JP2015500042A - Systems and methods for facilitating analysis of brain injury and damage - Google Patents

Abstract

The present disclosure, in an exemplary embodiment, includes, based on user test performance data, at least one performance variable that characterizes stability of balance and posture, and at least one performance variable that characterizes vestibular oculomotor reflex (VOR). A computer system is provided that includes a processor configured to calculate. For each performance variable, the processor can calculate a respective score based on the respective performance variable and also based on a set of performance metrics. The at least one calculated score can be output via the output device.

Description

== Cross-reference to related applications ==
This application is filed on Oct. 3, 2011 under the name of the invention “SYSTEM AND METHOD FOR MOTOR AND COGNITIVE ANALYSIS”, US Provisional Application No. 61 / 542,548 ( The entire content of which is incorporated herein by reference. This application includes US Provisional Patent Application No. 61 / 262,662 filed on November 19, 2009 and PCT / US2010 / 057453 filed on November 19, 2010 (the entire contents of which are hereby incorporated by reference). Related to the book).

  The invention described herein relates primarily to systems and methods for facilitating analysis of brain injury and damage.

  There are various types of neurological movement disorders and neurological cognitive disorders. Examples include Alzheimer's disease, Parkinson's disease (PD), and progressive supranuclear paralysis. Neuromotor impairment and neurocognitive impairment affect motor function, cognitive function, or both. Another category of conditions that affect motor and cognitive function is traumatic brain injury. These brain injuries can include mild traumatic brain injury, also commonly referred to as concussion. Concussions can be graded on various scales.

  To properly treat brain damage as well as many neuromotor and neurocognitive disorders, it is desirable to better understand or classify an individual's condition. For this reason, different tests have been developed for different types of diseases and injuries. For example, one of the scales for evaluating a patient's Parkinson's disease is the Unified Parkinoson's Disease Rating Schedule (UPDRS). There are also a variety of other tests that clinicians use to help categorize patient dysfunction.

  In order to more efficiently manage and objectively analyze the results of such tests, computerized systems for performing some of these tests have been proposed. US Pat. No. 6,435,878 (“the '878 patent”) proposes a system that can measure a user's response time to a stimulus. However, the proposed system does not measure the quality of user interaction with the system. In addition, the system of the '878 patent dynamically modifies the stimulus presentation time or stimulus quality based on the user's performance, but qualitatively determines the test difficulty based on the user's performance. I will not correct it.

  US Pat. No. 7,294,107 (“the '107 patent”) similarly refers to a test execution system that can measure a user's reaction time. However, like the '878 patent, this system does not measure the quality of user interaction with the system. The '107 patent system also determines which tests are to be performed and whether or not to end the test based on the user's results, but the difficulty of a specific test is determined based on the user's results. Do not qualitatively correct the degree.

  US Pat. No. 6,517,480 (“the '480 patent”) refers to a test execution system that detects a maze trace and detects the overall time to complete the test. Does not qualitatively measure the user's test results and does not modify the test difficulty in light of the user's results.

  Furthermore, none of the '878,' 107, and '480 patents provide a system or method for testing the degree of cognitive ability on a time basis, and the medication and / or patient Nor does it provide a system or method for presenting data relating to correlation between patient information such as stimulation parameters used for deep brain stimulation (DBS) and test results.

  The present invention relates to systems and methods for facilitating analysis of brain injury and damage.

  According to an exemplary embodiment, the computer system may include at least one performance variable characterizing balance and posture stability and at least one performance variable characterizing vestibular oculomotor reflex (VOR) based on user test performance data. And a processor configured to calculate. For each performance variable, the processor may calculate a respective score based on the respective performance variable and also based on a set of performance metrics. The at least one calculated score may be output via the output device.

  In another exemplary embodiment, a computer-implemented method uses a computer processor to create a performance variable, vestibular oculomotor reflex (VOR), that characterizes balance and posture stability based on user test performance data. It may include calculating at least one of the grading performance variables. For each of at least one performance variable, a processor may calculate a respective score based on the respective performance variable and also based on a set of performance metrics. The processor may output at least one calculated score via the output device.

  In yet another example, a non-transitory computer readable medium can store instructions executable by a processor. This instruction, when executed by the processor, causes the processor to perform a method for assessing the extent of the user's traumatic brain injury. The method corresponding to this instruction is based on the test results data of the user, a performance variable characterizing balance and posture stability, a performance variable characterizing cognitive function, a performance variable characterizing neuromotor function, vestibular ocular reflex (VOR) And calculating at least one of the performance variables characterizing. For each of at least one performance variable, a respective score can be calculated based on the respective performance variable and also based on a set of performance metrics. The at least one calculated score can be output via an output device to facilitate diagnosis of the user's traumatic brain injury.

  In some examples, the measurement results may relate to a function affected by traumatic brain injury, such as mild traumatic brain injury (eg, commonly referred to as concussion). These types of traumatic brain injury can be graded on various scales to indicate severity. In an example of analyzing concussion-type injury or other diseases (such as Parkinson's disease), balance and posture stability and / or vestibular function may be tested in addition to neuromotor and cognitive functions. The invention also relates to a system and method for displaying information derived from background measurement results. Such analysis and / or resulting display can facilitate a health care provider, such as a physician, to diagnose the patient's condition.

  In another example, the present invention also relates to a system and method for detecting a patient's intentional poor performance and predicted response when performing a test. The present invention also relates to a system and method for determining posture stability.

  Exemplary embodiments of the present invention relate to systems and methods for displaying information derived from measurements of vestibular, balance, and posture stability functions. In some examples, this information may be displayed as a cobweb graph having multiple vertices, each vertex corresponding to a score or index value. Each score / index value may be derived from a measurement of posture stability and vestibular oculomotor reflex (VOR) integrity.

  Exemplary embodiments of the present invention relate to a system and method for determining whether a patient is intentionally performing poorer than expected or predicting a correct response when performing a test.

  Exemplary embodiments of the present invention relate to a system and method for determining posture stability using a combination of gyroscope measurement results and accelerometer measurement results.

  For ease of use and access, one or more tests can be accessed by an authorized user at a remote location using a predefined resource locator (eg, URL) It may be implemented as an application. Also, by implementing the system as a web-based application, it is possible to maintain test data for multiple patients on a central server (anonymously) for easier analysis and research. For example, the results of testing with multiple users can be further collected to create a new index for classifying motor impairment or determining the severity of motor impairment, which is the commonly used Parkinson's disease It may (or may not) correlate with existing standards such as the Unified Parkinoson's Disease Rating Schedule (UPDRS).

  In an exemplary method, the display device may be part of a patient terminal (eg, a handheld device) and the measurement data is recorded on a server connected to the patient terminal via a network; The test result information is output from a clinician's terminal connected to the server via the network. In some examples, the network includes the Internet.

  The various method and system components disclosed herein may be implemented and provided alone or in various combinations.

  Exemplary embodiments of the invention relate to one or more processors, which may be implemented using any conventional processing circuitry and devices, or combinations thereof. The processor is a personal computer for executing the given code on a computer readable hardware medium, including a conventional memory device, for example, which can be used alone or in combination with any of the methods described herein. It may be a central processing unit (CPU) of a computer (PC) or other workstation processor. One or more processors may be implemented in a server or user terminal, or may be implemented in combination. User terminals may include, for example, desktops, laptops, handheld devices, personal digital assistants (PDAs), television set-top Internet appliances, mobile phones, smartphones, etc., or a combination of one or more of these. The memory device may include conventional permanent storage circuits and / or primary storage circuits, or combinations thereof, including a non-comprehensive list of random access memory (RAM), read only memory (ROM), compact disc ( CD), digital versatile disc (DVD), and magnetic tape.

  Exemplary embodiments of the present invention may be performed by a processor to perform the methods described herein and / or store output data generated by performing the methods described herein. It relates to a computer readable medium in hardware, for example as described above, in which instructions are stored.

  An exemplary embodiment of the invention relates to a method, such as a hardware component or machine, that is executed by a processor to send instructions for performing the methods described herein.

1 is a block diagram of a test and analysis system, according to an illustrative embodiment of the invention. FIG. 2 illustrates a system architecture that can be implemented for testing and analyzing motor and cognitive functions according to exemplary embodiments of the present invention. 4 is a screen shot of a login screen, according to an illustrative embodiment of the invention. FIG. 4 shows a patient questionnaire that can be presented to a patient or clinician user according to an exemplary embodiment of the present invention. FIG. FIG. 4 shows a screenshot of another portion of a questionnaire that can be presented to a patient or clinician user, according to an illustrative embodiment of the invention. 2 illustrates a graphical user interface (GUI) that can be used to calibrate a remote test device, according to an illustrative embodiment of the invention. FIG. 4 illustrates a GUI of a screenshot of an introductory screen that can be used to initiate a test, according to an illustrative embodiment of the invention. FIG. 6 illustrates a screenshot GUI that can be used to provide instructions for a “Sevens Test”, according to an illustrative embodiment of the invention. FIG. 6 illustrates a “Sevens Test” GUI that can be implemented on a computer, according to an illustrative embodiment of the invention. FIG. FIG. 10 illustrates the “Sevens Test” GUI of FIG. 9 indicating that the test has been completed by the user, according to an illustrative embodiment of the invention. FIG. FIG. 4 shows a screenshot GUI that can be used to provide instructions for reaction testing, according to an illustrative embodiment of the invention. FIG. 5 illustrates a computer-implemented reaction test GUI according to an exemplary embodiment of the present invention. FIG. 13 shows the GUI of the reaction test of FIG. 12 at least partially completed by a patient user, according to an exemplary embodiment of the present invention. FIG. 6 illustrates a screenshot GUI that can be used to provide instructions for a computer-implemented trail making test (Part A), according to an illustrative embodiment of the invention. FIG. 4 illustrates a computer-implemented trail making test (Part A) GUI, according to an illustrative embodiment of the invention. FIG. 16 shows the GUI of the trail making test (Part A) of FIG. 15 partially completed by a patient user, according to an exemplary embodiment of the present invention. FIG. 6 illustrates a screenshot GUI that can be used to provide instructions for a computer-implemented trail making test (Part B), according to an illustrative embodiment of the invention. FIG. 4 illustrates a computer-implemented trail making test (Part B) GUI, according to an illustrative embodiment of the invention. FIG. 19 shows the GUI of the trail making test (Part B) of FIG. 18 partially completed by a patient user, according to an exemplary embodiment of the present invention. FIG. 4 illustrates a trail making test being performed on the surface of a tablet personal computer (PC) by a patient user, according to an illustrative embodiment of the invention. FIG. 6 illustrates test results that can be displayed to a user via an administrative user interface, according to an illustrative embodiment of the invention. FIG. FIG. 22 illustrates the administrative user interface of FIG. 21 representing an example of obtaining data used to evaluate a patient user's neural cognitive function according to an exemplary embodiment of the present invention. FIG. 4 shows speed data that can be calculated for a trail making test (Part A), according to an illustrative embodiment of the invention. FIG. FIG. 24 shows an enlarged view of a portion of the test of FIG. 23 that can be generated based on data obtained from a patient user according to an exemplary embodiment of the present invention. FIG. 6 shows dwell time data calculated for multiple targets for trail making test (Part A) and trail making test (Part B) testing, according to an illustrative embodiment of the invention. FIG. 6 shows a screenshot of another administrative user interface that can be used to view and select patient test data, according to an illustrative embodiment of the invention. FIG. 5 illustrates another administrative user interface used to manage patient protocols employed in performing tests, according to an illustrative embodiment of the invention. FIG. FIG. 6 illustrates a computing environment available to implement the systems and methods described herein, according to an illustrative embodiment of the invention. 1 is a block diagram of an analysis engine of a test and analysis system, according to an illustrative embodiment of the invention. FIG. 4 is a flowchart illustrating a method for displaying concussion-related performance metrics according to an exemplary embodiment of the present invention. 4 is a set of cobweb graphs corresponding to exemplary results from a series of tests performed on a first patient, according to an exemplary embodiment of the present invention. 4 is a set of cobweb graphs corresponding to exemplary results from a series of tests performed on a second patient, according to an exemplary embodiment of the present invention. It is a set of measurement results obtained using a conventional accelerometer. A set of measurement results obtained using a conventional gyroscope is shown. It is a flowchart which shows the method for calculating | requiring stability of an attitude | position based on what combined the data of the gyroscope and the data of the accelerometer. FIG. 6 is a flow chart illustrating a method for detecting an intentional poor performance and potential for prediction by a patient undergoing a test. FIG.

  FIG. 1 illustrates an example of a test system 10 that can be implemented in accordance with an aspect of the present invention. The system 10 includes a test engine 12 that includes methods and functions utilized to obtain data related to the test execution process performed by the system 10. In the exemplary embodiment, test engine 12 may be located on a server to which a user's computing device can connect for remote access to the methods and functions of test engine 12.

  Test engine 12 may include, for example, a patient information module 14. The patient information module 14 is programmable to obtain patient information that can be provided to the analysis engine 16 in the form of patient data 18. Patient information may include a series of templates or forms (eg, XML or other documents) that can be completed by a user using a suitable user input device such as a keyboard and / or mouse and / or stylus. Examples of patient information include login information for authenticating a user with the system, such as a login ID and a password. Patient information may also include information regarding the environment in which the patient is being tested as well as information regarding the patient's current health status. Additional information can be obtained regarding the medications the patient is currently taking (including medication name, dose, number of doses taken per day, time since last medication). Those skilled in the art will appreciate that there are various types of configurations and configurations that can be used to obtain patient data 18 and transmit it to analysis engine 16.

  The test engine 12 also includes a calibration module 20. This calibration module 20 is programmed to calibrate the system, such as for remote operation where the specific content of the user's test facility may not be known. The calibration component 20 can be used, for example, to ascertain the two-dimensional relative size of the display or monitor on which the test is performed. For example, a graded scale (given with a corresponding normalization scale known to the programmer) such as an array of lines spaced from each other can be graphically represented on the display. Depending on the size of the image displayed to the user via the graphical user interface 22, the user can position adjacent to the scale to see the dimensions of the user's display (eg, in both x and y directions). An article of known size (eg, 8.5 inch x 11 inch paper, dollar bill, or credit card) may be presented to the user. The user can then enter the corresponding score into the system 10 as calibration data 24 utilized by the analysis engine 16. In this way, once the relative display size of the user's computing device is known, appropriate geometry and position information can be obtained in a series of subsequent tests. The analysis engine 16 can utilize the calibration data 24 to scale the corresponding test execution data so that it matches the user's particular test environment (eg, display screen) being tested. . In particular, data presented to the user for tests and / or test results may be scaled based on calibration data. For example, the accuracy (ie size) of the displayed targets and / or the distance between the displayed targets may be modified based on Fitz's law depending on the user's test environment.

  In another exemplary embodiment of the present invention, calibration may be performed by sending data to present three or four targets, one at each corner of the screen, on the user's screen. The system may record the number of pixels between targets, or the predetermined X, Y position, as a representation of the vertical and horizontal distance between targets. These distances can be used as calibration data for scaling the test data.

  Alternatively or in addition, certain patient devices can be pre-configured to have known settings so that calibration can be omitted. For example, a device having a predetermined setting may be registered in the system. For example, such pre-registered devices or terminals can be installed in a doctor's office, hospital, or other facility.

  In another exemplary embodiment, differences between display screens may be ignored, and test data presented to the user as well as test results may be available on various test platforms.

  The test engine 12 also includes a plurality of test applications (eg, functions or methods) 26 and 28 indicated by TEST 1 application to TEST N application (where N is a positive integer representing an arbitrary number of tests). Including. Each test application is programmable in such a way as to test the user's motor and / or cognitive functions and / or combinations of motor and cognitive functions.

  For example, the test can be used to test the user's proper motor function and / or neural cognitive function ("seven's test") (details are described below with reference to FIGS. 9 and 10). Trail making tests such as trail making tests Part A and Part B, clock drawing tests, reaction times and exercise tasks, and similar to known tests. Other tests that can program the test engine 12 include center-out tests (evaluating information processing speed (eg, determining which target to move) and motor ability (eg, quality of movement for a particular target)), Archimedes spiral Test (to evaluate Parkinson's tremor, as the patient goes outward, the tremor will increase), Benton's line orientation test (measures the visuospatial judgment of patients with brain injury), A test that provides full rendering dynamically when the user takes the test. For example, a circular tracking test may be provided in which a pattern for tracking by the user is provided, including points that are rendered when the user begins to trace the pattern. For example, a sine wave, circle, or other pattern that changes or moves as the user traces the pattern may be displayed.

  Each test application 26-28 can be provided to a user computer in an interactive manner that provides an interactive graphical object to the GUI 22 on a hardware display device such as a computer or tablet screen. Such interactivity can be achieved by using action scripts or other functions or methods that can be provided to the user, some of which, for example, can cause the test engine 12 to be based on calibration content as described above. It can be changed according to the platform to be implemented.

  In an exemplary embodiment, test engine 12 can be implemented using a Flash platform, such as one programmable using ADOBE® FLEX® software available from Adobe Systems Incorporated, for example. is there. The Flash platform, for example, is very popular in the market and does not require any additional software components to be installed on the user's machine, such as when accessing the test engine 12 remotely via a web browser on the user's machine, There are advantages such as. Conveniently, the FLEX® application for each component of the application or method 14, 20, 26, 28 is a stateful client that can change on the display without having to load a new web page. May be provided. In addition, with such an implementation, we can analyze the test results of user's movement, cognition, and cognitive-motor function and provide meaningful information so that the geometrical shape data and position data to be acquired can be provided. We already know that it will be possible to fully elucidate.

  Data for each of the test applications 26 to 28 is acquired as test data 30 to 32, respectively. These are provided to and / or available to the analysis engine 16. Thus, by performing a plurality of multipart tests 26-28, each test can provide corresponding test data 30-32 that can be analyzed by the analysis engine 16, and based on the test data 30-32, Can provide meaningful information and results.

  Test data includes content indicating the location of a graphical object for testing (eg, a target) and content indicating the location of a cursor or other pointing device used to perform the test, It may include content indicating which tests are being performed. For example, the test applications 26 to 28 adopt get # cursor # pos () or other application programming interface (API), and the test data 30 to 32 includes time information (time corresponding to the obtained cursor position) and the like. The cursor position information stored together can be monitored and acquired. The sampling rate of such data may be 30 Hz, for example.

  For example, the test application 26, 28 may have one or more targets on the display, each containing a set of coordinates on the user's display device, or a shape (eg, circle, ellipse, triangle). Or a graphical object having a rectangular) can be displayed. The user can use a pointing device such as a mouse or touch screen stylus, or touch the touch screen with a finger without using a pointing device, etc. A cursor or other graphical object can be positioned such that it has a unique object position within a space having a Y coordinate. The test application 26, 28 can provide instructions to the user to position the cursor / object or to draw a line between two or more specific targets. The analysis engine 16 can analyze the movement of the cursor on the screen relative to the known position of each target (corresponding to the test data 30-32).

  The analysis engine 16 may include, for example, a motion calculation unit 34 and / or a recognition calculation unit 38. The calculators 34 and / or 38 may be, for example, software modules stored on a computer readable hardware device that perform various calculations based on the same or different input parameters. May be.

  In the exemplary embodiment, motion calculator 34 is programmed to determine a number of one or more kinematic variables based on test data 30-32. For example, the motion calculator 34 can be programmed to determine the velocity, acceleration, speed, and / or tangential acceleration for each sample of test data acquired during the test interval, as well as the position of the cursor or pointer device. For example, tangential acceleration may be used by the analysis engine 16 as an indication of the degree of curvature in the user's movement.

  In the exemplary embodiment, the motion calculator 34 is, for example, whether a correspondence to a measure of how close to the user's line between a pair of targets fits an ideal straight line between the pair of targets, etc. Information derived from the variables referred to above can also be obtained. For example, for each data point on an ideal straight line between sequential targets, the distance to the corresponding point on the line drawn by the user can be determined. It is possible to find the sum of the distances determined between each point and divide the distance by the number of points determined to provide an average associated with the ideal line over the line drawn by the user . This can be repeated for the line drawn by the user between each target in order to objectively indicate the accuracy of the drawn line.

  In the exemplary embodiment, the motion calculation unit 34 may further calculate the mean square error by obtaining an average of errors obtained by squaring the difference between the ideal line and the actual line. In the exemplary embodiment, motion calculator 34 further calculates a mean square error or standard deviation of the difference between the ideal line and the actual line, for example by calculating the square root of the mean square error. May be. In this manner, the motion calculation unit 34 can obtain various values representing the average distance that the user data points on the user line deviate from the ideal line.

  Similarly, in an exemplary embodiment, for each position recorded during the test, for each separate X and Y position, and / or for each line, between speed, velocity, acceleration, tangential acceleration, etc. The mean, mean square error, mean square root error, and / or standard deviation information may be calculated as the deviation between the actually recorded values of these parameters and the ideal values.

  Other information includes whether or not the user has drawn lines that intersect each other as described later with reference to FIGS. 15 and 16.

  The above is not intended to be a comprehensive list of calculations that the motion calculator 34 may perform; in other exemplary embodiments, acquired test data sampled over time. Based on 30-32, additional or alternative variables and parameters are calculated. The calculation result obtained by the motion calculation unit 34 can be stored as a part of the analysis data 36. Analytical data 36 may also include calibration data 24 and patient data 18. These can be used to improve the accuracy of calculations by the motion calculation unit 34 and the cognitive calculation unit 38. Alternatively, the calibration data can be used as described above so that test results performed on different platforms can be compared without further consideration of differences between platforms as reflected in the calibration data 24, etc. The tests performed may be changed.

  In an exemplary embodiment of the invention, cognitive calculator 38 is programmed to calculate variables or parameters related to the assessment of the patient user's cognitive function. For example, the recognition calculation unit 38 can calculate the dwell time based on the acquired test data 30 and 32. The dwell time corresponds to the time when the cursor or other graphical object is in a specific predefined bounded area that can be defined as the X and Y positions, for example, or in a range that includes the displayed graphical object or target. Also good. The calculated dwell time may be used, for example, to assess the patient's ability to switch sets to refocus from one task to another. In this case, the dwell time reflects the dwell time at the first target before moving to the next target (after the first movement to the first target). Dwell time may be an indicator of “cognitive freezing” in a neurocognitive patient group or other patient group. The recognition calculation unit 38 can also calculate a reaction time corresponding to the time from the presentation of the stimulus to the start of the movement of the pointing device by the user between the reaction test applications 26 and 28, for example. It is also possible to create a score for the information processing ability of the patient by further using the calculation unit 38. In an exemplary embodiment of the invention, the cognitive calculator 38 further utilizes data output by the motor calculator 34, such as, for example, data output representative of motor function quality, to calculate data representing cognitive ability. May be.

  For example, the initial speed or acceleration when leaving a particular target to move to the next target may be used as a cognitive measure in a particular case. For example, if the first stage of motion is relatively fast (high speed and acceleration) and the user moves to the correct target, this information can be used to predict that the user ’s motion is mainly predictive or feedforward It may be concluded that it was done under control (ie, it was very clear where the user was going). On the other hand, if the speed or acceleration is relatively low or the user moves or stops multiple times after leaving the target, this information can be used to determine where the patient is going to move. You may conclude. This indicates a lack of information processing speed, especially when other components of the user's movement are relatively normal or can be made relatively quickly. It should be understood and appreciated that depending on the primary purpose of the test, certain types of calculations may not apply to different types of tests.

  In an exemplary embodiment of the invention, analysis engine 16 can output the calculation results and provide corresponding analysis data 36. For this reason, the analysis data 36 is obtained based on the method and calculation performed by the motion calculation unit 34 and / or the cognitive calculation unit 38 based on the test data 30 to 32 acquired for each test application. It is possible to include.

  In an exemplary embodiment of the invention, analysis engine 16 is programmed to calculate one or more indices based on the output results determined for each patient by motion calculator 34 and / or cognitive calculator 38. The index calculation unit 40 may be included. For example, the index calculation unit 40 collects analysis data required for a specific set of test data acquired for a specific patient, and determines the motor function of the specific patient based on the collected test data set. An index (or score) having the indicated value can be determined. Alternatively or additionally, the index calculator 40 may calculate an index (or score) having a value indicating the patient's cognitive function based on a set of test data. Alternatively or in addition, the index calculator 40 may calculate an index (or score) having a value indicating the patient's cognitive-motor function based on a set of test data. The index calculator 40 can be normalized according to a known scale or index such as UPDRS. Alternatively or additionally, the index calculator 40 can calculate a new scale indicative of the patient's motor function and / or neural cognitive function. The output obtained for each index calculation can be supplied and stored as part of the analysis data 36 for later analysis, such as by a clinician who has access to the stored analysis data 36.

  In an exemplary embodiment of the invention, the system may modify the coefficients used in the index calculation based on a corpus of data for multiple patients. For example, if the results of a particular test for a large number of users deemed to be nearly healthy are not good, the test results for that test may be corrected with a smaller weighting factor.

  In an exemplary embodiment of the invention, when a user takes one or more tests, the system analyzes data 36 indicating that the one or more tests presented to the user are too difficult or too simple for the user. Can be generated. For example, if the calculated scores are very small, these scores may indicate that the user's ability level is below that particular threshold, even though the user can indicate that the user is below that particular ability threshold level. Not shown. Similarly, if the calculated scores are very large, these scores may indicate that the user's ability level is above a certain ability threshold level, but the ability of the user above that particular threshold The level is not shown in detail.

  Thus, in an exemplary embodiment of the invention, the test engine 12 accesses stored analysis data for the current patient, and based on past performance indicated by the accessed analysis data, The next output further includes a module for selecting one of the test applications 1-N. For example, if the test engine 12 determines from the analysis data that the user's performance is below a predetermined threshold, the test engine 12 performs the next test that is ranked at a particular low difficulty level. You may choose. For example, rank the difficulty according to target accuracy (ie, the size of the displayed target (for calibrated screen size, etc.)) and / or the distance between displayed targets (for, eg, calibrated screen size). Also good. For example, a first size test, such as having targets at a first distance from each other, having a plurality of targets at a second distance greater than the first distance from each other and / or smaller than the first size. It may be ranked as easier than another test of the same type that has multiple targets that are two in size. In an exemplary embodiment, a test difficulty change may be implemented by changing the target accuracy and distance and re-running the same type of test as previously performed (and thus re-executed). The difficulty level of the test changes).

  In another exemplary embodiment, changing the difficulty of the test by changing the movement state of at least one target and re-running the same test or the same type of test as previously performed. May be implemented. For example, to increase the difficulty level, the target may be changed from a stationary target to a moving target, or may be changed from a slowly moving target to a fast target. Similarly, in order to lower the difficulty level, the moving target may be changed to a stationary target, or the fast moving target may be changed to a slower target. You may rank difficulty according to a movement state.

  In another exemplary embodiment, changing the test difficulty may be implemented by selecting different types of tests. This test type is ranked at a different difficulty level than previously performed tests.

  In an exemplary embodiment of the invention, when performing a test, analysis engine 16 may generate a portion of analysis data 36 associated with the test, even before the test is complete. At the time of testing, the test engine 12 may access the partial analysis data 36 created for the test before the test is completed, and based on the partial analysis data 36 when the current test is performed. You may modify the test. For example, when performing a test, the test engine 12 may enlarge the previously displayed target of the test and / or reduce the distance between the previously displayed targets and / or move. The target may be changed between a state and a stationary state. Alternatively or in addition, if the test dynamically displays the target as it is run, the test engine 12 may display a new target that is larger than the previously displayed target pair. It may be displayed at a distance shorter than the distance between them or in a moving state different from the target displayed in the past.

  FIG. 2 shows an example of a network system 50 that includes an exemplary architecture for performing testing, analysis, and / or evaluation. In the example of FIG. 2, the system 50 includes a system server 52 that is programmed to provide methods and functions used to implement various methods remotely from a user device indicated by 54, 56, 58. User devices 54, 56, 58 are each connected to or can communicate with system server 52 via network 60. Network 60 may include a local area network (LAN), a wide area network (WAN) (eg, the Internet), or a combination of networks, including private and public domains as known in the art.

  In the exemplary embodiment, system server 52 may include a web server having a plurality of different functions and methods, each accessible via a corresponding resource locator, such as a uniform resource locator (URL). In one example, the system server 52 includes an access control function 64 that provides a level of security so that only authorized users can access various other functions and methods of the system. The access control function 64 can provide a login user interface screen for each of the user devices 54, 56, 58, where a user ID and password for authentication can be requested for each user. Each user ID and password may be associated with a corresponding authorization level to selectively provide access to one or more of the other functions and methods provided by server system 52. In FIG. 2, devices 54, 56, and 58 are shown as separate devices, but each operation may be performed on a single device, which is logically separated along with login information. May be. In an exemplary embodiment, a set of login information may authorize access to two or more operations of the illustrated devices 54, 56, 58.

  For example, the user device 54 can be assigned a high or unrestricted authorization level and includes the functions and methods provided by the server system 52, including accessing corresponding management functions and methods schematically illustrated at 67. Can be utilized to provide one or more administrative user interfaces 66 for accessing each of these. For this reason, the user who is given the authority of the management user interface 66 can access various management functions 67 regarding patient information while browsing the test data and the like. For example, the user interface 66 may be a clinician's interface that allows clinicians to access test results or other relevant information for tests taken by their patients. Examples of the selected management user interface screen are shown in FIGS.

  The user device 56 may include a patient user interface 68 that may provide a limited amount of access, such as test execution functions and methods 70. For example, after logging in via the access control function 64, the patient application interface 68 can be used to access the test application 70. This application can be displayed graphically in the patient GUI. The test application can be provided as an interactive web page programmed for functions and methods for performing various tests and obtaining patient-controlled movement information from the patient user.

  The test method 70 provided to the patient user device 56 via the patient user interface can be implemented using market-proven ADOBE® FLEX® or another similar software or platform. Because it is well penetrated into the market, virtually no software needs to be installed or loaded on individual user devices. In response to the interaction with the test provided by the test method 70, the test data 30-32 may be obtained by the system server 52 for storage in the central data store 74 (details are described below). The method of analysis engine 16 may be performed locally on patient user device 56 or remotely on system server 52.

  Examples of relevant graphical user interfaces associated with test execution functions and methods that can be presented to the user are illustrated in and described with reference to FIGS.

  Another user device 58 may include a research user interface 72 that provides access to relevant data (eg, excluding patient identification information) to facilitate study and analysis of test data, for example. For example, a researcher or other authorized user can access a set of test data for multiple patients and thus apply a statistical method or other mathematical manipulation to the set of data. Relevant information such as correlation or likelihood can be confirmed. The researcher can also use the analysis data to derive a correlation with other information (for example, patient data including drug ID, dosage, etc.) input by the user for the test results of each of the plurality of users. Such analysis can provide information that can be stored in the central data store 74 for later use and review by other authorized users. For example, a correlation between a pharmaceutical product and a test result and a change over time of the test result can be derived, and this correlation can be presented to an authorized user such as a doctor via the management user interface 66.

  Alternatively or in addition, a specific patient device can be preset in a preset authorization state so that authorization is not required. Such devices known to the system server 52 side are accessible to the system server 52 via the network 60 or via a secure local area network or other suitable connection. For example, such pre-configured terminals can be placed in a clinic, hospital or other facility.

  In the exemplary embodiment, regardless of the settings or distribution of patient user devices 56, the test data is integrated into a database or other central data store 74 associated with central system server 52.

  Central data store 74 may include raw test data 76 and result data 78. Although the central storage device 74 is illustrated as a single storage device and / or logical storage location, in the exemplary embodiment, raw test data 76, for example, reduces response time to results data queries. Therefore, the result data 78 may be stored separately.

  Raw test data 76 and result data 78 can be indexed by patient and individual tests, and patient specific information (example implementations) for the purpose of distinguishing patient data of one patient from that of another patient. The form may include (excludes patient identification information, possibly other than the patient's unique identification number). As described herein, test method 70 and system server 52 are programmable to perform calculations on raw test data obtained from a patient user interface via a test execution application implemented therein.

  Similarity between patients in a particular cluster (ie, patients with specific characteristics) can be used to facilitate treatment and diagnosis of other patients in similar conditions. For example, the clinician may provide patient information in terms of information (eg, pharmaceutical (type and / or dose), stimulation parameters (eg, DBS therapy), symptoms, condition, and / or diagnosis via the administrative user interface 66. ), Which can be tagged (or programmatically linked) to patient test data and result data, for example, to augment or provide metadata. This metadata can be further evaluated or reviewed to facilitate patient clustering and understanding of each condition. In this way, a more statistically significant group of test data can be maintained for statistical analysis of the test data, which can be drilled down, for example via a researcher user interface, or otherwise statistical You can evaluate it or do something else to understand the correlation between symptoms and condition. For example, the system of the present invention may be capable of querying test result data by one or more symptoms and / or diagnosis, in response, the system matches one or more symptoms and / or diagnosis Result data 78 may be returned for the patient and / or the average of the result data 78 and / or other aggregate data.

  In an exemplary embodiment, the system and method of the present invention uses the user device 54 to determine the pharmaceutical (type and / or dose) and / or for a particular patient for which patient information and test results are being obtained, for example. It may be possible for the clinician to input proposed changes for stimulation parameters (eg DBS therapy). In response to a query triggered by a user selection of a command at user device 54, system server 52 may store patient data and test data similar to a particular patient's patient data and test data (in a predetermined degree). Related patients may be retrieved in the central data store 74. The server may further retrieve patients that have been modified similar to that proposed for a particular patient and patients for whom subsequent test result data is available. The server outputs an average of such subsequent test results for display on the user device 54 to inform the clinician of the expected change due to the proposed change in a particular patient condition. It may be shown as a change measured in view of the assumed change in.

  Alternatively or additionally, the system may output a medical condition category that corresponds to the average of such subsequent test results. For example, different interval test scores may be associated with different categories of cognitive and / or motor skills in memory. A category including the average of subsequent test results may be output. Alternatively or in addition, an assumed change direction, such as whether cognitive and / or athletic skills are assumed to improve or worsen, may be output to the pathology category.

  FIGS. 3 through 27 are presented in the user interface to provide a general understanding of the algorithms and functionality that can be implemented in the systems 10 and 50 shown and described with reference to FIGS. An exemplary screenshot or other graphics for doing so is shown.

  FIG. 3 shows an example screenshot 100 that includes a GUI element 102 that can be used to control access to the system as described above in detail. The GUI element 102 includes a user input field 104 that can be used to obtain a user name and access code for authorized use of the system. A graphical interface element 106, such as a graphical button, may be provided to submit or clear information in the user input field 104.

  FIG. 4 shows an example screenshot 110 that includes an example of a GUI element 112 for obtaining information associated with the user's overall health, the psychological state being tested, and the environment. For example, the questions include: “How many hours of sleep did you get last night?”, “How many fatigue levels are 0-10?” (What is “Your level of fatigue on a scale of 0-10?”, “On a scale from 1 to 5 how noisy is your environment?” , "Where are you currently taking the test right now?" Associated with this or other questions may be a drop-down context menu 114 that is available to the user to identify and select one of a predetermined number of answers. After entering answers to one or more questions, the user can press the “continue” user interface element (graphical button) 116 to proceed.

  FIG. 5 shows another example screenshot 120 that can be used to obtain information about medications that a particular patient can take. Screenshot 20 includes a GUI element 122 with a wide variety of drop-down context menus that include one or more medications (medication (s), doses, and one or more medications per day). Identify the number of times per day the medication (s) is taken and the time (s) since last dosage of the medication (s) It is available to do. After entering details for a particular pharmaceutical in the drop-down context menu 124, the user may enter them into the system with an add user interface element 126. Similarly, an entry can be deleted or deleted by selecting the entry with the cursor or other user interface element and then pressing the delete user interface element 128. After all medications have been properly entered into the medication form GUI element 122, the user can proceed to the next phase of the test execution process by pressing a user interface element or button 130. As described herein, correlation can be obtained by associating pharmaceutical information with test data programmatically. In an exemplary embodiment, some or all of the medication and / or other treatment information may be entered into the system by the clinician.

  FIG. 6 illustrates an example screenshot 134 representing a calibration GUI 136 that can be implemented to horizontally calibrate a remote user's computing device, according to an illustrative embodiment of the invention. The calibration GUI 136 presents the user with a scale 138 having a plurality of spaced apart marks or marks. In the example of FIG. 6, serial numbers from 0 to 30 are assigned to the marks or marks. The calibration GUI 136 folds an 8.5 inch × 11 inch piece of paper in half, places one long side of the folded piece of paper to zero, and places the short end adjacent to the scale 138. The user is instructed to enter the number closest to the other long side in the user input dialog box 140. The number entered in this dialog box 140 can be used in comparison with the actual size of the paper to determine the size or size of the display area presented on the user's screen during the test execution process. A similar calibration is available for the vertical direction of the user screen so that the horizontal and vertical dimensions are known so that the results of the test run can be properly scaled.

  Figure 7 shows a “welcome” that can be presented to the user to inform the individual that the test is about to begin and to identify some additional information about the type of test and how they will proceed. welcome) "screenshot. It should be understood that some practice screens and tests may be implemented to allow the user to become familiar with the test execution process before starting the actual test.

  FIG. 8 shows an example of an instruction GUI 146 that can be given before performing the first test. 9 and 10 show an example of a GUI 150 that includes a plurality of targets 152 that are used to perform a “Sevens test”. In this test, in order to test cognitive and / or motor skills, the user is instructed to draw a path similar to the number “7” between targets. Each target can be graphically configured as a graphical object that encompasses an area represented by the X / Y coordinates of the patient's graphical interface, such as within a screen. The GUI 150 corresponds to an example of a conventional “Sevens test” in which a user is instructed to connect points using a pointing device such as a mouse, a stylus, or a touch screen. In the example of FIG. 9, three targets with numerals 1, 2, and 3 are presented on the screen. The user is instructed to connect with the target 1-2-3 and draw a shape similar to the number “7”. The system may record test data in association with test results. The test data may include, for example, location and time information of paths taken to tie the target. FIG. 10 shows an example of a patient outcome where target positions 1 and 2 are tied, but target positions 2 and 3 are not tied. To show that target locations 1 and 2 are tied correctly, the system highlights targets 1 and 2 while target 3 is not highlighted. Returning to FIG. 1, the test data obtained in FIG. 9 may include the ID of the location of each target, the path taken by the patient to connect or attempt to connect the target, and / or time information associated with this path.

  Thus, the information obtained regarding the test outcome shown in FIG. 10 is not only the coordinates of each target 152, but also during the test, the cursor or other pointing element moves between the respective targets and the corresponding line. Alternatively, the coordinates (or position) of the cursor or other pointing element when forming the path 154 may be included.

  FIG. 11 shows an example of an instruction GUI 158 that can be provided before performing the second test.

  12 and 13 show examples of GUIs 160 that can be provided in connection with performing a continuous motion reaction time test, such as a center-out test to test cognitive abilities and the like. Thus, the GUI 160 includes a plurality of targets, including a central target 162 and a plurality of outer targets 164 that are in a relationship surrounding the central target. The center-out test displays one of the outer targets 164 in a color that contrasts with the other targets 162 and 164, and then the contrasting target is displayed on the GUI 160 before making this contrast. By storing the time until the user begins to move the pointing device to connect the outer target 164 and the central target 162, it can be done to test the user's reaction time. During this process, additional information such as the path taken by the user between the central target 162 and the outer target 164 and / or the cursor over time for each graphical rendering of the target representing the corresponding time. Position, etc. can be obtained.

  FIG. 13 shows a partially completed center where one of the targets 166 is a color that contrasts with the other target and shows the assumed target connecting the central target 162 and the contrasting target 166. Indicates an out test. The center-out test can be performed such that different ones of the outer targets are selected one or more times in a predictable or random order.

  FIG. 14 is an example of another instruction GUI 168 that can be presented to the user to provide instructions for performing a third test, including those illustrated and described with reference to FIGS. 15 and 16. Show.

  15 and 16 show an example of a test GUI 170 that can be used to perform a trail making test (Part A) for testing cognitive skills and / or motor skills. The trail making test (Part A) implemented in the GUI 170 can be used to evaluate both exercise information and cognitive information simultaneously. For example, a plurality of targets 172 are dispersed in the display area provided by the GUI 170. In the example of FIGS. 15 and 16, the targets are numbered from 1 to 24, and the user connects the targets in numerical order (when instructed by the instruction GUI 168 of FIG. 14). The test engine can connect the targets in the numerical order to each other by an ideal straight line without intersecting the display area of the GUI 170 with the line connecting the other targets in the numerical order. Targets can be gathered pseudo-randomly. Thus, in addition to obtaining position, velocity, speed and / or acceleration information, lines that intersect each other are also identifiable to further indicate the patient's motor and cognitive functions.

  FIG. 16 shows an example in which the patient starts with target 1 and connects the first five targets in the order of target 2, target 3, target 4, and target 5 with lines. Thus, from the exemplary tests of FIGS. 15 and 16, information corresponding to the position of the cursor used to draw each line connecting the numbered targets is used as test data (for example, Can be recorded and stored in memory (local or associated with the server). In addition to position data, temporal data can also be obtained for each sample as well. Thus, as shown and described in detail below, position data and time data can be provided to the analysis system as test data for evaluation.

  Another example shown in FIG. 16 is to calculate, for example, the average error, the mean square error, or the mean square error square root for each of a plurality of lines interconnecting the targets 172 that are sequentially numbered by the GUI 170. FIG. 5 is a diagram of lines used for analysis that can be performed to characterize the degree of error. For example, referring to targets 2 and 3, this analysis is performed by the patient (eg, a pointing device with a pointing device) between the same respective targets, eg, along the ideal straight line 250 connecting targets 2 and 3. This can be done by comparing the relative position with respect to the line segment 252 drawn (in response to movement). For example, the same number of equally spaced sample points can be collected along the length of each of line segments 250 and 252 and the corresponding mean square error calculated for the difference between the sets of sample points. For example, the error values recorded at the sample points can be squared and summed, and then divided by the number of sample point pairs to give the mean square error for the ideal line segment 250 of the patient line 252. To calculate how close the user's line 252 fits to the ideal (straight) line 250 between targets, for example, sample mean, sample variance, analysis of variance, mean square error square root, Those skilled in the art will understand and appreciate that various types of estimators are available, including standard deviation and linear regression techniques.

  For example, by taking the square root of the mean square error of each line segment between the targets, the mean square error square root can be calculated by further utilizing the obtained mean square error. Thus, the mean square error square root can essentially provide a measure of the average distance from the corresponding point on the ideal line 250 to the user's data point on line 252.

  FIG. 17 is an example of an instruction GUI 180 that can be provided to issue instructions to a user for a trail making test (Part B) test that tests cognitive and / or motor skills, as shown in FIGS. 18 and 19. Indicates.

  18 and 19 show an example of a GUI 182 that can be presented to the user regarding performing and recording information related to the trail making test (Part B). The GUI 182 presents a plurality of targets located in the display area based on application data obtained by the corresponding test application. In the example of FIGS. 18 and 19, the targets are circles, each of which defines a bounded region having a corresponding set of coordinates. In the display GUI 182, a continuous letter from A to H is attached to a part of the target indicated by 184, and a consecutive number from 1 to 13 is assigned to another corresponding part of the target indicated by 186. Yes. The test engine is programmable to automatically create any arrangement of targets that matches the format of the trail making test (Part B), and this arrangement is part of the test data provided to the analysis engine 16. Those skilled in the art will understand that this may be the case.

  Exercise information and cognitive information may be evaluated simultaneously using a trail making test (Part B) implemented with GUI 182. For example, the instructions (using the instruction GUI 180 in FIG. 17) indicate that the user patient has started with the smallest number and then the second smallest number, as shown up to the letter E in FIG. For example, by connecting each target with a straight line, such as the second character, etc., it is specified to follow the consecutive characters and numbers alternately. Thus, FIG. 19 shows an exemplary outcome of a test using a cursor that allows a user to track position with a corresponding API. The system is configured to dynamically render a graphical representation of the line on the display GUI 182 in response to cursor movement by a corresponding pointing element, such as a mouse, stylus, or touch screen. The location related information and the location related time (representing the time for each movement) can be recorded for later analysis and evaluation as described herein.

  FIG. 20 illustrates an exemplary embodiment in which a user computing device for testing is implemented as a tablet personal computer (PC). Thus, in this example, the user has a stylus (like a pen) on the corresponding touch screen to draw a line connecting the targets as shown in FIG. It should be understood that the user may use his / her fingers to draw the connecting lines.

  FIG. 21 shows an example of analysis data that can be created and displayed in the GUI 190 as a function of the test data acquired in each test. The obtained analysis data can be presented in a wide variety of formats, and the format may be selected by the user. The GUI 190 shows a plurality of different plots for representing different information that can be calculated based on the location obtained for a particular test and the respective time data. For a particular test, each plot in the top row shows position information, the plot in the middle row shows velocity information, and the bottom row shows acceleration information. The position data can be correlated with the corresponding velocity information and acceleration information by analyzing changes in the X and Y coordinates of the cursor over time obtained from the specimen recorded during the test. In the example of FIG. 21, the GUI indicates analysis data of the “Sevens test” as illustrated and described in FIGS. The GUI 190 can be displayed via the administrative user interface 66 or the researcher user interface 72 for analysis and evaluation by authorized users.

  As an example, plot 192 shows the relative position of a graphical object based on user input on a corresponding pointing device that represents the path taken by the user between the “Sevens Test” targets by showing X position versus Y position. It is shown as a pair of connected line segments in the system. A representative plot 194 shows X data for plot 192 plotted as a function of time, and plot 196 shows Y data for plot 192 plotted as a function of time.

  Plot 198 shows velocity information corresponding to changes in the X and Y positions plotted in plot 192 over the time that the change occurred, ie during the test. The plot 200 shows the velocity in the X direction with respect to time, for example by taking the change between the plotted positional points in the plot 194 (over the plotted time at which such a change occurs). Similarly, plot 202 shows the velocity in the Y direction with respect to time, for example by taking the change between the plotted positional points in plot 196 (over the plotted time at which such a change occurs). Yes.

  Similarly, FIG. 23 shows a velocity plot 230 as a function of time (cm / sec) obtained from data acquired in a trail making test (Part A) performed in accordance with an aspect of the present invention. An enlarged view of the portion 232 of the waveform formed by the plot 230 is shown at 240 in FIG.

  Referring again to FIG. 21, another set of plots 204, 206, 208 shows the acceleration of each curve. For example, plot 204 displays an acceleration versus time plot, for example, by taking the change in velocity value of plot 198 (over the corresponding time at which such a change occurs). Plot 206 corresponds to the display of acceleration versus time in the X direction, for example by taking the change in velocity value of plot 200 (over the corresponding time at which such a change occurs). Similarly, plot 208 displays a plot of acceleration versus time in the Y direction, for example by taking the change in velocity value of plot 202 (over the corresponding time at which such a change occurs).

  Another data regarding movement in each of the X and Y directions, ie one or more of the plots 194, 196, 200, 202, 206, 208, for example, may be used to characterize skills in different directions, for example. Good.

  As described above, the index calculator 40 may calculate a score that characterizes the performance of the person taking the test for one or more tests performed. In an exemplary embodiment of the invention, the analysis engine 16 may take the shape of the entire curve of one or more types of graphs shown in FIG. 21 plotted, for example, as velocity and / or acceleration, You may compare. For example, the plotted velocity or acceleration may be compared to a stored smooth bell-shaped curve. This bell-shaped curve may be considered as representing the ideal movement of a healthy person when taking the test. The analysis engine 16 may score the movement of the person taking the test so that the closer the shape of the graph is to the shape of the stored graph, the higher the score. Similarly, the analysis engine 16 may determine the degree (in terms of number and / or degree) that one or more graphs contain spikes. In this case, such spikes may be used as an indication of significant and / or many low-quality movements (including corrective and / or tremor-like movements).

  For example, the index calculator 40 may calculate an index using one or more graph shape scores, which may be stored and output via the management user interface 66. Note that the index may be based on a number of factors. In an exemplary embodiment, different factors (eg, including graph shape scores) may be multiplied by their respective weight values, eg, depending on the significance ranked against the overall index. Good.

  In yet another example, FIG. 22 is a reproduction of the GUI 190 shown in FIG. 21, where selected portions of position data and kinematic data are used by the cognitive calculator 38 to calculate dwell time. Identified by a circle 220 corresponding to the relevant data available at. For example, the dwell time may correspond to the amount of time that a user-controlled graphical interface element, such as a cursor, is within a bounded area, such as a defined boundary of the target. Such bounded regions can be identified when testing the data according to position data (eg, X and Y coordinates) for each target gathered in the test GUI. The identified area for which the dwell time is calculated is to identify (for example, from plot 198) the X position and Y position corresponding to each time when no change has occurred in the X position and the Y position or a period without speed. And can be identified.

  FIG. 25 shows exemplary plots 2050 and 2052 of dwell time that can be calculated for the trail making test (Part A) and the trail making test (Part B), respectively. The horizontal axis corresponds to the number of targets displayed in the trail making test, and the vertical axis corresponds to the percentage of the overall time relative to the time that the cursor stayed at the corresponding target in the test performed. As described above, the dwell time corresponds to the time that the cursor / pointing object is within a certain pre-defined XY position range that includes the displayed target. In this way, the dwell time is predetermined in position information (eg, indicating that the cursor is within a bounded target) and velocity information (eg, the cursor is not moving or in a bounded target). (Indicating that it is moving slower than the threshold). It will be obvious that by calculating and analyzing the corresponding exercise information, the motor function information can be acquired simultaneously with the cognitive data expressed in the dwell time. For example, in PD patients, tremor predominates when the patient is resting. That is, if the patient moves his hand deliberately, for example, the tremor is slight, if any, while the patient's hand is not deliberately moving and is basically in a resting position. Significant tremors can occur at a constant frequency of 3-8 Hz. Thus, the dwell time information can be used as an indicator of which data is significant for measuring tremor in PD patients. For example, the system may determine the degree of tremor from data corresponding to a location where the requested dwell time is at a position where the user's hand is basically resting. This simultaneous motion information can be used to assess motor function (especially if the patient has tremor, for example, the patient may have some minor movement within this time), while this Cognitive functions in time (eg, related to information processing and set switching) are also analyzed.

  In an exemplary embodiment of the invention, the system and method are used to perform specific tests that are used to measure only motor functions related to, for example, finger tapping or tapping between two points. Data may be obtained by implementation. In an exemplary embodiment of the present invention, the system and method are used to perform specific tests that are used to measure only cognitive functions, such as, for example, a mental state brief test or a Levin progressive matrix test. You may get

  FIG. 26 shows an example of a management GUI 260 that can be used to provide access to test data by an appropriate level of authorized user. Each set of test data can be associated with a patient by name or other identifying information. As shown in GUI 260, there can be any number of elements of raw data for a particular patient, and this may generally depend on the one or more tests being performed. Each set of raw data can correspond to another set of test data, for example, for one particular test or a particular test phase (or iteration).

  FIG. 27 shows a GUI 270 that can be used to manage the protocol (eg, via an administrative user interface 66 implemented in the system). The protocol management GUI 270 can be used, for example, to identify the test execution protocol used in a particular patient test process. In the example of FIG. 27, the GUI is for the patient, for example, an indication of whether the deep brain stimulator is on or off at each test, the medication to be administered to the patient at the time of each test, and / or its dose. A selection interface element 272 that can be used to identify a set of protocols. This allows a user, such as a clinician, to select a set of protocols associated with a particular patient, where a particular condition is associated with a set of test data acquired for each patient during a particular test session. Helps understand the impact. .

  For example, these protocols can be implemented and the corresponding set of test data can be evaluated to see the effect on different states, for example, whether DBS is on or off at the time of testing. And the effects on conditions such as whether the patient is following the dose prescribed for his drug, and the effects of such conditions on test results. Those skilled in the art will appreciate the various other protocols that can be used to specify patient control parameters associated with a particular set of tests, as well as combinations of protocols.

  In view of the above, neuropathy such as neurological cognitive impairment and neuromotor impairment (eg PD, Alzheimer's disease, multiple sclerosis, dementia, amyotrophic lateral sclerosis (ALS), Parkinsonism, induced by trauma It will be appreciated that a system and method have been described that can be implemented to provide a series of cognitive and motor tests for remotely assessing brain damage, stroke, and multiple system atrophy (MSA), etc. . The system and method collect data in a central data repository to allow patient users to perform tests remotely and to allow clinicians to access such information for further study, for example. Enable.

  In view of the above structural and functional description, it will be apparent to those skilled in the art that portions of the present invention may be implemented as a method, data processing system, or computer program product. Accordingly, such portions of the present invention may take a fully hardware embodiment, a fully software embodiment, or as illustrated and described in the computer system of FIGS. The embodiment may be a combination of software and hardware. Further, a portion of the present invention may be a computer program product including a hardware computer readable storage medium having computer readable program code on the medium. Any suitable computer-readable storage medium may be utilized, examples of which include, but are not limited to, static and dynamic storage devices, hard disks, optical storage devices, and magnetic storage devices.

  Certain embodiments of the present invention have also been described with reference to block diagrams of methods, systems and computer program products. It will be understood that the illustrated blocks, as well as combinations of the illustrated blocks, may be implemented by computer-executable instructions. These computer-executable instructions are transmitted to one or more processors in a general-purpose computer, a dedicated computer, or other programmable data processing device (or combination of devices and circuits) for manufacturing a machine. May be provided to implement the functions indicated in one or more blocks.

  These computer-executable instructions enable a computer or other programmable data processing device to be a prototype that includes instructions stored in computer-readable memory that implement the functions shown in the flowchart blocks. To be connected, it may be stored in a computer readable memory that can function in a particular way. The computer program instructions can also be loaded into a computer or other programmable data processing device to cause the computer or other programmable device to perform a series of operational steps on the computer or other programmable device. A computer-implemented process may be generated such that the instructions executed in step 1 provide steps for implementing the functionality shown in one or more blocks of the flowchart.

  In view of this, FIG. 28 is of a type that can be utilized to implement one or more embodiments of the systems and methods described herein for testing and analyzing a patient's motor and cognitive functions. An example of a computer system 500 is shown. The computer system 500 can be implemented in one or more general-purpose network computer systems, embedded computer systems, routers, switches, server devices, client devices, various intermediate devices / nodes, and / or stand-alone computer systems. . In addition, the computer system 500 or a portion thereof may be various, such as a laptop computer or notebook computer, a personal digital assistant (PDA), a tablet computer, a smartphone (see, eg, FIGS. 33-34), and the like. It can also be implemented on a mobile client or a portable client.

  System 500 may include a computer 502 that may function as any user device 54, 56, 58 and / or server 52, for example. The computer 502 may include a system bus 508 that may include any of several types of bus structures, several examples of the system bus 508 include peripheral component connection (PCI), video electronics standards establishment. Memory buses or memory controllers and peripherals using any of a wide variety of traditional bus architectures, such as committee (VESA), microchannel, industry standard architecture (ISA), extended industry standard architecture (EISA) There are buses and local buses. The system memory 506 may include read only memory (ROM) 510 and / or random access memory (RAM) 512. For example, a basic input / output system (BIOS) including a basic routine for facilitating the transfer of information between elements in the computer 502 at the time of startup or the like may be stored in the ROM 510.

  The computer 502 may also include, for example, a hard disk drive 514, for reading and writing to an optical medium such as a magnetic disk drive 516 (for example, a floppy drive) for reading and writing to the removable disk 518 and a CD-ROM disk 522. An optical disk drive 520 (eg, a CD-ROM drive) may be included. The hard disk drive 514, magnetic disk drive 516, and optical disk drive 520 are connected to the system bus 508 by a hard disk drive interface 524, a magnetic disk drive interface 526, and an optical disk drive interface 528, respectively. The drive and associated computer-readable medium are non-volatile storage devices that store data, data structures, computer-executable instructions, etc. for the computer 502. Although the above description of computer readable media refers to hard disks, removable magnetic disks, CDs, other types of computer readable media such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges are exemplary. Any of these media may be used in the various operating environments 500, and any such media is disclosed in view of the method of the present invention, including the functionality of the analysis engine 17 of FIG. 29, and FIGS. Those skilled in the art will appreciate that they may include computer-executable instructions for performing the relevant methods and functions.

  A number of program modules may be stored in the drives and RAM 512, examples of which include an operating system 530, one or more application programs 532, other program modules 534, and program data 536. The operating system 530 of the computer 502 may be any suitable operating system or combination of operating systems. Application program 532, other program modules 534, and program data 536 can cooperate to perform exercise and cognitive tests on the patient's computing device as previously illustrated and described. Also, the application program 532, other program modules 534, and program data 536 are based on the test execution data as shown and described above, and the patient's motor function, cognitive function, or motor function and cognitive function. It can be used to calculate what represents the combination.

  A user may enter commands and information into the computer 502 through one or more user input devices such as a keyboard 538 and a pointing device (e.g., a mouse 540). Other input devices (not shown) may include a microphone, joystick, game pad, scanner, touch screen, and the like. Other data (eg, acceleration data and gyroscope data) 536 may be provided via a corresponding interface by still other input devices such as sensors. In some examples, the accelerometer and gyroscope may be placed in the same location on a device (eg, a smartphone, PDA or tablet computer) that is attached to or held by the user during the test. Point actions and click actions can be performed using a mouse or other pointing device. This includes actions where the computer user moves the cursor to a specific location on the screen (point), and then clicks (clicks) a mouse button (usually the left button) or other pointing device. Such a point-and-action can be used regardless of the number of various input devices such as a mouse, a touch pad, a keyboard, a joystick, a scroll button, and a roller bar. Information related to such point-and-click actions is a portion of test data (eg, test data 30, 31, 32) for each test (eg, to a central server), as described herein. Can be provided as.

  These input devices and other input devices are often connected to the processing device 504 via a serial port interface 542 connected to the system bus 508, but may be a parallel port, a game port, or a universal serial bus (USB). It may be connected via other interfaces. One or more display devices 544 (eg, a monitor, etc.) are also connected to the system bus 508 via an interface, such as a video adapter 546. Other display devices such as speakers, printers, etc. may be provided instead of or in addition to the monitor.

  Computer 502 may operate in a network environment using a logical connection with one or more remote computers 560. The remote computer 560 may be a workstation, server computer, router, peer device, or other common network node, and generally includes many or all of the elements described with respect to the computer 502, but simply For this purpose, only the memory storage device 562 is shown in FIG. The logical connections shown in FIG. 28 may include a LAN 564 and / or a WAN 566. Such network environments are common in offices, corporate computer networks, intranets, and the Internet.

  When used in a LAN network environment, the computer 502 is connected to the LAN 564 via a network interface or adapter 568. When used in a WAN network environment, the computer 502 typically includes a modem 570, is connected to a communication server on an associated LAN, or has another circuitry for establishing communication over a WAN 566 such as the Internet. . A modem 570, which may be internal or external, is connected to the system bus 508 via the serial port interface 542. In a network environment, program modules illustrated in connection with computer 502 or portions thereof may be stored in remote memory storage device 562 (and / or locally). It will be appreciated that the network connections shown are only examples and other arrangements for establishing a communications link between computers 502 and 560 may be used.

  In the following, exemplary embodiments of systems and methods for evaluating concussion-related functions are described. In order to comprehensively evaluate concussion injury, multiple functions may have to be evaluated, which is often difficult to evaluate. For this reason, concussion usually requires a multi-dimensional approach to performing a series of tests including balance / posture stability tests.

  Although described for the assessment and treatment of traumatic brain injury, this exemplary system and method may be applied to other types of medical conditions that affect the same function as concussion. For example, the system and method may be used to evaluate and / or treat patients with balance disorders or vestibular dysfunction that are not caused by concussion injury, or patients with Parkinson's disease.

<Analysis engine>
FIG. 29 shows an example of an analysis engine 17 that can be used in place of the analysis engine 16 of FIG. The analysis engine 16 may include at least one of a balance / posture stability calculator 31, a neuromotor function calculator 35, a VOR integrity calculator 37, and an index calculator 39. In another example, the analysis engine can include a cognitive function calculator 33. Each calculation unit can calculate a performance variable characterizing each function of the user based on the test data collectively indicated by 31. This test data can be stored in a memory. This can be obtained, for example, in response to user interaction with a device used to perform a test as disclosed herein. When implementing the test engine 12 of FIG. 1, the test engine 12 provides concussion-related test data 17 that is sent to the analysis engine 17 for analysis or otherwise made accessible by the analysis engine 17. May be.

  Concussion-related functions can be broadly defined as falling within one of four domains: cognition, neuromotor, balance and posture stability, and vestibule. The cognitive domain may include functions such as memory / recall, information processing ability, and set switching. The neuromotor domain may include reaction time and coordination. The balance and posture stability domains may include vision, somatosensory function, and vestibular system function. The vestibular domain is also related to the vestibular system, but may specifically include VOR, visual and floating dizziness. Domains may overlap in function (eg, vision is both a balance / posture stability function and a vestibular function). These four domains can also be used to assess other brain disorders such as Parkinson's disease.

  Each concussion-related function may be evaluated using one or more tests, and the results can be stored in memory as test data 31. Posture stability may be assessed with an accelerometer and / or gyroscope data, a balance error scoring system (BESS) that tests and determines posture swing. Cognitive functions can be tested using the Sports Concussion Assessment Tool (SCAT) (which is a standard questionnaire-style test for concussion injury), working memory, set switching, or delayed recognition tests And may be evaluated. Alternatively or in addition, the cognitive function is illustrated in FIGS. 1-27 herein, as disclosed with reference thereto, Sevens test, trail making test, clock drawing test, center out test, Evaluation can be made using one or more of a test, an Archimedes spiral test, a line orientation test, and the like. Thus, cognitive function can be evaluated according to one or more cognitive test implementation and analysis techniques previously illustrated and described.

  VOR integrity tests static vision (eg, asks the patient to indicate the orientation of the image when the patient is stationary with respect to the image), tests moving vision (eg, the patient sees the image Asking the patient to indicate the orientation of the image when moving against) and testing the perceptual time. Neuromotor function may be assessed by testing the reaction time as disclosed herein. Examples of reaction time tests include simple reaction time (SRT) and selective reaction time (CRT). For example, an SRT may display an image and instruct the patient to press a button as soon as the image is displayed. In CRT, an image is displayed at one of a plurality of display locations (for example, the left side or the right side of the display) and the correct display location is selected by the user (for example, if the image is displayed on the touch screen, It may be instructed to touch the image.

  In an exemplary embodiment of the invention, the calculator 31/33/35/37 calculates variables or parameters related to evaluating the function of each domain based on the corresponding test data 17. As programmed. For example, the posture stability calculation unit 31 may calculate one or more BESS variables based on the data in the BESS test performed, and the neurological motor function calculation unit 35 may perform SRT or CRT, For example, one or more variables relating to the time between when the stimulus was displayed and when the patient entered a valid response may be calculated.

  The index calculation unit 39 calculates one or more indexes (for example, also referred to as a score in this specification) based on the output result obtained by each calculation unit 31/33/35/37 for a certain patient. May be programmed. For example, the index calculation unit 39 collects analysis data obtained from a specific set of test data acquired for a specific patient, and based on the collected set of test data, the posture of the specific patient is collected. An index (or score) with a value indicative of stability can be calculated (eg, gyroscope data is combined with accelerometer data to determine attitude sway). Alternatively or in addition, the index calculator 39 may calculate an index (or score), each value of which is a BESS test (eg, BESS score), SCAT or SCAT2 test (eg, SCAT). Score), working memory, set switching, delayed recognition, static visual acuity, perception time, moving visual acuity, SRT, or CRT. The output obtained for index calculation can be provided and stored as part of the analysis data 41 for later analysis, such as by a clinician who has access to the stored analysis data 41. The analysis data 41 may be input to the visualization module 43 for later display, as will be described later.

<Visualization module>
Concussion-related functions may be assessed prior to injury to establish a set of baseline metrics (eg, score or index) for each patient. A post-damage assessment can be performed to generate a subsequent set of metrics that can be compared to baseline metrics. In an exemplary embodiment, visualization module 43 may be configured to display a subsequent set of metrics and output instructions for displaying baseline metrics. Each set of metrics may be displayed as a cobweb graph such that the cobweb graph corresponding to the subsequent set of metrics overlaps the cobweb graph corresponding to the baseline metric in a simultaneous display. Alternatively, the visualization module can simultaneously display two or more respective cobweb graphs (eg, displaying baseline metrics and post-damage metrics side by side).

  The cobweb graph may include any combination of scores and / or indexes. For example, FIG. 33 shows a cobweb graph 300/310/320/330 whose vertices include SRT, CRT, SCAT, BESS, VOR scores / indexes. For example, different numbers and indices may be used for the vertices depending on the test being performed, including any disclosed herein. The operation of the visualization module 43 is shown in FIG. 30, which is a flowchart illustrating a method 600 for displaying concussion related performance metrics. The method 600 may be performed after obtaining a set of baseline metrics, for example, starting with step 610 of obtaining one or more subsequent posture stability metrics from the analysis data 41. In an exemplary embodiment of the invention, method 600 obtains at least one metric from each concussion domain. Thus, steps 612/614/616 provide metrics related to VOR integrity, cognitive function, and neuromotor function, respectively. For each baseline metric, the corresponding subsequent metric may be obtained.

  In step 618, subsequent metrics may be normalized to a baseline metric scale. According to an exemplary embodiment, the baseline metric corresponds to a fully functioning or normal condition of the subject patient. Baseline metrics may also be scaled to facilitate visual understanding. For example, each baseline score or index can be considered to have a scale value of 1 on a scale from 0 to 1. The visualization module 43 may calculate the difference between the baseline metric and its subsequent subsequent metric, for example, the rate of change of each baseline metric. Subsequently, the subsequent metrics are scaled based on this. For example, a difference of 25% on a scale from 0 to 1 corresponds to a scale value of 0.75.

  In step 620, the baseline metric may be displayed along with the subsequent scaled metric. The cobweb graphs shown in FIGS. 31 and 32 correspond to exemplary display results for two separate patients. Each patient begins with a baseline assessment (300/400), then an assessment 24 hours after concussion injury (310/410), an assessment 3 days after the same injury (320/420), the same injury It was evaluated several times over time, such as the evaluation (330/430) after 10 days. In this way, subsequent evaluations may be performed any number of times, each resulting in a new cobweb graph.

  As shown in the examples of FIGS. 31 and 32, all patients lost function after injury. One of the benefits of displaying metrics in a cobweb graph, and in particular in a normalized cobweb graph, is that each patient may respond differently to damage (eg, different types of damage). How different baseline metrics may change for each patient at the same time, as it may affect different functions or each patient may respond differently to the same type of injury) This makes it easy to compare different patients, so instead of comparing the raw scores or indexes of different patients, it is simple to see how each patient was affected against their baseline. It is easy to visualize. To illustrate this, the patient of FIG. 31 had substantially worse SRT and CRT after 10 days (330), moderately worse SCAT and BESS, and almost no difference in VOR. In contrast, the patient in FIG. 32 had little worsening of SRT after 10 days (430), but CRT, SCAT, BESS, and VOR were moderately worse.

  Displaying metrics in the manner described above may facilitate the decision to return to competition in a sport, for example, whether an injured player should decide whether to resume the competition. This can be based on the clinician's assessment of how much damage has been done compared to the line. Clinicians may have different opinions about which criteria are most important to the player's ability to continue the competition. For example, a clinician may place more value on the value of VOR than CRT and, as long as the VOR is substantially the same, leads to the opinion that the player can continue playing even if the CRT is substantially worse Also good. In addition to the decision to return to competition, displaying metrics may be useful, for example, during treatment interventions in DBS, and stimulation parameters may target specific functions, so the deterioration of VOR Can be addressed using a combination of specific stimulation parameters that is different from the combination used to address the worsening of the.

<Judgment of posture stability and VOR integrity>
In an exemplary embodiment, posture stability may be determined using objective tests in addition to or instead of subjective tests such as BESS. Traditional BESS test implementations rely on the subjective interpretation of the test administrator (eg, to determine whether the patient is in the correct posture) but are objective to assess posture stability. It may be desirable to include specific data. Thus, an exemplary embodiment relates to combining data obtained with a gyroscope and data obtained with an accelerometer. In a preferred embodiment, the gyroscope and accelerometer may co-exist on a single portable device, such as a device attached to or held by the user patient (eg, PDA, smartphone, tablet PC, etc.). Good. The portable device may also include a display (eg, a touch screen) for performing at least one of the tests described above, eg, an SRT, CRT, or trial test.

  FIG. 33 shows a set of measurement results obtained using a conventional accelerometer. In a three-axis coordinate system (eg, xyz coordinates), the accelerometer can measure linear acceleration along three axes. Thus, for example, the accelerometer can provide information regarding changes in the patient's body position relative to the forward, backward, lateral, or median directions.

  FIG. 34 shows a set of measurement results obtained using a conventional gyroscope. In contrast to accelerometers, gyroscopes provide information about angular rotation about three axes. In an exemplary embodiment, gyroscope measurements are used to supplement accelerometer measurements and provide more data regarding patient movement than accelerometers alone.

  FIG. 35 is a flowchart illustrating a method 700 for determining posture stability based on a combination of gyroscope data and accelerometer data. The method 700 can begin at step 710 by having the test administrator pull the patient to the measurement location. The measurement position may be one of any number of conventional measurement positions, such as those used in BESS. For example, the patient may be asked to balance himself on the mat. This may or may not be inclined with respect to the ground. The patient may be asked to maintain a balanced position as long as possible. The patient may also be asked to maintain a position to see a distant target in a particular direction or to keep his eyes closed. Other variations of measurement positions / conditions are possible and will be known to those skilled in the art. The method 700 may be used alone to generate objective data that measures posture stability. Alternatively or additionally, method 700 may be used to independently verify the accuracy of BESS test results.

  In step 712, gyroscope measurement results are obtained, and angular acceleration values are calculated from these measurement results. This calculation can be performed using the analysis engine 17, for example.

  In step 714, the accelerometer measurement result is obtained and a linear acceleration value is calculated. The method 700 may then proceed to step 716 to determine whether additional measurement locations or conditions are needed. For example, the angle of inclination of the pad may be adjusted or the patient may be asked to change posture. If there are additional locations / states, the method 700 returns to step 710.

  If the measurement position or state does not remain, a posture stability score or index can be calculated based on the obtained measurement result. Gyroscope and accelerometer data may be used to determine the location of the patient's center of gravity as a function of parameters (which may include how fast, how far, in which direction the user is moving) . One skilled in the art would know to calculate the centroid as a function of these parameters.

  A posture stability score or index may be calculated using a change in the position of the center of gravity over time. For example, the average deviation of the center of gravity, the function of the difference between the patient's initial center of gravity (eg when the accelerometer / gyroscope starts recording) and the average center of gravity position, and / or the frequency of deviation from the initial center of gravity and / or Alternatively, posture fluctuation can be calculated based on a function of the degree of deviation.

  In an exemplary embodiment, the function of the vestibular system is tested and measured. For example, balance and posture stability are associated with proper functioning of the vestibular system. VOR is also associated with the vestibular system. In particular, when the patient moves his / her head during the VOR test, the vestibular system stabilizes the image, maintains the field of view, helps maintain posture stability, and provides information about spatial orientation. To do. These are mostly achieved by the anatomy of the peripheral vestibular system and its neurophysiological connections to the visual system and motor tract.

  The vestibular labyrinth includes anatomical structures that respond to changes in body position. Certain structures respond to linear acceleration, while other structures respond to angular acceleration. Acceleration occurs when the position of the patient's body changes. The anatomy is responsive to acceleration, and the ability of the patient to maintain balance or posture is the effect of these responses. Patient movement (e.g., in attempting to maintain posture stability) further generates acceleration, which can be measured with a gyroscope and / or accelerometer. For example, the semicircular canal, the oval sac, and the spherical sac emit centripetal information about spatial orientation and elicit balance responses by communicating with the cerebellar and motor tracts to maintain posture stability.

  In addition to reactions related to posture stability, visual reactions (eg, involving VOR) may occur. Centripetal input from the vestibular organs forms synapses with the oculomotor nerves, producing rapid compensatory eye movements to maintain visual focus. Symptoms common to vestibular dysfunction include floating dizziness, impaired balance, blurred vision, rotational dizziness, and reduced tolerance to bright light, noise or movement. Such symptoms can be measured and detected using a visual movement test such as VOR.

<Intentional poor performance and prediction>
The ability to accurately test concussion-related functions depends on patient cooperation. Athletes may be motivated to perform lower than expected when performing a baseline test, so when performing subsequent tests after injury, functional deterioration is less significant than baseline function. May not appear.

  Patients may also predict the correct answer to a task or question when performing a test. For example, it may be a prediction as a result of an accidental guess about the location of the next target before the next target is displayed. It may also be a prediction as a result of cheating, such as memorizing a task (eg, trail trajectory). For this reason, it is desirable to detect intentionally poor results and predictions.

  FIG. 36 is a flowchart illustrating a method 800 for detecting intentional poor performance and prediction that may occur in a patient undergoing a test. The method 800 may begin at 810 to obtain patient response time (eg, SRT or CRT).

  In step 812, it may be determined whether the reaction time is above or below the threshold. If the reaction time is above the upper threshold, this indicates an intentional poor performance and the method proceeds to step 814 where the correction procedure is performed. The correction procedure may be as simple as ignoring (eg, disabling or deleting) the response associated with the reaction time. In an exemplary embodiment, the correction procedure may involve replacing the original task or question with a new task / question of the same difficulty level. Alternatively, the difficulty level can be changed to accurately determine whether or not the patient behaves as expected in response to a change in difficulty. For example, the higher the difficulty level, the longer the time it takes for the patient to respond. However, if a patient deliberately performs less than originally, the patient may respond in substantially the same way as before (eg, the response takes the same amount of time). Alternatively, in order to compare the reaction times in two or more cases with respect to the original task, the original task may be performed again at different times after performing some additional tasks, for example.

  If the upper threshold is not exceeded, the method can proceed to step 816 where it is sufficient to determine whether the reaction time is less than the lower threshold. If it is less than the lower threshold, it may be predicting, so that it is connected to another correction procedure (step 818).

  In an exemplary embodiment, the upper and lower threshold values are obtained from statistical information relating to criteria for individuals in a similar situation selected from other patients or members of the general population. For example, the upper threshold value and the lower threshold value may be obtained by referring to the reaction time of another patient of the same age as the target patient. The upper threshold may correspond to an absolute maximum response time or an average maximum response time observed in all patients of the same age as the target patient or in a range including the age of the target patient (for example, plus or minus 2 years). Similarly, the lower threshold may correspond to the absolute minimum response time or the average minimum response time observed in all patients of the same age or age range. Other groups, such as gender, height, weight, body mass index, human body ratio (eg, leg length to torso), disease status (eg, Alzheimer), etc., can be used to select groups of individuals in similar situations Clinical factors may be used.

  If the reaction time is not less than the lower threshold, no prediction or intentional poor performance is detected, the response is valid, and the method proceeds to the next task or question (820).

  While the method 800 has been described with respect to intentional poor performance and predictive detection when performing SRT and CRT tests, the method 800 can be applied to other types of tests with time response. For example, age-based quality control for posture stability (eg how quickly the patient changes position) and cognitive testing (eg how quickly the patient recognizes the displayed object) You may apply.

  Unless otherwise stated, the invention has been described in terms of actions and symbols of actions taken by a computer, such as computer 502 or remote computer 560, in accordance with practices of those skilled in the art of computer programming. Such actions and actions are sometimes referred to as being performed on a computer. This action and symbolically illustrated operation is the manipulation of the electrical signal processing unit 504 representing the data bits that cause the conversion or reduction of the resulting electrical signal representation, the memory system (system memory 506, hard drive 514, floppy disk 518). Maintain data bits at storage locations (including CD-ROM 522, remote memory storage device 562) to reconfigure or otherwise modify other signal processing as well as computer system operation and It will be obvious to include storing data obtained as a result of such processing. The storage location where such data bits are maintained is a physical location having specific electrical, magnetic, or optical properties corresponding to the data bits.

  What have been described above are examples and embodiments of the present invention. Of course, it is not possible to describe all possible combinations of components or methodologies for the purpose of illustrating the invention, but it will be appreciated by those skilled in the art that many other combinations and permutations of the invention are possible. You will recognize it. For example, this approach can be used to help diagnose conditions or dysfunctions other than traumatic brain injury.

Claims (27)

Calculating at least one performance variable characterizing balance and posture stability and at least one performance variable characterizing vestibular oculomotor reflex (VOR) based on user test performance data;
For each performance variable, calculate each score based on the respective performance variable and also based on a set of performance metrics,
A computer system comprising a processor configured to output the at least one calculated score via an output device.   The processor is configured to calculate the performance variable characterizing balance and posture stability, and calculating the performance variable characterizing balance and posture stability includes measuring a linear acceleration from an accelerometer and a gyroscope. The system according to claim 1, based on a combination with measurement results of angular rotation from a scope.   The system of claim 2, wherein the accelerometer and the gyroscope are co-located on a device that is attached to or held by the user when performing a test.   One of the devices attached to or held by the user includes a display to which the processor outputs a graphical test object, at least some of the test objects being relative to the device The system of claim 3, wherein the system represents an input user input. Each of the calculated scores has a value that indicates the degree of a particular injury or disease of the user;
The processor is configured to each calculate at least three of the performance variables;
The outputting includes simultaneously displaying the respective calculated scores of the at least three of the performance variables in a first cobweb graph;
The system of claim 1, wherein each vertex of the first cobweb graph corresponds to one of the calculated scores. A second cobweb graph is displayed simultaneously with the first cobweb graph,
The vertices of the second cobweb graph correspond to a set of baseline scores calculated from the measurement results during the first test of the user, and the scores to which the vertices of the first cobweb graph correspond are: The system of claim 5, wherein the system is calculated from measurement results obtained during subsequent user testing.   The system of claim 6, wherein the scores to which the first and second cobweb graphs correspond are normalized according to the same scale.   The outputting includes simultaneously displaying the first and second cobweb graphs together with a third cobweb graph, and a score corresponding to a vertex of the third cobweb graph is determined by the user The system according to claim 6, wherein the system is calculated from measurement results obtained during additional subsequent tests.   6. The system of claim 5, wherein the injury or disease comprises one of traumatic brain injury or Parkinson's disease. The processor is
When performing the test, get the user's first response time,
Comparing the response time with an upper threshold and a lower threshold;
The response time,
In the comparison, if it is determined that the response time exceeds the upper threshold, the result is intentional low performance,
If the response time is less than the lower threshold, it is a predicted response.
And categorized as
The system of claim 1, wherein the impact that the response time has on calculating the at least one performance variable depends on the categorization.   The system according to claim 10, wherein the upper threshold and the lower threshold are selected based on statistical information regarding a normal response time of an individual who is in the same situation as the user.   The processor is responsive to the response time being categorized into one of an intentional poor performance and a predicted response, the test performance data used to calculate the at least one performance variable. The system of claim 10, wherein the system is configured to exclude test performance data from which the response time is calculated. The processor is responsive to the response time being categorized into one of an intentional poor performance and a predicted response,
Repeating the test task with the response time, and performing a new test task,
The system of claim 10, wherein the system is configured to obtain additional test performance data for calculating the at least one performance variable by outputting a request to the user to perform one of the following: . The processor outputs a request to the user to perform a new test task in response to the response time being categorized into one of an intentional poor performance and a predicted response. Configured to obtain additional test performance data for calculating the at least one performance variable;
The new test task is different in difficulty from the test task that has obtained the response time,
The processor is
Comparing the response time associated with the new test task with the first response time;
The system of claim 10, configured to verify the effectiveness of the categorization based on the comparison of the response times. The processor outputs a request to the user to repeat the test task that has obtained the response time in response to the response time being categorized into one of intentional poor performance and predicted response. Configured to obtain additional test performance data for calculating the at least one performance variable,
The system of claim 9, wherein the request to repeat is output after the user has performed at least one additional task.   The at least one performance variable characterizing balance and posture stability is obtained by at least one of an accelerometer measurement, a gyroscope measurement, a balance error scoring system (BESS) test, and a posture sway test. The system of claim 1, wherein:   The system of claim 1, wherein the at least one performance variable characterizing balance and posture stability is calculated, the calculating being based on a detected change in the user's center of gravity.   The at least one performance variable characterizing a VOR is calculated, wherein the calculating is based on data obtained by at least one of a static vision test, a dynamic vision test, and a perceptual time test. The system described. The processor includes at least one performance variable characterizing balance and posture stability, at least one performance variable characterizing a VOR variable, at least one performance variable characterizing cognitive function, and at least one performance characterizing neuromotor function. Configured to calculate at least three grade variables, including variables,
The output by the processor displays a graphical output based on the respective calculated scores for the at least three of the performance variables to indicate the degree of concussion injury of the user. The system of claim 1, comprising:   The system of claim 19, wherein the graphical output further includes a first cobweb graph, wherein each vertex of the first cobweb graph corresponds to one of the calculated scores, respectively. Outputting by the processor includes displaying a second cobweb graph simultaneously with the first cobweb graph;
The vertices of the second cobweb graph correspond to a set of baseline scores calculated from the measurement results obtained during the user's first test for each of the at least three performance variables, and the first cobweb graph 21. The system of claim 20, wherein the score to which the vertices correspond is calculated from measurements obtained when performing a subsequent test of the user after a concussion injury. The at least one performance variable characterizing balance and posture stability is calculated, the calculating being based on a detected change in the user's center of gravity;
The performance variable characterizing the VOR is calculated, the calculating being based on data obtained by at least one of a static vision test, a dynamic vision test, a perceptual time test,
The at least one performance variable characterizing cognitive function is calculated, the calculating by at least one of a test with a sports concussion assessment tool (SCAT), a test of working memory, a set switching test, a delayed recognition test Based on the data obtained,
The at least one performance variable characterizing neural motor function is calculated, and the calculation is based on data obtained by at least one of a simple reaction time (SRT) test and a selective reaction time (CRT) test. The system of claim 19. A computer processor calculates at least one of a performance variable characterizing balance and posture stability and a performance variable characterizing vestibulo-oculomotor reflex (VOR) based on user test performance data;
For each of the at least one performance variable, the processor calculates a respective score based on the respective performance variable and also based on a set of performance metrics;
A computer-implemented method comprising outputting the at least one calculated score by the processor via an output device. A non-transitory computer readable medium having instructions executable by a processor stored thereon, the instructions assessing the degree of traumatic brain injury of a user to the processor when executed by the processor. A method for performing the method, the method comprising:
Based on the test results data of the user, among the performance variables characterizing balance and posture stability, the performance variables characterizing cognitive function, the performance variables characterizing neuromotor function, and the performance variables characterizing vestibulo-ocular reflex (VOR) Calculate at least one of
For each of the at least one performance variable, calculate a respective score based on the respective performance variable and also based on a set of performance metrics;
Outputting the at least one calculated score via an output device to facilitate diagnosis of the traumatic brain injury of the user. The at least one performance variable including at least three performance variables includes at least one performance variable characterizing balance and posture stability, at least one performance variable characterizing a VOR variable, at least one performance variable characterizing cognitive function, nerve Including at least one performance variable characterizing sexual motor function;
A respective output score is calculated for each of the performance variables,
25. The outputting further comprises providing a cobweb graph, wherein each vertex of the cobweb graph corresponds to one of the calculated scores calculated for each of the performance variables, respectively. The medium described in 1. The cobweb graph is a first cobweb graph;
The outputting further includes simultaneously displaying the first cobweb graph and another cobweb graph;
The vertices of the second cobweb graph correspond to a set of baseline scores calculated from measurements obtained during the user's first test for each of the at least three performance variables, and the first cobweb graph 26. The medium of claim 25, wherein the score to which the vertex of the graph corresponds is calculated from measurements obtained when performing subsequent testing of the user after concussion injury. The at least one performance variable characterizing balance and posture stability is calculated, the calculating being based on a detected change in the user's center of gravity;
The performance variable characterizing the VOR is calculated, the calculating being based on data obtained by at least one of a static vision test, a dynamic vision test, a perceptual time test,
The at least one performance variable characterizing cognitive function is calculated, the calculating by at least one of a test with a sports concussion assessment tool (SCAT), a test of working memory, a set switching test, a delayed recognition test Based on the data obtained,
The at least one performance variable characterizing neural motor function is calculated, and the calculation is based on data obtained by at least one of a simple reaction time (SRT) test and a selective reaction time (CRT) test. 26. The medium of claim 25.