Patent Publication Number: US-2020289042-A1

Title: Systems, Devices, and Methods of Determining Data Associated with a Persons Eyes

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     The present disclosure is a non-provisional of and claims priority to U.S. Provisional Patent Application No. 62/818,028 filed on Mar. 13, 2019 and entitled “Physiological State Evaluation Devices, Systems, and Methods”, which is incorporated herein by reference in its entirety. 
    
    
     FIELD 
     The present disclosure is generally related to physiological state evaluation devices, systems, and methods, and more particularly, to devices, systems, and methods configured to capture data (such as optical data, pressure data, vibration data, other data, or any combination thereof) associated with a person&#39;s eyes and the muscles around the person&#39;s eyes and to determine one or more physiological states based on the captured data. 
     BACKGROUND 
     A variety of factors may adversely impact cognitive processes and associated performance of a person, both in sports and in other aspects of life. For example, head injuries, genetic influences, disease/infection, exposure to toxic substances, and lifestyle factors (e.g., drugs use, alcohol use, dehydration), other factors, or any combination thereof may adversely impact a physiological state of a person, interfering with cognitive function and adversely affecting a person&#39;s life, including the person&#39;s well-being, performance, and even the person&#39;s life span. For example, lifestyle factors may adversely impact executive functions in cognition, such as visuo-spatial processing. In some instances, physiological state changes may interfere with the way neurons send, receive, and process signals by inhibiting neural pathways. 
     Similarly, head injuries or traumatic brain injuries, such as a concussion, may adversely impact the person&#39;s physiological state, such as by negatively affecting the person&#39;s short-term memory, reaction time, eye movements, behaviors, moods, pupillary reflexes, and other physiological functions. A concussion is a type of traumatic brain injury that may be caused by a bump, blow, or jolt to the head or by an impact that causes the head and brain to move rapidly back and forth. For example, falls, vehicular crashes, bicycle crashes, assaults, and sports impacts can cause concussions. Such impacts can cause the brain to bounce around or turn in the skull, causing bruising and stretching of brain tissue compromising brain cells, creating chemical changes in the brain, cognitive impairments, or any combination thereof. Some head injuries may also cause the brain to swell. Such bruising, stretching, or swelling of brain tissue may impair the person&#39;s physiological state. 
     SUMMARY 
     Embodiments of testing devices, systems, and methods are described below that can capture data associated with a person&#39;s eyes and surrounding eye muscles to detect one or more parameters indicative of physiological state changes. Such physiological state changes may be representative of brain injury, impairment, dehydration, or any combination thereof. In some implementations, a device may present visual data to a display and may capture image data associated with a person&#39;s eyes and eye muscles as the person looks at and tracks the visual data. The captured image data may be processed by the device or by an associated computing device (communicatively coupled to the device) to determine one or more parameters indicative of physiological state changes, which may be representative of cognitive impairment, brain injury, impairment, dehydration, or any combination thereof based on the image data. 
     In some implementations, a system may detect physiological state changes representative of cognitive impairment of a person based, at least in part, on optical data. The system may include a computing device including a display to present visual information to a person and an optical sensor to capture optical data of eyes, optical data associated with facial muscles around the eyes of the person, other data, or any combination thereof. The computing device may further include a processor to generate data indicative of impairment based on the optical data. 
     In some implementations, a system may include a computing device. The computing device may include one or more sensors to capture data associated with a person&#39;s eyes as the person observes one or more objects moving in a three-dimensional space. The computing device may include a display to present information related to the captured data. 
     In other implementations, 
     In still other implementations, a system may include a computing device. The computing device may include one or more sensors to capture data associated with a person&#39;s eyes as the person observes one or more objects moving in a three-dimensional space. The computing device may also include a processor coupled to the one or more sensors and configured to generate information related to the capture data and a display coupled to the processor and configured to present the generated information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a diagram of systems and devices to provide a physiological state evaluation, in accordance with certain embodiments of the present disclosure. 
         FIG. 2  depicts a flow diagram of a process of determining data indicative of a person&#39;s physiological state, in accordance with certain embodiments of the present disclosure. 
         FIG. 3  depicts a block diagram of a system including an analytics system to provide physiological state evaluation and analysis, in accordance with certain embodiments of the present disclosure. 
         FIG. 4  depicts a block diagram of a computing device, in accordance with certain embodiments of the present disclosure. 
         FIG. 5  depicts a block diagram of a computing device such as a virtual reality device or a smart glasses device, in accordance with certain embodiments of the present disclosure. 
         FIG. 6  depicts a diagram of optical test data that can be presented on one of the computing devices of  FIGS. 4 and 5 , in accordance with certain embodiments of the present disclosure. 
         FIG. 7  depicts a diagram of an eye-tracking test that uses three-dimensional movement, in accordance with certain embodiments of the present disclosure. 
         FIGS. 8A-8C  depict view angles that may be used to determine impairment, in accordance with certain embodiments of the present disclosure. 
         FIG. 9  depicts a system to capture optical data of a person as the person observes a three-dimensional moving object, in accordance with certain embodiments of the present disclosure. 
         FIG. 10  depicts an image including an image processing matrix and including elements or areas for analysis, in accordance with certain embodiments of the present disclosure. 
         FIG. 11  depicts a flow diagram of a method of determining impairment based on optical data, in accordance with certain embodiments of the present disclosure. 
         FIG. 12  depicts a flow diagram of a method of determining impairment based on optically detected ocular pressure, in accordance with certain embodiments of the present disclosure. 
         FIG. 13  depicts a flow diagram of a method of determining impairment based on motion and orientation data, in accordance with certain embodiments of the present disclosure. 
     
    
    
     In the following discussion, the same reference numbers are used in the various embodiments to indicate the same or similar elements. 
     DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS 
     Embodiments of systems, methods, and devices are described below that may capture data associated with a person&#39;s eyes, facial area surrounding the person&#39;s eyes, other data, or any combination thereof, and may automatically detect a change in physiological state, indicative of impairment based on the captured data. The captured data may include optical data, pressure data, vibration data, other data, or any combination thereof. 
     Examples of cognitive disorders that manifest with cognitive impairment disturbances may include, but are not limited to, a head injury, concussion or other traumatic brain injury; a chemical impairment (such as due to consumption of alcohol or illicit drugs, abuse of prescription drugs, smoking marijuana, allergic reaction, other sources of chemical impairments, exposure to toxic substances, or any combination thereof); early indicators of neurocognitive diseases or infections (such as Multiple Sclerosis, Parkinson&#39;s disease, Meningitis, AIDS related dementia, or any combination thereof); genetic influences (such as Alzheimer&#39;s disease); strokes; dementia; lifestyle factors (such as malnutrition, poor diet, dehydration, overheating—increased core body temp, or any combination thereof); other cognitive disorders or impairments; or any combination thereof. 
     In some implementations, an electronic device may be worn by a person. For example, the electronic device may include a virtual reality (VR) headset device, a smart glasses device, a smartphone positioned in front of the user&#39;s eyes, or another electronic device. The electronic device may include a display to provide data (such as moving images, colors, texts, light of varying intensities, other information, or any combination thereof). At the same time, the electronic device may capture optical data associated with a person&#39;s eyes, including facial muscles, skin surrounding the person&#39;s eyes, other data, or any combination thereof as the person observes the data on the display. The optical data may be used to determine physiological state changes, which may be indicative of cognitive impairment of the person. The optical data may be processed by the electronic device or may be communicated to a computing device coupled to the electronic device by a wired or wireless communications link so that the computing device can process the optical data. 
     In some implementations, the optical data may provide a biometric fingerprint that can be used to uniquely identify the person based, for example, on images of the user&#39;s eye. Further, the optical data may include color variations that may be imperceptible to the human eye, but which may reveal blood flow within and around the person&#39;s eyes. Additionally, the optical data may include data variations that can reveal details of the person&#39;s pupil reflexes, eye movements (smooth pursuits, saccadic movements, irregular, convergent, divergent, and so on), reaction time, eye shape, facial muscle movements, other information, or any combination thereof In some implementations, the optical data may also reveal ocular pressure based on movement data of the eye and the facial muscles, for example, in response to a physical impulse or vibration. 
     In some implementations, one or more transducers may be included in the electronic device. In one possible implementation, a transducer may be responsive to an electronic signal to apply a physical vibration or impulse to the person&#39;s skin, such as the skin below the user&#39;s eyes, and the optical data may observe eye and facial movements in response to the vibration or impulse. In some instances, the device or a computing device may infer swelling or ocular pressure based on the eye movements, facial movements, other data, or any combination thereof in response to the vibration or impulse. Other implementations are also possible. 
     In some implementations, as the person observes visual data on the display, optical data may be captured of the person&#39;s retina and optionally the interior of the person&#39;s eye through the pupil. The optical data may be used to detect macular degeneration, glaucoma, bulging eyes (swelling), cataracts, cytomegalovirus (CMV) retinitis, crossed eyes (or strabismus), macular edema, possible or impending retinal detachment, an irregular shaped cornea, lazy eye, ocular hypertension, uveitis, other ocular conditions, or any combination thereof. 
     In some implementations, the electronic device may include orientation and motion sensors, which may generate signals proportional to the movement and stability of the person. For example, a person with a neurocognitive impairment condition may sway or otherwise have difficulty standing still and straight without tilting. The orientation and motion sensors may generate signals representative of dizziness or changes in balance of the person, which signals may be indicative of physiological state changes representative of cognitive impairment. Other implementations are also possible. 
     It should be understood that the systems, devices, and methods may be implemented in a variety of configurations. In one implementation, a device may be self-contained and configured to display images, capture data, and determine physiological changes based on the captured data. In another implementation, the device may display images, capture data, and communicate the captured data to a computing device (through a wired or wireless connection), and the computing device may determine physiological changes based on the captured data. In still other implementations, the computing device may communicate with another computing device (such as a computer server) through a network to compare at least a portion of the captured data to previously captured data associated with the person. The previously captured data may include baseline physiological data that can be used as a basis for comparison to detect changes, which may be the result of an impact or other condition. In some instances, deviation from a baseline may be indicative of a physiological change, which may be used as a basis for diagnosis, such as to determine whether a person should enter a concussion protocol. Examples of implementations are described below with respect to  FIG. 1 . 
       FIG. 1  depicts a diagram  100  of systems and devices to provide physiological state evaluations, in accordance with certain embodiments of the present disclosure. The diagram  100  depicts a first person  102 ( 1 ) wearing a virtual reality (VR) headset  104 , which may communicate with a computing device  106 ( 1 ) through a communications link  108 ( 1 ). The computing device  106 ( 1 ) may be a tablet computer, a smartphone, a laptop computer, another computing device, or any combination thereof. The communications link  108 ( 1 ) may be a wired communications link (such as a Universal Serial Bus (USB) connection or another wired connection), a radio frequency (RF) communications link (such as a Bluetooth® communications link, a Wi-Fi® communications link, an 802.11x IEEE communications link, another RF communications link), or any combination thereof. 
     The VR headset  104  may include a display and a plurality of sensors, including optical sensors (such as a camera). The display may present visual data for viewing by the person. For example, the VR headset  104  may present images, objects, colors, different brightness intensities, information, or any combination thereof to the display. In some implementations, the VR headset  104  may present a moving object on the display such that the moving object appears to move three-dimensionally (away from and toward the user as well as side to side). For example, the VR headset  104  may present an object that appears to move from a distance at a center of a field of view directly toward a point between the person&#39;s eyes (i.e., a convergence test). 
     Optical sensors of the VR headset  104  may concurrently capture optical data  110 ( 1 ) associated with the eyes, facial area surrounding the eyes of the person, or any combination thereof as the person observes the visual data on the display. In the example of the convergence test, the optical data  110 ( 1 ) may capture divergence of the person&#39;s eyes as the object appears to move toward the person. The optical data  110 ( 1 ) may also include facial muscular movements, eye movements (rapid movement, tracking movement, and so on), pupil reflexes, pupil shape, blood flow, eye shape, swelling information, divergence data, biometric data, miniscule color variations, other optical information, or any combination thereof. Such optical data may be too rapid or too small to be detected by the naked eye of the doctor or observer, but changes may be amplified by the system to provide a readily discernable physiological response. 
     The VR headset  104  may also include one or more transducers to apply a vibration or impulse to the person&#39;s face while the optical sensors capture the optical data. For example, the transducers may impart a vibration or impulse that may cause the person&#39;s face and eyes to undulate, providing movements that can be captured in the optical data  110 ( 1 ) from the optical sensors. Optical data  110 ( 1 ) of the undulations may be used to infer pressure data  112 . In an example, when a person has facial swelling, the vibrations may be dampened by the pressure more rapidly than when such swelling is not present. 
     The plurality of sensors may also include orientation sensors, motion sensors, gyroscopes, other sensors, or any combination thereof. For example, as the person wears the VR headset  104 , the sensors may generate electrical signals proportional to movements of the person, which signals may represent motion data  114 ( 1 ). In some implementations, swaying movements when the user is standing still may differ from person to person; however, variations in a person&#39;s movements relative to baseline tests may be indicative of traumatic brain injury. 
     Further, in some implementations, the VR headset  104  may present information to the person, such as a list of words, an arrangement of objects, objects of different colors, and so on, and may instruct the person to memorize the information. Then, the VR headset  104  may present visual data and may monitor the eyes, facial area surrounding the eyes, or any combination thereof as the person observes the visual data. After presenting the visual data, the VR headset  104  may test the person&#39;s recall of the information to determine memory response data  116 ( 1 ). 
     In some implementations, the VR headset  104  may capture other data  118 ( 1 ). The other data  118 ( 1 ) may include differences between the measurement data and one or more baseline measurements. The other data  118 ( 1 ) can also include retinal data and other information. 
     In some implementations, the VR headset  104  or the computing device  106 ( 1 ) may include a processor configured to analyze the optical data  110 ( 1 ), pressure data  112 , motion data  114 ( 1 ), memory response data  116 ( 1 ), other data  118 ( 1 ), or any combination thereof to detect impairment and to produce data indicative of impairment  120 ( 1 ). For example, the processor may analyze the optical data  110 ( 1 ) to determine biometric data, which can be used to uniquely identify the person. Further, the processor may present data to the display and receive optical data  110 ( 1 ) as the person watches the data on the display. The processor may analyze the optical data  110 ( 1 ) to detect facial muscle movements, eye movements (rapid movements, tracking movements (smooth or otherwise), divergence, other eye movements, or any combination thereof), pupil reflexes, pupil shape, eye shape, swelling, retinal injury, and so on. The processor may further analyze the motion data  114 ( 1 ) to detect movement indicative of dizziness or imbalance. The processor may determine data indicative of impairment  120 ( 1 ) of the person  102 ( 1 ) based on the optical data  110 ( 1 ), the pressure data  112 , the motion data  114 ( 1 ), the memory response data  116 ( 1 ), other data  118 ( 1 ), or any combination thereof. 
     While a VR headset  104  may provide a self-contained testing apparatus for determining physiological state changes representative of cognitive impairment of a person  102 ( 1 ), it may also be possible to provide similar testing, obtain similar optical data  110 , and determine data indicative of impairment  120  using other devices. For example, smart glasses  130  may be worn by a person  102 ( 2 ) and may communicate with a computing device  106 ( 2 ) through a communications link  108 ( 2 ). The smart glasses  130  may be configured to present visual data to a display. The visual data may include objects that move, various colors, various intensities of light, and other data. In some implementations, the smart glasses  130  may present an augmented reality, such as by presenting the objects superimposed over visual objects in the real world. 
     The smart glasses  130  may include one or more optical sensors to capture optical data  110 ( 2 ) of the person  102 ( 2 ). The smart glasses  130  may also include one or more motion sensors (such as an inertial measurement unit (IMU) sensor) to generation motion data  114 ( 2 ). Further, the smart glasses  130  may present information to the display and instruct the person to remember the information. Subsequently, the smart glasses  130  may test the person&#39;s memory with respect to the information to determine memory response data  118 ( 2 ). The smart glasses  130  may also produce other data  118 ( 2 ). In some implementations, a processor of the smart glasses  130  or a processor of the computing device  106 ( 2 ) may analyze the data to determine data indicative of impairment  120 ( 2 ). 
     In another implementations, a device may include a wearable element  140  including a holder  142  configured to secure the computing device  106 ( 3 ) in front of the person&#39;s eyes. For example, the wearable element  140  may include a cap, and the holder  142  may extend from the cap and secure the computing device  106 ( 3 ) at a pre-determined distance from the person&#39;s eyes. The wearable element  140 , the holder  142 , or both may be adjustable to fit the person  102 ( 3 ) and to present the computing device  106 ( 3 ) at a selected distance from the person&#39;s eyes. In this example, the computing device  106 ( 3 ) may be a smartphone, a tablet computer, or other computing device with a display and sensors. 
     The computing device  106 ( 3 ) may present visual information to the person, and may capture optical data  110 ( 3 ), motion data  114 ( 3 ), memory response data  116 ( 3 ), and other data  118 ( 3 ). A processor of the computing device  106 ( 3 ) may analyze the optical data  110 ( 3 ), motion data  114 ( 3 ), memory response data  116 ( 3 ), and other data  118 ( 3 ) to determine data indicative of impairment  120 ( 3 ). 
     The data indicative of impairment  120  may be determined, for example, by comparing captured data to one or more thresholds. In some implementations, the thresholds may be determined by analyzing data collected from a plurality of persons  102 . Over time, a generalized average baseline measurement may be determined that may be used to determine impairment. Such impairments may include traumatic brain injuries (e.g., a concussion), chemical impairments or exposure to toxic substances, neurological diseases or infections, lifestyle factors, eye injuries, and so on. The processor may compare the captured data to the baseline and may determine impairment when the captured data deviates from the baseline by more than a threshold amount. Other implementations are also possible. 
     In some implementations, a person  102  may be initially tested, such as prior to injury, one or more times to determine a baseline for the person  102 . In some implementations, such data may be used to determine a biometric signature for the person  102 . The baseline may be associated with the biometric signature in a database, which may be stored on the device or on a server accessible through a computing network, such as the Internet. Subsequently, when the person  102  is tested, a biometric signature may be determined from the optical data. The biometric signature may be used to retrieve the baseline for the person  102 , and the captured data may be compared to the baseline to determine impairment when the captured data deviates from the baseline by more than a threshold amount. Other implementations are also possible. 
     The computing device  106  may retrieve the baseline for the person  102  from a local memory of the computing device  106 , from a memory of another computing device  106 , from a database accessible through a communications network (such as the Internet), or any combination thereof. 
     Various impairments may be determined based on the captured data. Such impairments can include head injury or traumatic brain injuries (such as concussions), chemical impairments (such as alcohol or drugs), injuries (such as retinal detachments), dehydration, other impairments, or any combination thereof. 
     It should be understood that a cognitive impairment (CI) includes a situation in which a person has trouble remembering, learning new things, concentrating, or making decisions. CI may not be caused by any specific disease/condition and is not necessarily limited to a specific age group; however, Alzheimer&#39;s disease, other dementias, Parkinson&#39;s disease, stroke, fatigue, traumatic brain injury, developmental disabilities, and other conditions may manifest as CI. Common signs of CI can include memory loss, change in mood or behavior, vision problems, trouble exercising judgment, and so on. The DSM- 5  (Diagnostic and Statistical Manual of Mental Disorders) now lists cognitive disorders as neurocognitive disorders indicating that there is some type of involvement of the brain. 
     Embodiments of the systems, devices, and methods described herein may provide optical data consistent with one or more cognitive evaluations (such as moving objects, item to be memorized, and so on) to a display. The systems, devices, and methods may capture optical data of the person&#39;s eyes and face as the person observes the data presented on the display. Such video data may be processed to detect a physiological state of the person. The physiological state may include physical conditions (e.g., dehydration, detached retina, swelling, and so on) and which may include CIs or neurological disorders. Some categories of types of CIs may include 1) Genetic Influences (such as Alzheimer&#39;s disease, Parkinson&#39;s disease, stroke, dementia, and so on); 2) Head Injury (such as a closed head injury, traumatic brain injuries (concussions, contusions, and so on), other head injuries, or any combination thereof); 3) Disease/Infection (such as Meningitis (from virus), Multiple Sclerosis (Autoimmune and attacks myelin), Parkinson&#39;s disease (dopamine producing cells die), AIDS (dementia from virus), Macular Degeneration, Retinal Detachment, other conditions, or any combination thereof); 4) Exposure to Toxic Substances (such as neurotoxins (lead, heavy metals, paint fumes, gasoline, aerosol), alcohol, drugs (legal and illegal), other toxins, or any combination thereof); and 5) Lifestyle Factors (such as malnutrition, dehydration, overheating (core body temperature, over exertion, etc.), other factors, or any combination thereof. 
     In one possible implementation, dehydration of a person may manifest as a physiological change that can be determined from the captured optical data. Symptoms may include feelings of confusion or lethargy, lack of urination for an extended period (such as for eight hours), rapid heartbeat, low blood pressure, weak pulse, inability to sweat, sunken eyes, and so on. In some instances, dehydration may also manifest as eye strain. Decreased lubrication and absence of tear production, tired eyes, blurred vision, headaches, and double vision are all symptoms of eye strain. Other optically detectable symptoms of dehydration are also possible, such as a change in skin elasticity relative to a baseline. In some implementations, the systems, devices, and methods may determine a change in skin elasticity relative to a baseline based on eye movements (and optionally damping of vibrations). 
     Dehydration can cause shrinkage of brain tissue and an associated increase in ventricular volume. The increase in BOLD (blood oxygen level dependent) response after dehydration suggested an inefficient use of brain metabolic activity. This pattern may indicate that participants may have exerted a higher level of neuronal activity in order to achieve an expected performance level. Given the limited availability of brain metabolic resources, these findings suggest that prolonged states of reduced water intake may adversely impact executive functions, such as visual-spatial processing which may include the ability to represent and mentally manipulate three-dimensional objects. Overheating may encompass dehydration and may have similar physiological manifestations. Other physiological states and other determinations may be made based on the video data, depending on the implementation. The systems, devices, and methods may determine the person&#39;s level of dehydration based on deviation of the persona&#39;s responsiveness relative to the baseline. 
     If a person is dehydrated or in a compromised state of hydration, the systems, methods, and devices may detect a physiological state that includes a change in rapid eye tracking of three-dimensional movement. The change may be relative to a standard baseline or relative to a baseline corresponding to the person. The baseline may be determined from a local memory of the computing device  106 , from another computing device  106 , from a database accessible through a communications network (such as the Internet), from another source, or any combination thereof. 
       FIG. 2  depicts a flow diagram of a process  200  of determining data indicative of a person&#39;s physiological state, in accordance with certain embodiments of the present disclosure. At  202 , data may be provided to a display. For example, visual data may be presented on a display of a VR headset  102 , smart glasses  130 , or a mobile computing device  106 . The data may include moving objects, information, varying colors, varying intensities of light and dark, other visual elements, or any combination thereof. 
     At  204 , optical data associated with a person&#39;s eyes and face around the person&#39;s eyes may be captured using a camera (or other optical sensor) while the person observes the data on the display. For example, one or more optical sensors integrated with the display device may capture optical data while the person observes the data on the display. In some implementations, other types of sensors may also be used. 
     At  206 , the optical data may be analyzed to identify physiological state changes representative of cognitive impairment or brain injury. For example, eye movements, divergence, pupil reflexes, pupil shape, eye shape, minute color changes, minute shape changes, facial movements, other data, or any combination thereof may be analyzed to detect information indicative of impairment. In a particular example, presentation of an object moving from far away toward a point between the user&#39;s eyes can be presented on the display, and divergence of the user&#39;s eyes can be determined to detect impairment. In another particular example, pupillary reflexes, a rate of change of the pupil size, variations in the pupil shape over time, or other measurements may be indicative of CI. Additionally, irregular or non-smooth eye movements may be indicative of CI. Other examples are also possible. 
     At  208 , data indicative of impairment may be sent in response to analysis of the optical data. In some implementations, the data indicative of impairment may be presented on a display of a computing device, such as a smartphone. In an example, the data indicative of impairment may include an email or a graphical interface, which may be sent to a computing device  106  or to another device. In some implementations, the data indicative of impairment may include an indication of the impairment and a basis for the determination, which may allow a physician to review the information. The data indicative of impairment may include the optical data (including, for example, magnification of selected pixels or subsets of image data values). Other implementations are also possible. 
       FIG. 3  depicts a block diagram of a system  300  including an analytics system  302  to provide neurological testing and analysis, in accordance with certain embodiments of the present disclosure. That analytics system  302  may be communicatively coupled to one or more computing devices  106  through a network  304 . The network  304  may include local area networks, wide area networks (such as the Internet), communication networks (cellular, digital, or satellite), or any combination thereof. 
     The analytics system  302  may include one or more network interfaces  306  configured to communicate with the network  304 . The analytics system  302  may further include one or more processors  308  coupled to the one or more network interfaces  306 . The analytics system  302  may include a memory  310  coupled to the processor  308 . The analytics system  302  may include one or more input interfaces  312  coupled to the processor  308  and coupled to one or more input devices  314  accessible by an operator to provide input data. The input devices  314  may include a keyboard, a mouse (pointer or stylus), a touchscreen, a microphone, a scanner, another input device, or any combination thereof. The analytics system  302  may also include one or more output interfaces  316  coupled to the processor  308  and coupled to one or more output devices  318  to display data to the operator. The output devices  318  may include a printer, a display (such as a touchscreen), a speaker, another output device, or any combination thereof 
     The memory  310  may include a non-volatile memory, such as a hard disc drive, a solid-state hard drive, another non-volatile memory, or any combination thereof. The memory  310  may store data and processor-executable instructions that may cause the processor  308  to analyze optical data and other data and to determine data indicative of impairment  120  for a person  102 . The memory  310  may include a graphical user interface (GUI) module  320  that may cause the processor  308  to generate a graphical interface including text, images, and other items and including selectable options, such as pull-down menus, clickable links, checkboxes, radio buttons, text fields, other selectable elements, or any combination thereof. The processor  308  may send the graphical interface to the output device  318 , to one or more of the computing devices  106 , or any combination thereof. 
     The memory  310  may further include an image analysis module  322  that may cause the processor  308  to receive image data from one or more of the computing devices  106 . The image analysis module  322  may cause the processor  308  to selectively process image values from the image data. For example, the image analysis module  322  may cause the processor  308  to analyze pixel color variations over time and to analyze other image data to determine various parameters. Further, the image analysis module  322  may cause the processor  308  to determine swelling, eye measurements, and other data. Other implementations are also possible. 
     The memory  310  can also include a biometrics module  324  that may cause the processor  308  to determine a biometric signature from the optical data. For example, the person&#39;s eye may be visually unique, and the visual data may be sufficiently unique to provide a biometric signature that may be used to uniquely identify the person. The biometric signature data may be stored as an identifier in a database, for example. 
     The memory  310  may further include an optical tests module  326  that may cause the processor  308  to send test data to one or more of the computing devices  106 . For example, the test data may include objects, object movements, memory testing items, other data, or any combination thereof. In some implementations, the computing device  106 ( 1 ) may provide the test data to the VR headset  104 . The computing device  106 ( 2 ) may provide the test data to the smart glasses  130 . The computing device  106 ( 3 ) may provide the test data to its display. Other implementations are also possible. 
     The memory  310  can also include an eye movement analysis module  328  that may cause the processor  308  to determine eye movement data from the optical data. For example, the eye movement analysis module  328  may determine smooth or irregular eye movements. Further, the eye movement analysis module  328  can determine divergence from the optical data. Other examples are also possible. 
     The memory  310  may further include a facial muscle movement analysis module  330  that may cause the processor  308  to determine muscle movements in the area around the person&#39;s eyes. For example, the facial muscle movement analysis module  330  may detect muscle twitches and other muscle movements. In some implementations, such muscle movements may provide insights related to neurological issues or impairments. Other implementations are also possible. 
     The memory  310  can also include a pupillary reflexes analysis module  332  that may cause the processor  308  to determine changes in the pupillary reflexes from the optical data. For example, exposure to varying intensities of brightness may cause the pupil to dilate or constrict, and pupil reflexes analysis module  332  may determine a rate of change of the pupil size, variations or irregularities in the pupil shape, or other parameters over time, which may be used to assess brain stem function. In some instance, abnormal pupillary reflex may be indicative of optic nerve injury, oculomotor nerve damage, brain stem lesions (such as tumors), and certain medications. The pupillary reflex analysis module  332  may be used to evaluate a person&#39;s health independent of any known impact or injury. Other implementations are also possible. 
     The memory  310  can also include a blood flow analysis module  334  that may cause the processor  308  to determine color variations in a time series of images, which color variations may be imperceptible to the human eye, but which may be indicative of capillary blood flow. For example, as blood flows into the capillary, the color values may change, and as blood flows out of the capillary, the color values may change again. Such changes may indicate the person&#39;s pulse and other information related to the person&#39;s pulse. Other implementations are also possible. 
     The memory  310  may also include a motion analysis module  336  that may cause the processor  308  to determine movement data associated with the VR headset  104 , the smart glasses  130 , or the computing device  106 . Such movement data may be indicative of dizziness or loss of balance. Other implementations are also possible. 
     The memory  310  can further include a pressure analysis module  338  that may cause the processor  308  to determine ocular pressure based on eye movements, such as vibrations or other movements, dimension data, other data, or any combination thereof. For example, the pressure analysis module  338  may detect undulations in a time series of image data. Other implementations are also possible. 
     The memory  310  may include a memory analysis module  340  that may cause the processor  308  to compare the person&#39;s responses to memory data presented to the person  102  to determine whether the responses match. For example, the graphical interface may display information, such as a list of words, a set of objects, or other information, and may instruct the person  102  to memorize the information. Subsequently, the graphical interface may test the recall of the person  102 . Short-term memory loss may be indicative of impairment. Other implementations are also possible. 
     The memory  310  can include a comparison module  342  that may cause the processor  308  to compare data received from the computing device  106  to one or more baselines  344  to determine a deviation from a baseline corresponding to the person  102 . For example, the analytics system  102  may retrieve a baseline associated with the person  102  based on biometric data determined by the biometrics module  324 . The analytics system  102  may then compare the data to the selected baseline and may determine impairment when the data deviates from the selected baseline by more than a threshold amount. Other implementations are also possible. 
     In some implementations, the analytics system  302  may receive image data from a computing device  106 , perform the image processing analysis to determine impairments, and send data indicative of impairment  120  to the computing device  106 . In other implementations, the analytics system  302  may process data received from the computing devices  106  to determine baselines  344  independent of a person  102 . In some implementations, the analytics system  302  may process the data over time to determine an average baseline and other data. In some implementations, data from multiple computing devices  106  may be analyzed to determine average baseline data and other parameters that can be used to diagnose neurological impairments and other information. Other implementations are also possible. 
       FIG. 4  depicts a block diagram  400  of a computing device  402 , in accordance with certain embodiments of the present disclosure. The computing device  402  may be an embodiment of the computing device  106  of  FIG. 1 . The computing device  402  may be a smartphone, a tablet computer, a laptop computer, another computing device, or any combination thereof. 
     The computing device  402  may include one or more power supplies  404  to provide electrical power suitable for operating components of the computing device  402 . The power supply may include a rechargeable battery, a fuel cell, a photovoltaic cell, power conditioning circuitry, other devices, other circuits, or any combination thereof 
     The computing device  402  may further include one or more processors  406  to execute stored instructions. The processors  406  may include one or more cores. Further, one or more clocks  408  may provide information indicative of date, time, clock flops, and so on. For example, the processor(s)  406  may use data from the clock  408  to generate a timestamp, to initiate a scheduled action, to correlate image data to data provided to the display, and so on. The computing device  402  may include one or more busses, wire traces, or other internal communications hardware that allows for transfer of data and electrical signals between the various modules and components of the computing device  402 . 
     The computing device  402  may include one or more communications interfaces  412  including input/output (I/O) interfaces  414 , network interfaces  416 , other interfaces, and so on. The communications interfaces  412  may enable the computing device  402  to communicate with another device, such as the analytics system  302 , other computing devices  402 , other devices, or any combination thereof through a network  304  via a wired connection or wireless connection. The I/O interfaces  414  may include wireless transceivers as well as wired communication components, such as a serial peripheral interface bus (SPI), a universal serial bus (USB), other components, or any combination thereof. 
     The I/O interfaces  414  may also couple to one or more I/O devices  410 . The I/O devices  410  may include input devices, output devices, or combinations thereof. For example, the I/O devices  410  may include touch sensors, keyboards or keypads, pointer devices (such as a mouse or pointer), microphones, optical sensors (such as cameras), scanners, displays, speakers, haptic devices (such as piezoelectric elements to provide vibrations or impulses), triggers, printers, global positioning devices, other components, or any combination thereof. The global positioning device may include a global positioning satellite (GPS) circuit configured to provide geolocation data to the computing device  402 . 
     The computing device  402  may include a subscriber identity module (SIM)  418 . The SIM  418  may be a data storage device that may store information, such as an international mobile subscriber identity (IMSI) number, encryption keys, an integrated circuit card identifier (ICCID), communication service provider identifiers, contact information, other data, or any combination thereof. The SIM  418  may be used by the network interface  416  to communicate with the network  304 , such as to establish communication with a cellular or digital communications network. 
     The computing device  402  may further include one or more cameras  420  or other optical sensor devices, which may capture optical data (images). For example, the cameras  420  may capture image data associated with a user automatically or in response to user input. Further, the computing device  402  may include one or more orientation/motion sensors  422 . For example, the orientation/motion sensors  422  may include gyroscopic sensors, accelerometers, tilt sensors, and so on. In some implementations, the orientation/motion sensors  422  may cause the processor  406  to alter the orientation of data presented to a display of the input/output interfaces  414  according to the orientation of the computing device  402 . In other implementations, the orientation/motion sensors  422  may generate signals indicative of motion, which may reflect dizziness or imbalance. 
     The computing device may include one or more memories  424 . The memory  424  may include non-transitory computer-readable storage devices, which may include an electronic storage device, a magnetic storage device, an optical storage device, a quantum storage device, a mechanical storage device, a solid-state storage device, other storage devices, or any combination thereof. The memory  424  may store computer-readable instructions, data structures, program modules, and other data for the operation of the computing device  402 . Some example modules are shown stored in the memory  424 , although, alternatively, the same functionality may be implemented in hardware, firmware, or as a system on a chip. 
     The memory  424  may include one or more operating system (OS) modules  426 , which may be configured to manage hardware resource devices, such as the I/O interfaces  414 , the network interfaces  416 , the I/O devices  410 , and the like. Further, the OS modules  426  may implement various services to applications or modules executing on the processors  406 . 
     The memory  424  may include a communications module  428  to establish communications with one or more other devices using one or more of the communication interfaces  412 . For example, the communication module  428  may utilize digital certificates or selected communication protocols to facilitate communications. 
     The memory  424  may include a test control module  430  to generate visual tests that may be provided to the display or that may be sent to the smart glasses  130  or to the VR headset  104 , depending on the implementation. The visual tests may include moving objects, information for memory testing, and other tests. The test control module  430  may control the content, the presentation (including timing), and may initiate operation of the one or more cameras  420  to correspond to presentation of the visual tests. 
     A camera control module  432  may control operation of the one or more cameras  420  in conjunction with the test control module  430  to capture optical data associated with the person&#39;s eyes and face surrounding the eyes. For example, in response to initiation of the visual test, the camera control module  432  may activate the one or more cameras  420  to capture optical data associated with the person. The optical data may include a time series of images of the person&#39;s eyes, the facial area that surrounds the eyes of the person, other image data, or any combination thereof that are captured during a period of time that corresponds to the presentation of the visual tests. 
     The memory  424  may further include an image analysis module  434  to determine parameters associated with the person&#39;s eyes and face. The parameters may include eye movement data, pupil reflexes data, pupil shape data, color variation data, facial movement data, eye shape data, blood flow data, and various other parameters. In some implementations, the image analysis module  434  may detect neurological impairment based on the parameters. 
     The memory  424  may further include a balance module  436  that may utilize orientation and motion data from the orientation/motion sensors  422  to determine balance data associated with the person  102 . For example, the balance module  436  may detect an impairment based on changes in the orientation and motion data over time, which may be indicative of dizziness or imbalance. Other implementations are also possible. 
     A baseline comparator module  438  may retrieve baseline data from the memory  424  or from the analytics system  302  and may compare the parameters associated with the person&#39;s eyes and face and the balance data to the baseline data. The baseline data may include one or more baselines associated with the person  102 . Alternatively, the baseline data may include an average baseline associated with multiple different persons. Other implementations are also possible. 
     An alerting module  440  may generate a graphical interface, an email, a text message, or another indicator to notify an operator of the impairment (or lack thereof) of the person. For example, the alerting module  440  may provide a popup notice to the display including data indicative of impairment of the person  102 . In another example, the alerting module  440  may send an email or text message to an administrator (such as a high school athletic director or medical personnel) including data indicative of impairment of the person  102 . Other implementations are also possible. 
       FIG. 5  depicts a block diagram  500  of a computing device  502  such as a VR device  104  or a smart glasses device  130 , in accordance with certain embodiments of the present disclosure. The computing device  502  may be an embodiment of the VR device  104  or the smart glasses  130  of  FIG. 1 . 
     The computing device  502  may include one or more power supplies  504  to provide electrical power suitable for operating components of the computing device  502 . The power supply may include a rechargeable battery, a fuel cell, a photovoltaic cell, power conditioning circuitry, other devices, other circuits, or any combination thereof. For example, the power supply may include a power management circuit configured to receive a power supply via a USB connection to a computing device  106 . Other implementations are also possible. 
     The computing device  502  may further include one or more processors  506  to execute stored instructions. The processors  506  may include one or more cores. Further, one or more clocks  508  may provide information indicative of date, time, clock flops, and so on. For example, the processor(s)  506  may use data from the clock  508  to generate a timestamp, to initiate a scheduled action, to correlate image data to data provided to the display, and so on. The computing device  502  may include one or more busses, wire traces, or other internal communications hardware that allows for transfer of data and electrical signals between the various modules and components of the computing device  502 . 
     The computing device  502  may include one or more communications interfaces  512  including input/output (I/O) interfaces  514 , network interfaces  516 , other interfaces, and so on. The communications interfaces  512  may enable the computing device  502  to communicate with another device, other computing devices  106 , other devices, or any combination thereof through a wired connection or wireless connection  108 . The I/O interfaces  514  may include wireless transceivers as well as wired communication components, such as a serial peripheral interface bus (SPI), a universal serial bus (USB), other components, or any combination thereof. 
     The I/O interfaces  514  may also couple to one or more I/O devices  510 . The I/O devices  510  may include input devices, output devices, or combinations thereof. For example, the I/O devices  510  may include touch sensors, pointer devices, microphones, optical sensors (such as cameras), displays, speakers, haptic devices (such as piezoelectric elements to provide vibrations or impulses), other components, or any combination thereof. In some implementations, the I/O devices  510  may include rocker switches, buttons, or other elements accessible by a user to activate and interact with the computing device  502 . 
     The computing device  502  may further include one or more cameras  518  or other optical sensor devices, which may capture optical data (images). For example, the cameras  518  may capture image data associated with a user automatically or in response to user input. Further, the computing device  502  may include one or more orientation/motion sensors  520 . For example, the orientation/motion sensors  520  may include gyroscopic sensors, accelerometers, tilt sensors, and so on. In some implementations, the orientation/motion sensors  520  may cause the processor  506  to alter the orientation of data presented to a display of the input/output interfaces  514  according to the orientation of the computing device  502 . In other implementations, the orientation/motion sensors  520  may generate signals indicative of motion, which may reflect dizziness or imbalance. 
     The computing device  502  may include one or more piezoelectric transducers  522 . The piezoelectric transducer  522  may be configured to vibrate or generate an impulse in response to electrical signals. For example, the piezoelectric transducer  522  may apply a vibration or pulse to the person&#39;s face, and the camera  518  may capture optical data including undulations of the person&#39;s skin, facial muscles, eyes, or any combination thereof in response to the vibration or pulse. In some implementations, the rate of decay of the undulations (or the distance traveled from the source) may be indicative of ocular swelling or pressure. Other implementations are also possible. 
     The computing device  502  may include one or more memories  524 . The memory  524  may include non-transitory computer-readable storage devices, which may include an electronic storage device, a magnetic storage device, an optical storage device, a quantum storage device, a mechanical storage device, a solid-state storage device, other storage devices, or any combination thereof. The memory  524  may store computer-readable instructions, data structures, program modules, and other data for the operation of the computing device  502 . Some example modules are shown stored in the memory  524 , although, alternatively, the same functionality may be implemented in hardware, firmware, or as a system on a chip. 
     The memory  524  may include one or more operating system (OS) modules  526 , which may be configured to manage hardware resource devices, such as the I/O interfaces  514 , the network interfaces  516 , the I/O devices  510 , and the like. Further, the OS modules  526  may implement various services to applications or modules executing on the processors  506 . 
     The memory  524  may include a communications module  528  to establish communications with a computing device  106  using one or more of the communication interfaces  512 . For example, the communication module  528  may utilize digital certificates or selected communication protocols to facilitate communications. 
     The memory  524  may include a test control module  530  to generate or otherwise render visual tests that may be provided to the display. The visual tests may include moving objects, information for memory testing, and other tests. The test control module  530  may control the content, the presentation (including timing), and may initiate operation of the one or more cameras  518  to correspond to presentation of the visual tests. 
     A camera control module  532  may control operation of the one or more cameras  518  in conjunction with the test control module  530  to capture optical data associated with the person&#39;s eyes and face surrounding the eyes. For example, in response to initiation of the visual test, the camera control module  532  may activate the one or more cameras  518  to capture optical data associated with the person. The optical data may include a time series of images of the person&#39;s eyes, the facial area that surrounds the eyes of the person, other data, or any combination thereof captured during a period of time that corresponds to the presentation of the visual tests. 
     The memory  524  may further include a piezoelectric transducer control module  534  to control the piezoelectric transducers  522  to produce the vibrations or impulses. For example, the piezoelectric transducer control module  534  may send an electrical signal to the piezoelectric transducer  522  to initiate a vibration or impulse, which may be applied to the person&#39;s face. 
     An orientation sensor control module  536  may control the orientation sensors  520  to determine orientation and motion changes. For example, as a person  102  moves around while wearing the computing device  502 , the orientation or motion data may be generated, which may be indicative of the dizziness or imbalance of the person. Other implementations are also possible. 
     The memory  524  may include an image analysis module  538  to determine parameters associated with the person&#39;s eyes and face. The parameters may include eye movement data, pupil reflexes data, pupil shape data, color variation data, facial movement data, eye shape data, blood flow data, and various other parameters. In some implementations, the image analysis module  538  may detect neurological impairment based on the parameters. Other implementations are also possible. 
     The memory  524  may further include a blood flow calculation module  540  to determine blood flow to the eyes and the facial area around the eyes based on color changes over time with respect to some of the image data. For example, the blood flow calculation module  540  may measure the person&#39;s heart rate and observe blood flow through capillaries in the skin based on color changes over time. Other implementations are also possible. 
     The memory  524  may also include a balance module  542  that may utilize orientation and motion data from the orientation/motion sensors  520  determined by the orientation sensor control module  536  to determine balance data associated with the person  102 . For example, the balance module  542  may detect an impairment based on changes in the orientation and motion data over time, which may be indicative of dizziness or imbalance. Other implementations are also possible. 
     A baseline comparator module  544  may retrieve baseline data from the memory  524 , from a computing device  106 , or from the analytics system  302  and may compare the parameters associated with the person&#39;s eyes and face and the balance data to the baseline data. The baseline data may include one or more baselines associated with the person  102 . Alternatively, the baseline data may include an average baseline associated with multiple different persons. Other implementations are also possible. 
     An alerting module  546  may generate a graphical interface, an email, a text message, or another indicator to notify an operator of the impairment (or lack thereof) of the person. For example, the alerting module  546  may provide a popup notice to the display including data indicative of impairment of the person  102 . In another example, the alerting module  546  may send an email or text message to an administrator (such as a high school athletic director or medical personnel) including data indicative of impairment of the person  102 . Other implementations are also possible. 
       FIG. 6  depicts a diagram  600  of optical test data that can be presented on one of the computing devices of  FIGS. 4 and 5 , in accordance with certain embodiments of the present disclosure. For example, the optical test data may be presented to a display of the VR headset  104 , the smart glasses  130 , and the computing device  106 . 
     In the illustrated diagram  600 , profiles  602  are shown, which represent the relative position of a pair of eyes being presented with different visual tests, which may be used to cause the eyes to move, the pupils to dilate, and so on. The cameras  518  may capture image data of the eyes  602  and the face of the person  102  as the person observes the visual data. 
     The person&#39;s eyes of the profile  602 ( 1 ) may be presented with a three-dimensional convergence test  606  in which an object  604  appears to move three-dimensionally toward the person&#39;s eyes. In this example, the object  604 ( 1 ) begins at a distance from the person&#39;s eyes and appears to move along the path  608 ( 1 ), growing larger as the object approaches, as illustrated by the object  604 ( 2 ). The convergence test  606  causes the object  604  to advance to a point between the person&#39;s eyes, while the camera  518  in  FIG. 5  or the camera  420  in  FIG. 4  captures optical data associated with the person&#39;s eyes. The optical data correlated to the position of the object in the convergence test  606  can be used to detect the distance at which the person&#39;s eyes diverge. In some implementations, the divergence may provide data indicative of impairment. 
     The person&#39;s eyes of the profile  602 ( 2 ) may be presented with a three-dimensional smooth tracking test  610  in which an object  604  moves along a path  612  from the object  604 ( 3 ) to the object  604 ( 4 ), growing and shrinking along the path to provide an appearance of three-dimensional motion. As the three-dimensional smooth tracking test  610  is provide to the display, the cameras  420  in  FIG. 4  or the cameras  518  in  FIG. 5  may capture optical data associated with the person&#39;s eyes. The optical data correlated to the position of the object in the smooth tracking test  610  can be used to detect irregular or non-smooth movement of the eyes, which may be indicative of impairment. 
     The person&#39;s eyes of the profile  602 ( 3 ) may be presented with a light and dark pupil reflexes and contraction test  616  in which the position, shape, color, intensity, or other parameters of one or more objects  622 ( 1 ) and  622 ( 2 ) may change over time as the background  620  also changes in color, intensity, and so on. In this example, an elliptical shape  622 ( 1 ) may be presented at a first position and a first time on a first background  620 ( 1 ) and a second rectangular shape  622 ( 2 ) may be presented at a second position at a second time and on a second background  620 ( 2 ). The changing background intensity may be received as changes in light by the pupils, causing the pupils to dilate or contract. As the test  616  is provided to the display, the cameras  420  of  FIG. 4 or 518  of  FIG. 5  may capture optical data associated with the person&#39;s eyes. The optical data correlated to the position of the object  622  in the test  616  together with the changing intensity (brightness) of the background  620  can be used to detect rates of pupil reflexes or contraction and irregular shaped pupils, one or more of which may be indicative of impairment. Other implementations are also possible. 
       FIG. 7  depicts a diagram of an eye-tracking test  700  that uses three-dimensional movement, in accordance with certain embodiments of the present disclosure. In this example, a three-dimensional space  702  is depicted, which may represent the visual data presented to the display of the VR headset  104 , the smart glasses  130 , or the computing device  106 . The eye-tracking test  700  may depict an object  704  that follows a path  706  within the three-dimensional space  702  changing sizes and color intensity. The object  704 ( 1 ) may thus have a larger size than the object  704 ( 2 ), which appears to be further away. 
     The visual information presented to the display may take a variety of forms. Such forms may include an eye test chart, with letters that get smaller with each row of the eye chart to detect blurry vision. Further, such forms may include moving objects, flashing objects, and so on. Rapid eye response may be tested by presenting objects in various locations and at various distances while the camera  420  in  FIG. 4 or 518  in  FIG. 5  tracks the person&#39;s eye movements. Other implementations are also possible. 
       FIGS. 8A-8C  depict view angles that may be used to determine impairment, in accordance with certain embodiments of the present disclosure. In  FIG. 8A , a view  800  is shown from above the person&#39;s head during a 3D convergence test. In this example, the left eye  802 ( 1 ) and the right eye  802 ( 2 ) are shown with a straight line of sight  806 ( 1 ) and  806 ( 2 ) respectively. The display may present an object  804  that appears to move from a distance away toward a point between the person&#39;s eyes  802  along an object path  808  that is perpendicular to the person&#39;s face (or to an imaginary line extending between and tangent to both of the eyes  202 ). 
     In a convergence test, the user&#39;s eyes  802 ( 1 ) and  802 ( 2 ) may adjust to follow movement of the object  804 , such that the left eye  802 ( 1 ) and the right eye  802 ( 2 ) may turn (rotate) toward the object  804  as the object  804  appears to move. In some implementations, when the angles of the eyes  802  diverge, the person may see double (e.g., two objects  804 ). In some implementations, divergence at a virtual distance of 10 centimeters or more may be indicative of a cognitive impairment. Some persons may have a baseline convergence at a distance that is less than 10 cm, and the baseline distance may be compared to a measured divergence to determine cognitive impairment. 
     In this example, the eyes  802  are turned toward the object  804  such that object tracking lines of sight  810 ( 1 ) and  810 ( 2 ) may vary from straight lines of sight  808 ( 1 ) and  808 ( 2 ) by left and right angles (α Left  and α Right ). The device may determine the angles from optical data of the person&#39;s face, which may be captured by one or more optical sensors as the person observes the moving object  804 . The device may determine a point at which the object tracking line of sight  810  of one of the eyes  802 ( 1 ) or  802 ( 2 ) diverges from the object  804 . If that point is at a virtual distance that is greater than 10 centimeters or that differs by more than a threshold amount from a baseline distance, the device may determine cognitive impairment. Other implementations are also possible. 
     In this example, the near point convergence is a linear distance from the eyes  802  to a location in depth at which the object  804  is reported to be doubled (e.g., the person sees two objects  804 ). The angles (α) of ocular rotation may be measured from straight ahead of the eyes  802 . In an example, the vergence angle may be equal to a difference between the left angle (α Left ) and the right angle (α Right ). 
     In  FIG. 8B , a view  820  from above the person&#39;s head is depicted showing the eyes  802  tracking an object moving to the right. As shown, rapid and smooth eye movements within a horizontal plane may be observed. The angles (α) of eye rotation may be measured from straight ahead of the eyes. The horizontal and vertical eye rotations may be treated separately. In this example, the left and right eye rotation angles (α Left  and α Right ) are depicted. 
     In  FIG. 8C , a view  840  from a side of the person&#39;s head is depicted showing the eyes  802  tracking an object moving up. The angles (α) of vertical eye rotation may be measured from a horizontal plane extending from the eyes (and represented by the straight line of sight  806 ). The vertical eye rotation angles (α Left  and α Right ) are depicted. 
     In some implementations, differences in the left and right rotation angles ( FIG. 8A or 8B ), differences in the light and right rotation angles ( FIG. 8C ), or any combination thereof may differ from a predefined threshold. Such differences may be indicative of CI. Alternatively, the rotational angles may be compared to baseline angles, and differences from the baseline may be indicative of CI. Other implementations are also possible. 
     In some implementations, it may be determined from studying baseline convergence and movement data that a generic baseline may be generated, which may be used to evaluate new persons who may not have their own baseline measurements. Deviations from the generic baseline values may indicate a possible injury or other issues indicative of potential cognitive problems. 
     In some implementations, optical data of the person&#39;s face and eyes, including the ocular rotation angles, may be determined as the person observes a moving object, which may move side-to-side, up-and-down, toward and away from the person&#39;s eyes, and so on. The object may be presented on a display of virtual reality goggles, smart glasses, a smartphone, or any combination thereof, and the optical data may be captured as the person observes the moving object. The system or device may determine the various angles, the divergence distance, and other eye and facial parameters based on the optical data. Variations in the angles or other facial parameters relative to a baseline associated with the person (or relative to average parameters determined across a plurality of persons) may be used to evaluate possible cognitive impairment of the person. 
       FIG. 9  depicts a system  900  to capture optical data of a person  102  as the person observes a three-dimensional moving object, in accordance with certain embodiments of the present disclosure. In this example, a tester  902 , such as a trainer, doctor, or another person, may present a moving object  904 . In this example, the moving object  904  may be a finger; however, other moving objects may also be used, such as a pen, a ball, and so on. The tester  902  may move the moving object  904  in three-dimensions in front of the person  902  and may use a computing device  106  to capture optical data associated with the person&#39;s eyes as the person  102  observes the moving object  904 . 
     In some implementations, the tester  902  may utilize the computing device  106  to confirm divergence test information, eye movement information, and so on. In this example, the computing device  106  may not present display data for observation by the person  102 , but rather may be used as a high-resolution camera to capture the optical data for use in determining whether the person  102  has a cognitive impairment. Other implementations are also possible. 
     The systems, methods, and devices described herein may be used in a clinical setting, such as in a doctor&#39;s office, or may be used in other venues, such as on a sideline at a sporting event. In some implementations, software may be downloaded onto a smartphone and a test may be administered directly by present information on the display of the smartphone while simultaneously capturing optical data of the person&#39;s eyes. In other implementations, software may be downloaded onto the smartphone and a first person may move an object around while capturing image data associated with the second person&#39;s eyes. In still other implementations, video of the person&#39;s eyes may be captured using another device and the video may be uploaded. The system may receive the image data and may process the image data against one or more baselines associated with the person, one or more thresholds, or any combination thereof to determine cognitive impairment. Other implementations are also possible. 
       FIG. 10  depicts an image  1000  including an image processing matrix  1004  and including elements or areas for analysis, in accordance with certain embodiments of the present disclosure. The image processing matrix  1004  may divide an image into rows and columns of subset of pixels or image values. Each pixel or image value may represent an intensity in two or more dimensions, such as a red/green/blue (RGB) color spectrum where each pixel has a value within a range of 0-255×0-255×0-255 (or 256×256×256=16,777,216 possible combinations). The number of pixels or image values within each cell  1006  of the matrix  1004  may vary, depending on the implementation. 
     In this example, subsets of the pixels or image values may be selected for further processing. In this example, a first area  1008  includes a selected subset of pixels or image values for facial muscle movement analysis. A second area  1010  includes a selected subset of pixels or image values for eye tracking analysis. A third area  1012  includes a selected subset of pixels or images values for pupil shape and reflexes analysis. 
     The captured optical data may include information that is not perceptible to the naked eye, but which may be clearly discerned by the processors. For example, transient color changes that can be detected in the optical data may be imperceptible to human vision, but nevertheless may be used to review information about the person. Such transient color changes may represent blood flowing through capillaries in the eyes and surrounding facial tissue. Further, small tremors in the eye movements may not be perceptible to the naked eye but may represent irregular or non-smooth eye movements. Further, divergence can be accurately determined based on correlations between eye movements and the apparent position of the object presented to the display. In some implementations, the processors may be configured to amplify such small color differences, movements, or other changes to render those difference or changes sufficiently to be seen by a user, such as a physician or trainer. Such amplified differences, movements, or changes may be used to determine one or more conditions of the person. Other implementations are also possible. 
       FIG. 11  depicts a flow diagram of a method  1100  of determining impairment based on optical data, in accordance with certain embodiments of the present disclosure. The method  1100  may be implemented on the computing device  116 , the analytics system  302 , the computing device  402 , the computing device  502 , or any combination thereof 
     At  1102 , optical data associated with a person is received. The optical data may include images of the person&#39;s eyes and facial area surrounding the person&#39;s eyes. The optical data may be received from a camera  420 , from the VR device  114 , or from the smart glasses  130 . 
     At  1104 , the optical data may be processed to detect eye movement, muscle movement, pupil reflexes, eye shape, pupil shape, blood flow, and other parameters. For example, the optical data may be processed to detect smooth eye movement while the person&#39;s eyes are tracking a moving object, or to detect divergence as an object moves toward a point between the person&#39;s eyes. Further, color changes over time may be processed to determine blood flow, and so on. 
     At  1106 , a biometric signature may be automatically generated for the person  102  based on the optical data. The eyes may provide a biometric signature that is unique, at least to the same degree that a fingerprint is considered unique. Accordingly, the optical data may be used to produce a biometric signature that can uniquely identify the person  102 . 
     At  1108 , one or more baselines corresponding to the person  102  may be retrieved from a data store using the biometric signature. The one or more baselines may include optical data from previous tests, which may reflect the person&#39;s good health or varying degrees of impairment. In an example, a person  102  may be tested when he or she is healthy to produce a healthy baseline. Subsequently, the patent  102  may be tested and the optical data may be compared to the healthy baseline to detect impairment (or to a recent test indicating impairment to determine improvement). Other examples are also possible. 
     At  1110 , data corresponding to the optical data may be compared to one or more baselines. For example, the optical data (or data determined from the optical data) may be compared to a baseline retrieved from a database. Other implementations are also possible. 
     At  1112 , if a difference between the optical data and the baseline is greater than a threshold, impairment may be determined based on the difference, at  1114 . It is understood that small variations may exist between tests, and the threshold is used to prevent the small variations from triggering a determination of impairment. Other implementations are also possible. 
     At  1116 , an output indicative of the person&#39;s neurological condition is sent. For example, the output may indicate that the person has a neurological impairment, such as a concussion, a chemical impairment, another cause of impairment, or any combination thereof. In some examples, dehydration of the person  102  may also be reflected in the optical data. Other implementations are also possible. 
     Returning to  1112 , if the difference is less than the threshold, no impairment is determined, at  1118 . In an example, if the optical data matches or is similar enough to the baseline, the optical data may be indicative of a healthy person. At  1116 , an output indicative of the person&#39;s brain condition can be sent. In this instance, the output may indicate that the person  102  is healthy. Other implementations are also possible. 
       FIG. 12  depicts a flow diagram of a method  1200  of determining impairment based on optically detected ocular pressure, in accordance with certain embodiments of the present disclosure. The method  1200  may be implemented on a system including a VR headset  104  and an associated computing device  106 ( 1 ) or on smart glasses  130  and an associated computing device  106 ( 2 ). Other implementations are also possible. 
     At  1202 , a piezoelectric element may be caused to vibrate. For example, a current may be applied to the piezoelectric element to cause vibration or an impulse. 
     At  1204 , optical data of a person&#39;s eyes and face may be captured before, during, and after vibration of the piezoelectric element. For example, vibration of the piezoelectric element may cause undulations of the person&#39;s facial muscles and eyes, which can be detected in the optical data. 
     At  1206 , the optical data may be processed to determine ocular pressure based on movement of the eyes and face. In one possible implementation, the rate of decay of the undulations may be indicative of ocular pressure, swelling, or other parameters. Other implementations are also possible. 
     At  1208 , data indicative of the person&#39;s brain condition or physiological state changes may be generated based in part on the determined ocular pressure. In one example, the data may indicate that the person  102  does not have a concussion. In another example, the data may indicate brain swelling or ocular swelling, which may be indicative of a concussion. Alternatively, the data may be indicative of another condition, such as dehydration, illness, or another condition. Other implementations are also possible. 
       FIG. 13  depicts a flow diagram of a method  1300  of determining impairment based on motion and orientation data, in accordance with certain embodiments of the present disclosure. The method  1300  may be implemented on a system including a VR headset  104  and an associated computing device  106 ( 1 ), on smart glasses  130  and an associated computing device  106 ( 2 ), on the computing device  106 ( 3 ), on the analytics system  302 , on the computing device  402 , on the computing device  502 , or any combination thereof. 
     At  1302 , motion and orientation data of a person  102  may be determined while the person observes a visual test. For example, the motion and orientation data may be determined by motion analysis module  336  of the analytics system  302 . In another example, the motion and orientation data may be determined from orientation/motion sensors  422  or from motion/orientation sensors  520 . 
     At  1304 , the motion and orientation data may be processed to detect motion indicative of imbalance. For example, relatively rapid changes in motion or orientation may indicate dizziness or imbalance. An unimpaired person  102  may produce motion or orientation data that is substantially stable, while an impaired person  102  may produce time-varying motion or orientation data indicative of instability. 
     At  1306 , the motion and orientation data optionally may be compared to one or more baselines. The baselines may be indicative of prior measurements of the person  102 . In an alternative, the baselines may be indicative of average measurements of a plurality of persons  102  over time. Other implementations are also possible. 
     At  1308 , data indicative of the person&#39;s brain condition may be generated based, at least in part, on the determined motion and orientation data and optionally the comparison. In some implementations, the data indicative of the person&#39;s brain condition (such as a concussion or other impairment) may be determined based on the motion and orientation data by itself, which may indicate that the person&#39;s balance is off In other implementations, the motion and orientation data (i.e., the person&#39;s movements, tilt angles, and other movement information) may be compared to a baseline associated with the person  102  to determine the person&#39;s physiological state changes representative of cognitive impairment. In still other implementations, the motion and orientation data may be compared to a baseline that may represent an average determined from the motion and orientation data from a plurality of persons. Other implementations are also possible. 
     In conjunction with the systems, methods, and devices of  FIGS. 1-13 , visual data may be presented to a display for viewing by a person, and optical sensors (such as a camera) may product optical data associated with the person&#39;s eyes and facial area surrounding the eyes. The optical data may be processed to determine a neurological impairment. In some implementations, data indicative of impairment may be sent to a computing device. 
     In some implementations, sensors including optical sensors, pressure sensors, temperature sensors, or other sensors may provide signals that may be processed to determine various parameters associated with the person. Such parameters may be compared to threshold or may be compared to baselines associated with the person to determine deviations that may be indicative of traumatic brain injury or cognitive impairment. 
     Although the present invention has been described with reference to preferred embodiments, workers skilled in the art will recognize that changes may be made in form and detail without departing from the scope of the invention.