmit data relating to the participants movement, an impact, weather conditions, other conditions and / or location to the database.

[0041] Preferably, the participant impact profile record is generated by combining data from the profile record for the participant, the transmitted physiological data, the impact force data, and the video data.

[0042] Preferably, the at least one physiological sensor is one or more or a combination of: an Electroencephalography (EEG) sensor, a Transcranial Doppler (TCD) sensor, an electrocardiography (ECG) sensor, a Photoplethysmography (PPG) sensor, a Galvanic skin response (GSR) sensor, a heart rate sensor and derivates, and / or a body temperature sensor.

[0043] Preferably, the at least one physiological sensor and the at least one biomechanical sensor are integrated in headgear worn by the participant.

[0044] Integrating advanced biomechanical and physiological sensors into smart headgear provides a suitable wearable article for locating sensors on or about the head to detect and monitor head injury caused by impacts.

[0045] Preferably, the modelling algorithms applied to estimate brain stress and strain data are provided by one more of: an artificial intelligence (Al) driven algorithm, a computational modelling algorithm and / or a machine learning algorithm, trained on known participant impact profiles and clinical injury profiles.

[0046] Preferably, the profile record for the participant is configured with personal characteristics including biomarkers and other physiological data relating to the participant.In a further aspect there is provided a method configured for identifying, monitoring, predicting and / or detecting brain injury to a human participant in a live active environment, the method comprising:

[0047] configuring a database to maintain a profile record for the participant, the profile record having data corresponding to the participant’s history of prior head injuries, and personal characteristics including one or more of: age, height, weight, head mass, Cumulative Playing Time, Cumulative Impacts, Cumulative Impacts, Biomarkers, and gender,

[0048] measuring one or more physiological characteristics of the participant by at least one physiological sensor communicatively coupled to the participant, the at least one physiological sensor configured to transmit data relating to the measured one or more physiological characteristics as physiological data to the database,

[0049] measuring impact forces experienced by the participant by at least one biomechanical sensor communicatively coupled to the participant, the at least one biomechanical sensor configured to transmit data relating to the measured impact forces as impact force data to the database,

[0050] configuring processing means to perform steps of:

[0051] generating a participant impact profile record by combining data from the profile record for the participant, the transmitted physiological data and the impact force data, and

[0052] comparing the participant impact profile record generated with previously modelled participant impact profile records to determine an optimally matching record for the participant impact profile record, whereby, if it is determined that there is an optimally matching record then the processing means is further configured to output brain stress and strain data associated with that optimally matching record, and if it is determined that there is not an optimally matching record then the processing means is configured to estimate and output brain stress and strain data using one or more modelling algorithms,incorporating the brain stress and strain data as fields in the participant impact profile record to generate an enhanced participant impact profile record and store the enhanced participant impact profile record to the database,

[0053] determining whether an alert criterion for a head injury risk event is met by comparing the enhanced participant impact profile record with a database of stored clinical injury profile records,

[0054] in which an alert criterion is met if a stored clinical injury profile record is located that optimally matches the enhanced participant impact profile record, and

[0055] if an optimally matching stored clinical injury profile record is not located for the enhanced participant impact profile record, the processing means is configured to use a machine learning algorithm to classify a head injury risk level and assess if the alert criterion is met or not, and store in the enhanced participant impact profile record data relating to the classified head injury risk level, and

[0056] outputting a substantially instantaneous alert to one or more remote computing devices if the alert criterion is met.

[0057] The method comprises a further step of capturing video and image data by using at least one video camera means communicatively coupled to the participant, the at least one video camera means configured to record and transmit data relating to the participants movement, an impact, weather conditions, and / or location to the database.

[0058] The modelling algorithms applied to estimate brain stress and strain data are provided by one more of: an artificial intelligence (Al) driven algorithm, a computational modelling algorithm and / or a machine learning algorithm.

[0059] The profile record for the participant is configured with personal characteristics including biomarkers and other physiological data relating to the participant.

[0060] The present invention provides a system and method for identifying, monitoring, predicting and / or detecting concussions and other mild traumatic brain injuries and related conditions that may be caused by activities where the participant suffers single or multiple impacts whichgenerate sensor readings and other data that, in combination with each other and with the personal physiological characteristics and history of the participant, and in combination with known participant impact profiles and the known profiles and histories of other clinically validated injured and non-injured players, consist of a diagnostic adjunct to detect, predict and ultimately prevent concussions and other mTBIs and related conditions.

[0061] The invention will be more clearly understood from the following description of some embodiments thereof, given by way of example only, with reference to the accompanying drawings in which:

[0062] Figs. 1 is a hybrid data flow and block diagram showing a system architecture configured according to the invention, and

[0063] Figs. 2 and 3 are process flow diagrams showing respective first and second parts of a method configured according to the invention.

[0064] Referring to the drawings, and initially to, Fig. 1 there is provided a system 1 configured for identifying, monitoring, predicting and / or detecting brain injury to a human participant 2 in an active environment.

[0065] The active environment may be provided by a sporting activity, an industrial setting and / or a military setting. In the context of a sporting activity, it will be understood that a head injury may be brought about by an impact occurring when the head of a participant collides with another player’s body or the ground. In an industrial setting, such an impact might occur when the head of the participant is impacted causing injury due to a work-related incident or environment. In a military setting a participant’s head might be impacted by an incident or environment.

[0066] Accordingly, the application of the present invention is not limited to sport only and has practical utility in a variety of real-life settings, and reference to sport in the following should in no way be seen as limiting.

[0067] The system 1 comprises at least one biomechanical sensor, indicated by the reference numeral 3, communicatively coupled to the participant 2 to measure impact forces being experienced by the head of the participant 2.The one or more biomechanical sensors 3 are configured to transmit via a network 6 data relating to the measured impact forces as impact force data. The network 6 may be configured as a typical computer network for data communication, and may be wired or wireless etc.

[0068] The one or more biomechanical sensors 3 may include one or more force sensitive resistors, accelerometers and gyroscopes, to measure impact forces directly. It will be understood that such sensor devices quantify linear and rotational accelerations experienced by the head of the participant during impacts, helping to identify dangerous forces that could cause brain injury.

[0069] The system 1 further comprises at least one physiological sensor 3 communicatively coupled to the participant 2 to measure one or more physiological characteristics of the participant 2.

[0070] The at least one physiological sensor 3 may be one or more or a combination of: an Electroencephalography (EEG) sensor, a Transcranial Doppler (TCD) sensor, an electrocardiography (ECG) sensor, a Photoplethysmography (PPG) sensor, a Galvanic skin response (GSR) sensor, a heart rate sensor or derivatives, and / or a body temperature sensor.

[0071] EEG sensors are widely used in clinical and research settings due to its ability to detect immediate electrical abnormalities caused by brain trauma.

[0072] Transcranial Doppler (TCD) sensors detect mTBI through the measurement of blood flow velocity in cerebral vessels. TCD is a non-invasive technique using ultrasound to assess cerebral circulation disruptions, which may provide immediate insights following a head impact.

[0073] For monitoring the heart and autonomic nervous system (ANS), electrocardiography (ECG) sensors provide heart rate variability (HRV) analysis by recording the electrical activity and rhythm of the heart through electrodes placed on the skin.

[0074] Photoplethysmography (PPG) sensors measure blood flow using light sources and photodetectors. Galvanic skin response (GSR) measure physiological arousal and stress associated with mTBI. GSR measures the electrical conductance of the skin, which varies with moisture level due to sweat gland activity. As a proxy for autonomic nervous system activity, GSR reflects changes in sympathetic nervous system activation. Although it does not directlymeasure mTBI, it provides valuable insights into stress responses potentially related to brain trauma.

[0075] The at least one physiological sensor 3 and the at least one biomechanical sensor 3 for generating kinematic and physiological data are integrated in protective headgear or headwear 4 worn by the participant 2.

[0076] The system 1 further comprises at least one video camera means communicatively coupled to the participant to capture video and image data, the at least one video camera means configured to record and transmit data relating to the participants movement, an impact, weather conditions and / or location.

[0077] The integration of sensors 3 into headgear 4 wearable by the participant 2 requires careful consideration to ensure they do not compromise the comfort or fit of the protective equipment. The sensors 3 are integrated to ensure that their connections withstand impacts, sweat, rain, and dirt. Moreover, ensuring accurate and stable data is important in a high-movement, physically demanding environment. The headgear 4 must be designed so that sensors 3 remain firmly in place without interfering with the participants 2 performance or safety. Additionally, the likelihood of frequent wear and tear in contact sports necessitates that these sensors 3 are easy to maintain and replace.

[0078] However, in alternative embodiments the at least one physiological sensor 3 and the at least one biomechanical sensor 3 may not be integrated in headwear but be provided as wearable sensors which are directly fixed to the participant’s head. In a further alternative, such sensors 3 may be provided in another wearable article, such a gumshield or clothing article. Accordingly, reference to the sensors 3 being integrated in protective headwear should not be seen as limiting.

[0079] The system 1 comprises processing means 5 provided by an application executing on one or more computer devices 7 having display means, which may be operated or monitored by individuals, coaching and / or medical staff 11.

[0080] The processing means 5 includes an alert generator means 12 to output a substantially instantaneous alert to one or more remote computing devices 7 if an alert criterion is met. The alert generator means 12 may be provided at the computer device 7 as an audible or visiblealert and may be in the form of a device screen message, an SMS, email, or any other type of alert capable of relaying that an alert criterion has been met.

[0081] The system 1 further comprises a database 10 coupled with the processing means 5 that is configured to maintain a profile record for the participant 2, the profile record having data corresponding to the participant’s history of prior head injuries, and personal characteristics including age, height, weight, head mass, Cumulative Playing Time, Cumulative Impacts (g), Cumulative Impacts (n), Biomarkers and / or gender.

[0082] The database 10 further maintains stored participant impact profile records and stored clinical injury profile records.

[0083] The database 10 may be coupled with data mining or analytics components 8 to assist in determining the likelihood that a participant has sustained a neurological injury. Such components may be utilised or trained by computational modelling, machine learning or Al algorithms to determine values for alert criterion for the participant 2.

[0084] Figs. 2 and 3 are process flow diagrams showing respective first and second parts of a method implemented by the system of Fig. 1 to determine if an injury risk event has occurred, indicated by step 20.

[0085] At steps 22, 24 and 26, the impact force or kinematic data, the physiological data, and video data are retrieved from the database 10 and combined, at step 28, to prepare a profile for a specific impact, referred to in Fig. 2 as an “impact profile”.

[0086] At step 30, data from the profile record for the participant is obtained from the database 10.

[0087] The data retrieved at steps 28, 30 are then combined at step 32 to generate a participant impact profile record.

[0088] It should be understood that generating a participant impact profile record by combining data from the profile record for the participant, the transmitted physiological data and the impact force data, and optional video data, provides a data pairing for a participant’s profile data and a specific impact event.At step 34, the participant impact profile record generated is provided as an input and compared with previously modelled participant impact profile records in the database 10 to determine an optimally matching record.

[0089] If an optimally matching record is located, then the processing means 5 is configured to output the brain stress and strain data that is stored as fields in the optimally matching record.

[0090] Conversely, if an optimally matching record is not located then the processing means is configured to estimate and output brain stress and strain data using one or more modelling algorithms.

[0091] The brain stress and strain data output at step 34 is incorporated as fields in the participant impact profile record to generate an enhanced participant impact profile record which is stored in the database 10.

[0092] At step 36, the enhanced participant impact profile record is compared with stored clinical injury profile records, such stored clinical injury profile records having data fields which will be comparable to the data fields of the enhanced participant impact profile record making such comparison is possible. The comparison serves to assess at step 38 if, based on the data in the enhanced participant impact profile record, a participant’s current condition or their disease trajectory is” similar-enough” to data contained in the data fields of the clinically proven injury profiles records.

[0093] If a stored clinical injury profile record is located that optimally matches the enhanced participant impact profile record, then it is determined that the alert criterion is met, that here is an elevated injury risk so that and, at step 40, a substantially instantaneous alert is output to one or more remote computing devices 5.

[0094] If an optimally matching stored clinical injury profile record is not located for the enhanced participant impact profile record, the processing means is configured to generate a classified head injury risk level using computational models, machine learning, artificial intelligence and / or other computational methods to create a risk classification for the instance and use this risk classification to determine whether an alert criterion has been met. As above, if it is determined that the alert criterion is met by this alternative classification approach then, at step 40, a substantially instantaneous alert is output to one or more remote computing devices 5.Data relating to the classified injury risk level is included in the enhanced participant impact profile record.

[0095] If an alert criterion has been met and an alert has been output at step 40, then at step 42, an appropriate intervention may be performed by coaching and medical staff, the system thereby acting as a diagnostic adjunct, and protecting users’ brain health.

[0096] At step, 44 if an alert criterion is not met then an alert is not output and no intervention is required.

[0097] The outcome of steps 42, 44 is measured at step 46 and step 48, the outcome is recorded and a labelling feedback loop is executed to continuously improve the performance of the system 1.

[0098] The present invention provides a system and method that avoids delays in participants receiving potentially lifesaving interventions following brain injury events thereby decreasing the risk of long-term damage from one-off or repeated head impacts.

[0099] By its use at least one biomechanical sensor and at least one physiological sensor the present invention provides advanced monitoring systems that can detect and alert teams, coaches and staff to potential injuries in near real time. Advanced analytics including machine learning algorithms are used to provide near real-time detection and prediction of mTBI by processing the multimodal data and the cumulative effect of head impacts from clinical data with components, including headgear with associated sensors, data collection and processing with cloud storage and advanced analytics platform using machine learning and Al.

[0100] The present invention correlates sensor data (real-time and historical) with a scientifically derived traumatic brain injury (TBI) indicator database via a proprietary predictive Al algorithm that is configured to output alerts indicating that participant should seek medical attention if a “concussion” or mTBI is suspected.

[0101] The at least one biomechanical sensor and at least one physiological sensor are configured to continuously transmit data in substantially instantaneously I near real-time to a digital health data management platform, which accesses a clinically validated library of player profiles and impacts and clinically validated indicator data sets.The system and method of the present invention transmits alerts to team management, medical staff or parents to risk of brain injuries resulting from either a large single impact or repeated / sub-concussive impacts with reference to participants own personalised impact and biomarker data and known clinical injury data or models trained on such data.

[0102] It is to be understood that the invention is not limited to the specific details described herein which are given by way of example only and that various modifications and alterations are possible without departing from the scope of the invention.

Claims

CLAIMS1. A system configured for identifying, monitoring, predicting and / or detecting brain injury to a human participant in a live active environment, the system comprising:a database configured to maintain a profile record for the participant, the profile record having data corresponding to the participant’s history of prior head injuries, and personal characteristics including one or more of: age, height, weight, head mass, Cumulative Playing Time, Cumulative Impacts (g), Cumulative Impacts (n), Biomarkers, and gender,at least one physiological sensor communicatively coupled to the participant to measure one or more physiological characteristics of the participant, the at least one physiological sensor configured to transmit data relating to the measured one or more physiological characteristics as physiological data to the database,at least one biomechanical sensor communicatively coupled to the participant to measure impact forces experienced by the participant, the at least one biomechanical sensor configured to transmit data relating to the measured impact forces as impact force data to the database,processing means configured to:generate a participant impact profile record by combining data from the profile record for the participant, the transmitted physiological data and the impact force data, andcompare the participant impact profile record generated with previously modelled participant impact profile records to determine an optimally matching record for the participant impact profile record, whereby, if it is determined that there is an optimally matching record then the processing means is further configured to output brain stress and strain data associated with that optimally matching record, and if it is determined that there is not an optimally matching record then the processing means is configured to estimate and output brain stress and strain data using one or more modelling algorithms,incorporate the brain stress and strain data as fields in the participant impact profile record to generate an enhanced participant impact profile record and store the enhanced participant impact profile record to the database, determine whether an alert criterion for a head injury risk event is met by comparing the enhanced participant impact profile record with a database of stored clinical injury profile records,in which an alert criterion is met if a stored clinical injury profile record is located that optimally matches the enhanced participant impact profile record, andif an optimally matching stored clinical injury profile record is not located for the enhanced participant impact profile record, the processing means is configured to use a machine learning algorithm to classify a head injury risk level and assess if the alert criterion is met or not, and store in the enhanced participant impact profile record data relating to the classified head injury risk level, andmeans for outputting a substantially instantaneous alert to one or more remote computing devices if the alert criterion is met.

2. The system as claimed in Claim 1, in which the at least one biomechanical sensor is one or more or a combination of: a force sensitive resistor sensor, an accelerometer sensor and / or a gyroscope sensor.

3. The system as claimed in Claim 1 or Claim 2, in which the at least one physiological sensor is one or more or a combination of: an Electroencephalography (EEG) sensor, a Transcranial Doppler (TCD) sensor, an electrocardiography (ECG) sensor, a Photoplethysmography (PPG) sensor, a Galvanic skin response (GSR) sensor, a heart rate sensor and derivatives, and / or a body temperature sensor.

4. The system as claimed in any one of the previous Claims, in which the at least one physiological sensor and the at least one biomechanical sensor are integrated in headgear worn by the participant.

185. The system as claimed in any one of the previous Claims, further comprising at least one video camera means communicatively coupled to the participant to capture video and image data, the at least one video camera means configured to record and transmit data relating to the participants movement, an impact, weather conditions, and / or location to the database.

6. The system as claimed in any one of the previous Claims, in which the modelling algorithms applied to estimate brain stress and strain data are provided by one more of: an artificial intelligence (Al) driven algorithm, a computational modelling algorithm and / or a machine learning algorithm.

7. The system as claimed in any one of the previous Claims, in which the profile record for the participant is configured with personal characteristics including biomarkers and other physiological data relating to the participant.

8. A method configured for identifying, monitoring, predicting and / or detecting brain injury to a human participant in a live active environment, the method comprising:configuring a database to maintain a profile record for the participant, the profile record having data corresponding to the participant’s history of prior head injuries, and personal characteristics including one or more of: age, height, weight, head mass, Cumulative Playing Time, Cumulative Impacts, Cumulative Impacts, Biomarkers, and gender,measuring one or more physiological characteristics of the participant by at least one physiological sensor communicatively coupled to the participant, the at least one physiological sensor configured to transmit data relating to the measured one or more physiological characteristics as physiological data to the database,measuring impact forces experienced by the participant by at least one biomechanical sensor communicatively coupled to the participant, the at least one biomechanical sensor configured to transmit data relating to the measured impact forces as impact force data to the database,configuring processing means to perform steps of:19generating a participant impact profile record by combining data from the profile record for the participant, the transmitted physiological data and the impact force data, andcomparing the participant impact profile record generated with previously modelled participant impact profile records to determine an optimally matching record for the participant impact profile record, whereby, if it is determined that there is an optimally matching record then the processing means is further configured to output brain stress and strain data associated with that optimally matching record, and if it is determined that there is not an optimally matching record then the processing means is configured to estimate and output brain stress and strain data using one or more modelling algorithms,incorporating the brain stress and strain data as fields in the participant impact profile record to generate an enhanced participant impact profile record and store the enhanced participant impact profile record to the database,determining whether an alert criterion for a head injury risk event is met by comparing the enhanced participant impact profile record with a database of stored clinical injury profile records,in which an alert criterion is met if a stored clinical injury profile record is located that optimally matches the enhanced participant impact profile record, andif an optimally matching stored clinical injury profile record is not located for the enhanced participant impact profile record, the processing means is configured to use a machine learning algorithm to classify a head injury risk level and assess if the alert criterion is met or not, and store in the enhanced participant impact profile record data relating to the classified head injury risk level, andoutputting a substantially instantaneous alert to one or more remote computing devices if the alert criterion is met.

209. The method as claimed in Claim 8, comprising a further step of capturing video and image data by using at least one video camera means communicatively coupled to the participant, the at least one video camera means configured to record and transmit data relating to the participants movement, an impact, weather conditions, and / or location to the database.

10. The method as claimed in Claim 9 or Claim 10, in which the modelling algorithms applied to estimate brain stress and strain data are provided by one more of: an artificial intelligence (Al) driven algorithm, a computational modelling algorithm and / or a machine learning algorithm.

11. The system as claimed in any one of Claims 8 to 10, in which the profile record for the participant is configured with personal characteristics including biomarkers and other physiological data relating to the participant.

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