U.S. patent application number 13/644344 was filed with the patent office on 2018-01-18 for system and method for motor and cognitive analysis.
This patent application is currently assigned to THE CLEVELAND CLINIC FOUNDATION. The applicant listed for this patent is THE CLEVELAND CLINIC FOUNDATION. Invention is credited to Jay L. Alberts, Cameron Mclntyre.
Application Number | 20180018441 13/644344 |
Document ID | / |
Family ID | 50234115 |
Filed Date | 2018-01-18 |
United States Patent
Application |
20180018441 |
Kind Code |
A9 |
Alberts; Jay L. ; et
al. |
January 18, 2018 |
SYSTEM AND METHOD FOR MOTOR AND COGNITIVE ANALYSIS
Abstract
In an example embodiment, this disclosure provides a
non-transitive computer-readable medium on which are stored
instructions executable by a processor, the instructions which,
when executed by the processor, cause the processor to perform a
method. The method includes computing, based on test performance
data of a user, at least one of a performance variable
characterizing cognitive functioning and a performance variable
characterizing neuromotor functioning. For each of the at least one
performance variable, a respective score can be computed based on
the respective performance variable and based on a set of
performance metrics. The method can also include outputting, via an
output device, the at least one computed score.
Inventors: |
Alberts; Jay L.; (Chagrin
Falls, OH) ; Mclntyre; Cameron; (Cleveland,
OH) |
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Applicant: |
Name |
City |
State |
Country |
Type |
THE CLEVELAND CLINIC FOUNDATION |
Cleveland |
OH |
US |
|
|
Assignee: |
THE CLEVELAND CLINIC
FOUNDATION
Cleveland
OH
|
Prior
Publication: |
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Document Identifier |
Publication Date |
|
US 20140074267 A1 |
March 13, 2014 |
|
|
Family ID: |
50234115 |
Appl. No.: |
13/644344 |
Filed: |
October 4, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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13510683 |
Sep 7, 2012 |
9653002 |
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PCT/US10/57453 |
Nov 19, 2010 |
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13644344 |
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61262662 |
Nov 19, 2009 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 50/20 20180101;
A61B 5/16 20130101; A61B 5/162 20130101; A61B 5/1124 20130101; G16H
15/00 20180101; G16H 50/70 20180101; A61B 5/4082 20130101; G16H
50/30 20180101; A61B 5/4833 20130101 |
International
Class: |
G06F 19/00 20110101
G06F019/00 |
Claims
1. A computer system, comprising: a computer processor; and a user
input device via which the processor is configured to receive user
position data informative of user positions relative to the input
device; wherein the processor is configured to: compute, based on
the position data, a variable value; and compute, based on the
computed variable, a score characterizing at least one of a motor
function of the user, a cognitive function of the user, and a
cognitive-motor function of the user.
2. The system of claim 1, wherein: the processor outputs via a
display device graphical test objects; the computation of the
variable value is based on a relationship of the user positions
relative to the test objects.
3. The system of claim 2, wherein the processor is configured to
determine a size of the display device based on input from the
user, and to scale the display of the test data according to the
determined size.
4. The system of claim 1, wherein the processor computes separate
scores respectively characterizing motor function and cognitive
function.
5. The system of claim 1, wherein the processor computes a
composite score characterizing a cognitive-motor function.
6. The system of claim 1, wherein the computation of the score
includes a weighting based on performance of a plurality of
patients of a patient population.
7. The system of claim 1, wherein the variable value is computed
based on kinematic data obtained from the position data.
8. The system of claim 7, wherein the processor is configured to
compute an error value indicative of a difference between the
kinematic data and ideal kinematic data, the variable value being
based on the computed error value.
9. The system of claim 1, wherein the processor is configured to
compute an error value based on a difference between a line drawn
to connect two test objects and an ideal line connecting the two
test objects, the line drawn being determined based on the position
data.
10. The system of claim 1, wherein the processor is configured to
compute a dwell time corresponding to a time interval that the
position data indicates a position to remain within a predefined
bounded area, and the score is based on the computed dwell time and
characterizes cognitive function.
11. The system of claim 1, wherein the processor is configured to
compute, based on the position data, a reaction time between
presentation of a stimulus and an initiation of a movement, and the
score is based on the computed reaction time and characterizes
cognitive function.
12. The system of claim 1, wherein the position data is obtained
during administration of a test, and the processor is configured to
select, from a plurality of tests, a second test to administer
based on a relationship between the computed score and respective
difficulties of the plurality of tests.
13. The system of claim 1, wherein the score is updated during
administration of a test during which the position data is
obtained, and the system is configured to adjust a difficulty of
the test based on the score and prior to completion of the
administration of the test.
14. The system of claim 1, wherein the system is configured to:
receive an input of a proposed change in medical treatment for the
user; search a patient database for patients with clinical
characteristics and scores similar to those of the user; search the
patient database for those of the similar patients who have been
subjected to a change similar to the proposed change; and based on
data stored in association with the patients subjected to the
similar change, determine an expected change, in response to the
proposed change, in at least one of the score for the user and
medical condition classification for the user.
15. The system of claim 1, wherein the user position data is
informative of user positions relative to the input device during
administration of at least two of a sevens test, a trail making
test, a clock drawing test, a reaction time and subsequent movement
task test, a center-out test, an Archimedes spiral test, a judgment
of line orientation test, and a test whose complete renderings are
dynamically provided as the user takes the test.
16. The system of claim 1, wherein the input device is one of
attached to and held by the user during administration of a test
that provides the user position data.
17. The system of claim 1, wherein the computed score
characterizing a cognitive function of the user is computed, the
computation being based on test data obtained via at least one of a
Sport Concussion Assessment Tool (SCAT) test, a working memory
test, a set-switching test and a delayed recognition test.
18. The system of claim 1, wherein the computed score
characterizing a motor function of the user is computed, the
computation being based on data obtained via at least one of a
simple reaction time (SRT) test and a choice reaction time (CRT)
test.
19. A computer-implemented method comprising: receiving, by a
computer processor, user position data informative of user
positions relative to an input device; computing, by the processor
and based on the position data, a variable value; and computing, by
the processor and based on the computed variable, a score
characterizing at least one of a motor function of the user, a
cognitive function of the user, and a cognitive-motor function of
the user.
20. A non-transitive computer-readable medium on which are stored
instructions executable by a processor, the instructions which,
when executed by the processor, cause the processor to perform a
method, the method comprising: computing, based on test performance
data of a user, at least one of a performance variable
characterizing cognitive functioning and a performance variable
characterizing neuromotor functioning; for each of the at least one
performance variable, computing, based on the respective
performance variable and based on a set of performance metrics, a
respective score; and outputting, via an output device, the at
least one computed score.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 61/542,548, filed Oct. 3, 2011, entitled SYSTEM AND
METHOD FOR MOTOR AND COGNITIVE ANALYSIS. This application also is a
continuation-in-part of U.S. application Ser. No. 13/510,683, which
relates to U.S. Provisional Patent Application No. 61/262,662,
filed Nov. 19, 2009, and PCT/US2010/057453, filed Nov. 19, 2010,
the contents of each of which are hereby incorporated by reference
in their entireties.
TECHNICAL FIELD
[0002] Inventions described herein relate generally to a system and
method to objectively qualitatively quantify cognitive, motor, and
cognitive-motor functioning.
BACKGROUND
[0003] There are various neuromotor and neurocognitive disorders
including Alzheimer's disease, Parkinson's Disease (PD), and
progressive supranuclear palsy to name a few. Neuromotor and
neurocognitive disorders affect motor function, cognitive function
or both.
[0004] In order to properly treat many neuromotor and
neurocognitive disorders, it is desirable to better understand or
classify an individual's condition. Accordingly, a variety of tests
have been developed for various types of diseases and injuries. For
example, one scale for assessing a patient's Parkinson's disease is
the Unified Parkinson's Disease Rating Schedule (UPDRS). Various
other tests exist that are utilized by a clinician to help the
clinician categorize a patient's disorder.
[0005] To more efficiently administer and objectively analyze
results of such tests, computerized systems for administering some
of such tests have been proposed. U.S. Pat. No. 6,435,878 ("the
'878 patent") proposes a system where a user's reaction time to a
stimulus can be measured. However, the proposed system does not
measure the quality of the user's interaction with the system. The
system of the '878 patent also dynamically modifies a presentation
time or quantity of a stimulus for the stimulus based on the user's
performance, but does not qualitatively modify test difficulty
based on user performance.
[0006] U.S. Pat. No. 7,294,107 ("the '107 patent") similarly refers
to a testing system with which user reaction time can be measured.
However, as with the '878 patent, the system does not measure the
quality of the user's interaction with the system. The system of
the '107 patent also determines based on user performance which
tests to administer and whether to terminate a test, but does not
qualitatively modify a particular test's difficulty based on user
performance.
[0007] U.S. Pat. No. 6,517,480 ("the '480 patent") refers to a
testing system in which a maze trace is detected and an overall
time for completion of the test is detected, but the system does
not provide for any qualitative measurement of the user's
performance of the test or for modifying testing difficulty in view
of user performance.
[0008] Moreover, none of the '878, '107, and '480 patents provide a
system or method for time-based testing of a degree of cognitive
ability, nor do they provide a system or method that presents data
regarding a correlation of test results to patient information,
such as medications the patient is taking and/or stimulation
parameters used for Deep Brain Stimulation (DBS) of the
patient.
SUMMARY
[0009] This invention relates to a system and method to for motor
and cognitive analysis.
[0010] According to an example embodiment, a computer system can
include a processor configured to compute, based on test
performance data of a user, at least one performance variable
characterizing cognitive functioning, and at least one performance
variable characterizing neuromotor functioning. For each
performance variable, processor can compute a respective score
based on the respective performance variable and based on a set of
performance metrics. The at least one computed score can be output
via an output device.
[0011] According to another example embodiment, a
computer-implemented method can include receiving, by a computer
processor, user position data informative of user positions
relative to an input device. The processor can also compute a
variable value based on the position data. The processor can also
compute, based on the computed variable, a score characterizing at
least one of a motor function of the user, a cognitive function of
the user, and a cognitive-motor function of the user.
[0012] According to yet another example embodiment, a
non-transitive computer-readable medium on which are stored
instructions executable by a processor, the instructions which,
when executed by the processor, cause the processor to perform a
method. The method includes computing, based on test performance
data of a user, at least one of a performance variable
characterizing cognitive functioning and a performance variable
characterizing neuromotor functioning. For each of the at least one
performance variable, a respective score can be computed based on
the respective performance variable and based on a set of
performance metrics. The method can also include outputting, via an
output device, the at least one computed score.
[0013] This invention also relates to systems and methods for
displaying information derived from the underlying measurements.
Such analysis and/or resulting display can help a physician or
other health care provider diagnose the patient's condition.
[0014] To facilitate use and access, the test can be implemented as
an Internet-based application that can be accessed by an authorized
user at a remote location via a predetermined resource locator
(e.g., a URL). Additionally, by implementing the system as a
web-based application, test data can be maintained (anonymously)
for a plurality of patients at a central server to facilitate
further analysis and research. For example, results of the testing
for a plurality of users further can be aggregated to generate a
new index for classifying movement disorders or determining the
severity of a movement disorder, which may (or may not) be
correlated with existing standards, such as the commonly used
Unified Parkinson's Disease Rating Scale (UPDRS). For example, a
composite score may be calculated that combines both cognitive
performance (e.g., related to dwell time or set switching time,
where set switching time refers to the time taken to refocus
attention from one task to another) and motor performance (e.g.,
related to straightness of movement), which composite score may be
useful for simultaneous assessment of both cognitive and motor
function. Since the measure would be captured in a standard manner
across users, the measure may be used to characterize a current
user's performance against a larger group of patients. The
clinician may use the comparative data to explore effects of
various medical interventions and their predicted outcomes.
[0015] According to an example embodiment of the present invention,
a computer-implemented testing method may include: recording, by a
processor, respective time information for each of a plurality of
positions of a display device that are traced during administration
of a test; determining, by the processor, a plurality of speed
values and/or a plurality of velocity values based on the recorded
time information; and outputting, by the processor, test result
information based on the speed and/or velocity values.
[0016] In an example method, the output information includes a
score computed based on the determined plurality of speed and/or
velocity values.
[0017] The method may further include plotting the values as a
graph curve, and comparing at least one slope of the curve to at
least one slope of a stored curve. The score may be based on the
comparison.
[0018] The method may include determining derivatives of the
velocity values, and the test result information may be based
additionally on the derivatives. For example, the derivatives may
include acceleration values.
[0019] The method may include generating at least a portion of the
test result information by calculating an average, standard
deviation, mean square error, and/or root mean square error of a
difference of (a) the determined values from ideal values, or (b)
derivative values of the determined values from ideal derivative
values.
[0020] The method may include, responsive to the trace of the
plurality of positions, recording the plurality of traced
positions. Each of the determined values may be recorded in
association with respective ones of the recorded plurality of
traced positions.
[0021] According to an example, the recorded plurality of traced
positions are recorded at a rate of approximately 30 Hz.
[0022] According to an example, the output information includes a
graph that plots the determined values against the recorded
plurality of traced positions.
[0023] According to an example, each of the plurality of traced
positions is recorded as a respective pair of an abscissa value and
an ordinate value. For each pair of abscissa and ordinate values,
the abscissa and ordinate values are separately associated with
respective ones of the determined values.
[0024] In an example method, the output information indicates
cognitive ability and/or motor skill of a user in response to whose
movement the plurality of positions are traced.
[0025] In an example method, the display device is part of a
patient terminal, the time information is recorded at a server
coupled to the patient terminal via a network, and the test result
information is output at a clinician terminal coupled to the server
via the network. In an example embodiment, the network includes the
Internet.
[0026] According to an example embodiment of the present invention,
a computer-implemented testing method may include: displaying in a
display device a first target and a second target; responsive to
user input corresponding to a trace of a plurality of positions in
the display device, which, in combination, form a path from the
first target to the second target, recording, by a processor,
respective time information for each of the plurality of positions;
and determining, by the processor, and outputting, information
regarding a cognitive ability and/or a motor skill of a user based
on the recorded time information.
[0027] The various methods and system components described herein
may be practiced and provided, each alone, or in various
combinations.
[0028] An example embodiment of the present invention is directed
to one or more processors, which may be implemented using any
conventional processing circuit and device or combination thereof,
e.g., a Central Processing Unit (CPU) of a Personal Computer (PC)
or other workstation processor, to execute code provided, e.g., on
a hardware computer-readable medium including any conventional
memory device, to perform any of the methods described herein,
alone or in combination. The one or more processors may be embodied
in a server or user terminal(s) or combination thereof. The user
terminal may be embodied, for example, as a desktop, laptop,
hand-held device, Personal Digital Assistant (PDA), television
set-top Internet appliance, mobile telephone, smart phone, etc., or
as a combination of one or more thereof. The memory device may
include any conventional permanent and/or temporary memory circuits
or combination thereof, a non-exhaustive list of which includes
Random Access Memory (RAM), Read Only Memory (ROM), Compact Disks
(CD), Digital Versatile Disk (DVD), and magnetic tape.
[0029] An example embodiment of the present invention is directed
to a hardware computer-readable medium, e.g., as described above,
having stored thereon instructions executable by a processor to
perform the methods described herein, and/or for storing output
data produced via execution of the methods described herein.
[0030] An example embodiment of the present invention is directed
to a method, e.g., of a hardware component or machine, of
transmitting instructions executable by a processor to perform the
methods described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] FIG. 1 is a block diagram of a test and analysis system,
according to an example embodiment of the invention.
[0032] FIG. 2 depicts a system architecture that can be implemented
for testing and analysis of motor and cognitive functions,
according to an example embodiment of the invention.
[0033] FIG. 3 is a screen shot for a log-in screen, according to an
example embodiment of the invention.
[0034] FIG. 4 depicts a patient's questionnaire that can be
presented to a patient or clinician user, according to an example
embodiment of the invention.
[0035] FIG. 5 depicts a screen shot for another part of a
questionnaire that can be presented to a patient or clinician user,
according to an example embodiment of the invention.
[0036] FIG. 6 depicts a graphical user interface (GUI) that can be
utilized for calibration of a remote test device, according to an
example embodiment of the invention.
[0037] FIG. 7 depicts a screen shot GUI for an introductory screen
that can be utilized for initiating a test, according to an example
embodiment of the invention.
[0038] FIG. 8 depicts a screen shot GUI that can be utilized for
providing instructions for a "seven's test", according to an
example embodiment of the invention.
[0039] FIG. 9 depicts a "seven's test" GUI that can be implemented
on a computer, according to an example embodiment of the
invention.
[0040] FIG. 10 depicts the "seven's test" GUI of FIG. 9
illustrating the test completed by a user, according to an example
embodiment of the invention.
[0041] FIG. 11 depicts a screen shot GUI that can be utilized for
providing instructions for a reaction test, according to an example
embodiment of the invention.
[0042] FIG. 12 depicts a reaction test GUI that can be implemented
on a computer, according to an example embodiment of the
invention.
[0043] FIG. 13 depicts the reaction test GUI of FIG. 12 at least
partially completed by a patient user, according to an example
embodiment of the invention.
[0044] FIG. 14 depicts a screen shot GUI that can be utilized for
providing instructions for a computer-implemented trail making test
(Part A), according to an example embodiment of the invention.
[0045] FIG. 15 depicts a trail making test (Part A) GUI that can be
implemented on a computer, according to an example embodiment of
the invention.
[0046] FIG. 16 depicts the trail making test (Part A) GUI of FIG.
15 partially completed by a patient user, according to an example
embodiment of the invention.
[0047] FIG. 17 depicts a screen shot GUI that can be utilized for
providing instructions for a computer-implemented trail making test
(Part B), according to an example embodiment of the invention.
[0048] FIG. 18 depicts a trail making test (Part B) GUI that can be
implemented on a computer, according to an example embodiment of
the invention.
[0049] FIG. 19 depicts the trail making test (Part B) GUI from FIG.
18 partially completed by a patient user, according to an example
embodiment of the invention.
[0050] FIG. 20 depicts a trail making test being performed on a
surface of a tablet personal computer (PC) by a patient user,
according to an example embodiment of the invention.
[0051] FIG. 21 depicts test results that can be displayed to a user
via a management user interface, according to an example embodiment
of the invention.
[0052] FIG. 22 depicts the management user interface of FIG. 21
demonstrating an example of where data can be obtained for use in
assessing neurocognitive functions of a patient user, according to
an example embodiment of the invention.
[0053] FIG. 23 depicts velocity data that can be computed for a
trail making test (Part A), according to an example embodiment of
the invention.
[0054] FIG. 24 depicts an enlarged view of a portion of the test
from FIG. 23 that can be generated based upon data acquired from a
patient user, according to an example embodiment of the
invention.
[0055] FIG. 25 depicts dwell time data computed for a plurality of
targets for a trailing making test (Part A) and a trail making test
(Part B) test, according to an example embodiment of the
invention.
[0056] FIG. 26 depicts a screen shot for another management user
interface that can be utilized for browsing and selecting patient
test data, according to an example embodiment of the invention.
[0057] FIG. 27 depicts another management user interface that can
be utilized for managing patient protocols employed during testing,
according to an example embodiment of the invention.
[0058] FIG. 28 depicts a computing environment that can be utilized
for implementing systems and methods described herein, according to
an example embodiment of the invention.
[0059] FIG. 29 is a block diagram of an analysis engine for a test
and analysis system, according to an example embodiment of the
invention.
DETAILED DESCRIPTION
[0060] FIG. 1 depicts an example of a test system 10 that can be
implemented according to an aspect of the invention. The system 10
includes a test engine 12 that includes methods and functions that
are utilized to acquire data relevant to the testing process
performed by the system 10. In an example embodiment, the test
engine 12 may be located at a server to which a user computing
device may connect to remotely access the methods and functions of
the test engine 12.
[0061] The test engine 12, for example, can include a patient
information module 14. The patient information module 14 can be
programmed to acquire patient information that can be provided to
an analysis engine 16 in the form of patient data 18. The patient
information can include a series of templates or forms (e.g., an
XML or other document) that can be completed by a user using an
appropriate user input device, such as a keyboard and/or mouse
and/or stylus. Examples of patient information include log-in
information to authenticate the user with the system, such as a
log-in ID and password. The patient information can also include
information about a patient's current health condition as well as
information about the environment in which the patient is taking
the test. Additional information can be acquired relating to
medication that the patient is currently taking, including names of
medication, dosage, number of times per day, and the time since the
last dosage. Those skilled in the art will appreciate various types
of forms and constructs that can be utilized to acquire and send
the patient data 18 to the analysis engine 16.
[0062] The test engine 12 also includes a calibration module 20
that is programmed to calibrate the system such as for remote
operation in which specifics of the users' test equipment may be
unknown. The calibration component 20, for example can be utilized
to ascertain a relative two-dimensional size of a display or
monitor on which the test is being implemented. For example, a
graded scale can be graphically rendered onto a display, such as a
series of spaced apart lines that are provided with a corresponding
normalized scale known to the programmer. An article can be
provided to the user of a known size (e.g., an 81/2''.times.11''
sheet of paper, a dollar bill, or credit card), which can be
positioned adjacent to the scale by the user to ascertain
dimensions (e.g., in both the x and y directions) of the user's
display according to size of the image being displayed to the user
via the graphical user interface 22. A user can in turn enter the
corresponding score into the system 10 as calibration data 24 that
is utilized by the analysis engine 16. Thus, by knowing the
relative display size for the user's computing device, appropriate
geometry and position information can be obtained from the
subsequent battery of tests to be performed. The analysis engine 16
can employ the calibration data 24 to scale the corresponding
testing data consistent with the user's particular test environment
(e.g., display screen) on which the user is taking the test.
Specifically, the data presented to the user for the test and/or
the test results may be scaled according to the calibration data.
For example, accuracy (i.e., size) of displayed targets and/or
distances between displayed targets may be modified according to
Fitts' law in accordance with the user's test environment.
[0063] In an alternative example embodiment of the invention, the
calibration may be performed by transmitting data for presentation
of three or four targets in the user's screen, one target at each
corner of the screen. The system may record a number of pixels or
defined X,Y positions between the targets as a representation of
vertical and horizontal distances between the targets, which
distances may be used as calibration data for scaling test
data.
[0064] Alternatively or additionally, certain patient devices can
be pre-configured having known configurations, such that
calibration may be omitted. For example, devices having a
predetermined configuration may be registered with the system. For
instance, such preregistered devices or terminals can reside at
doctors' offices, at hospitals, or other institutions.
[0065] In an alternative example embodiment, differences in the
display screens may be ignored, and the test data presented to the
user and the test result may be uniform across the various test
platforms.
[0066] The test engine 12 also includes a plurality of test
applications (e.g., functions or methods) 26 and 28, indicated as
TEST 1 application through TEST N application, where N is a
positive integer denoting any number of tests. Each test
application can be programmed in a manner to test motor and/or
cognitive functions, and/or a combination of motor and cognitive
functions, of a user.
[0067] For example, the test can be similar to known tests such as
a "Seven's test" (described in detail below with respect to FIGS. 9
and 10), a trail making test such as the trail making test parts A
and B, a clock drawing test, and a reaction-time and motor task
that can be utilized to test appropriate motor and/or
neurocognitive functions of the user. Other tests with which the
test engine 12 maybe programmed may include a center-out test
(which assesses information processing speed (e.g., deciding to
which target to move) and motor performance (e.g., quality of
movement to a given target)), an Archimedes spiral test (to assess
tremor in Parkinson's patients, where, as the patient progresses
outward, tremor intends to increase), Benton's judgment of line
orientation test (which measures visuospatial judgment in
brain-injured patients), and tests whose complete renderings are
dynamically provided as the user takes the tests. For example, a
cyclical tracking test may be provided, for which a pattern for the
user to trace is provided that includes points rendered after the
user begins to trace the pattern. For example, a sine wave, circle,
or other pattern, which changes or moves as the user traces the
pattern may be displayed.
[0068] Each test application 26 to 28 can be provided to a user
computer in an interactive manner that provides interactive
graphical objects in the GUI 22, within a hardware display device,
such as a computer or tablet screen. Such interactivity can be
implemented through the use of an action script or other functions
or methods that can be provided to the user, some of which can vary
according to the platform in which the test engine 12 is being
implemented, for example, based on a calibration as described
above.
[0069] In an example embodiment, the test engine 12 can be
implemented using the Flash platform, such as can be programmed
using ADOBE.RTM. FLEX.RTM. software available from Adobe Systems
Incorporated. The Flash platform has advantages in that, for
example, it has an extremely high market penetration and no
additional software components are required to be installed on the
user's machine, such as when the test engine 12 is accessed
remotely such as via a web browser of the user's machine.
Advantageously, the FLEX.RTM. applications for each of the
components of the applications or methods 14, 20, 26 and 28 may
provide a stateful client where changes can occur on the display
without requiring to load a new web page. Additionally, it has been
determined that such an implementation allows sufficient resolution
of geometry and position data to be acquired such that
corresponding test results can be analyzed to provide meaningful
information about motor, cognitive and cognitive-motor function of
the user.
[0070] Data is acquired for each test application 26 to 28 as
corresponding respective test data 30 to 32, which can be provided
to and/or utilized by the analysis engine 16. Thus, by performing a
plurality of multi-part tests 26 through 28, each test can provide
corresponding test data 30 to 32 that can be analyzed by the
analysis engine 16 to provide meaningful information and results
based on the test data 30 to 32.
[0071] The test data can include an indication of which test of a
plurality of different tests is being performed along with an
indication of the position of graphical objects (e.g., targets) for
the test as well as an indication of the position for a cursor or
other pointing device that is utilized for performing the test. For
example, the test application 26 to 28 can employ a get_cursor_pos(
)or other Application Programming Interface (API) to monitor and
obtain cursor position information that is stored along with
temporal information, such as the times corresponding to the
obtained cursor positions, as the test data 30 to 32. The sampling
of such data may be at a rate of, for example, 30 Hz.
[0072] As an example, a test application 26, 28 can display on the
display a GUI having one or more targets, each as a graphical
object having a shape (e.g., a circle, oval, triangle or rectangle)
that encompasses or bounds a set of coordinates on the user's
display device. A user can position on the display a cursor or
other graphical object having its own object position in
two-dimensional space (e.g., having X and Y coordinates), for
example, using a pointing device, such as a mouse or stylus for
touch screen, or without a pointing device, such as via a finger on
a touch screen. The test application 26, 28 can provide
instructions requesting the user to position the cursor/object or
draw lines between two or more particular targets. The movement of
the cursor on the screen relative to the known position of each of
the targets (corresponding to the test data 30 to 32) can be
analyzed by the analysis engine 16.
[0073] The analysis engine 16 may include, for example, a motor
calculator 34 and/or a cognitive calculator 38. The calculators 34
and/or 38 may be, for example, software modules stored on a
computer-readable hardware device, which may be executed to perform
various calculations based on the same or different input
parameters.
[0074] In an example embodiment, the motor calculator 34 is
programmed to determine a number of one or more kinematic variables
based on the test data 30 to 32. For example, the motor calculator
34 can be programmed to determine a position of the cursor or
pointer device, a velocity, an acceleration, a speed and/or a
tangential acceleration for each sample of test data acquired
during a test interval. For example, the tangential acceleration
may be used by the analysis engine 16 as an indication of degree of
curvature in user movements.
[0075] In an example embodiment, the motor calculator 34 can also
determine derivative information of the above-referenced variables,
such as corresponding to a measure of how close to the user's line
between a pair of targets is fitted to an ideal straight line
between the pair of targets. For example, for every data point
residing on the ideal straight line between sequential targets, a
distance to a corresponding point on the line drawn by the user can
be determined. The sum of the determined distances between the
respective points can be determined, and divided by the number of
points for which the distances were determined to provide an
average associated with the ideal line relative to that drawn by a
user. This can be repeated for a line drawn by a user between
respective targets to provide an objective indication of the
accuracy of lines drawn.
[0076] In an example embodiment, the motor calculator 34 may
further calculate a means square error by determining an average of
the error squared for the difference between the ideal and actual
lines. In an example embodiment, the motor calculator 34 may
further calculate the root mean squared error or standard deviation
of the difference between the ideal and actual lines, for example
by calculating the square root of the means square error. Thus, the
motor calculator 34 can determine various values representative of
the average distance that the user's data points on the user's line
deviate from the idea line.
[0077] In an example embodiment, average, means square error, root
means square error, and/or standard deviation information may be
similarly calculated for deviation, over the course of a test, per
each recorded position, each separate X position and Y position,
and/or on a per line basis, between speed, velocity, acceleration,
tangential acceleration, etc., between actual recorded values and
ideal values for those parameters.
[0078] Other information includes whether the user has drawn
crossing lines, as described below with respect to FIGS. 15 and
16.
[0079] The above is not intended as an exhaustive list of
calculations which the motor calculator 34 may perform, and other
example embodiments provide for calculation of additional or
alternative variables and parameters based on the acquired test
data 30 to 32 that is sampled over time. The results of the
calculations determined by the motor calculator 34 can be stored as
part of analysis data 36. The analysis data 36 can also include
calibration data 24 and patient data 18, which can be utilized to
improve the accuracy of the calculations by the motor calculator 34
and the cognitive calculator 38. Alternatively, calibration data
may be used, as described above, to alter the administered test, so
that results of tests administered at different platforms are
comparable, without requiring further consideration of differences
between the platforms, e.g., as reflected by the calibration data
24.
[0080] In an example embodiment of the invention, the cognitive
calculator 38 is programmed to compute variables or parameters
relevant to assessing cognitive function of a patient-user. For
example, the cognitive calculator 38 can compute a dwell time based
on the acquired test data 30 and 32. A dwell time can correspond to
a time period during which a cursor or other graphical object is
within a given predefined bounded area, such as can be defined as
an X and Y position or range that encompasses a displayed graphical
object or target. The computed dwell time may be used to assess a
patient's set switching ability, to refocus attention from one task
to another, for example, where dwell time reflects a dwell period
in a first target (after initial movement to the first target)
before moving to the next target. Dwell time may be an indicator of
"cognitive freezing" in neurocognitive or other patient groups. The
cognitive calculator 38 can also calculate the reaction time, such
as corresponding to a time interval between a presentation of a
stimulus and the initiation of movement of a pointing device by a
user during a reaction test application 26, 28, which can be
further utilized by the cognitive calculator 38 to generate a score
of the patient's information processing capacity. In an example
embodiment of the invention, the cognitive calculator 38 may
further use data output by the motor calculator 34, e.g.,
representative of motor function quality, to calculate data
representative of cognitive ability.
[0081] For example, an initial speed or acceleration when leaving a
given target to move to a following target may be used as a
cognitive measurement in certain instances. For example, if the
initial phase of movement is relatively rapid (with high velocity
and acceleration), and the user moves to the correct target, this
information may be used to conclude that the user movement was made
primarily under predictive or feedforward control (i.e., the user
was very sure of where to go). On the other hand, if the speed or
acceleration is relatively low or there are multiple starts and
stops once the user leaves the target, the information may be used
to conclude that the patient is unsure of the target to which to
move, indicative of a deficiency in information processing speed,
especially where other components of the user movements are
relatively normal or can be made relatively quickly. It should be
understood and appreciated that certain types of calculations may
not apply to different types of tests depending upon the main
purpose of the test.
[0082] In an example embodiment of the invention, the analysis
engine 16 can output results of calculations to provide
corresponding analysis data 36. Thus, the analysis data 36 can
include results data based upon the methods and calculations
performed by the motor calculator 34 and/or the cognitive
calculator 38 based on test data 30 to 32 acquired for each of the
respective test applications.
[0083] In an example embodiment of the invention, the analysis
engine 16 may include an index calculator 40 that is programmed to
compute one or more indices based upon the output results
determined by the motor calculator 34 and/or cognitive calculator
38 for a patient. For example, the index calculator 40 can
aggregate the analysis data determined for a given set of test data
acquired for a given patient to determine an index (or score)
having a value indicative of motor function for the given patient
based on the aggregate set of test data. Alternatively or
additionally, the index calculator 40 can compute an index (or
score) having a value indicative of cognitive function for a
patient based upon the set of test data. Alternatively or
additionally, the index calculator 40 can compute an index (or
score) having a value indicative of cognitive-motor function for a
patient based upon the set of test data. The index calculator 40
can be normalized according to a known scale or index, such as the
UPDRS. Alternatively or additionally, the index calculator 40 can
calculate a new scale that provides an indication of motor and/or
neurocognitive functions for the patient. The resulting output for
the index calculation can be provided and stored as part of the
analysis data 36 for subsequent analysis, e.g., by a clinician who
may access the stored analysis data 36.
[0084] In an example embodiment of the invention, the system may
modify factors used for the index calculation based on the corpus
of data for a plurality of patients. For example, if a large number
of users who are considered generally healthy perform poorly on a
certain test, the test results for that test may be modified by a
low weighting factor.
[0085] In an example embodiment of the invention, as a user takes
one or more tests, the system may generate analysis data 36 which
indicates that the test(s) presented to the user are too difficult
or too easy for the user. For example, where calculated scores are
extremely low, the scores may indicate that the user is below a
certain threshold level of ability, but do not finely indicate the
user's level of ability below that certain threshold. Similarly,
where calculated scores are extremely high, the scores may indicate
that the user's level of ability is above a certain threshold level
of ability, but do not finely indicate the user's level of ability
above that certain threshold.
[0086] Accordingly, in an example embodiment of the invention, the
test engine 12 further includes a module for accessing stored
analysis data concerning a current patient and selecting one of the
TEST applications 1-N to next output to the user based on past
performance indicated by the accessed analysis data. For example,
where the test engine 12 determines from the analysis data that the
user's performance is below a predetermined threshold, the test
engine 12 may select a next test that is ranked as being at a
particular low difficulty level. For example, difficulty may be
ranked according to target accuracy (i.e., the size of the
displayed targets (e.g., relative to calibrated screen size))
and/or distance between the displayed targets (e.g., relative to
calibrated screen size). For example, a test having targets at a
first distance from each other and of a first size may be ranked as
easier than another test of the same type having targets that are
at a second distance from each other, longer than the first
distance, and/or that are of a second size, smaller than the first
size. In an example embodiment, the change in test difficulty may
be implemented by re-administering the same type of test as a
previously administered test, with changes to the target accuracy
and distances and thus changes in the difficulty level of the
re-administered test.
[0087] In an alternative example embodiment, the change in test
difficulty may be implemented by re-administering the same test or
same type of test as a previously administered test, but with
changes to the moving status of at least one target. For example,
an increase in difficulty may involve changing from a stationary
target to a moving target, or changing from a slow-moving target to
a faster target. Similarly, a decrease in difficulty may involve
changing from a moving target to a stationary target, or changing
from a fast-moving target to a slower target. Difficulty may be
ranked according to moving status.
[0088] In an alternative example embodiment, the change in test
difficulty may be implemented by selecting a different type of
test, which test type is ranked at a different difficulty level
than that of a previously administered test.
[0089] In an example embodiment of the invention, during
administration of a test, the analysis engine 16 may produce part
of the analysis data 36 associated with the test, even before
completion of the test. During the test, the test engine 12 may
access the partial analysis data 36 produced for the test prior to
its completion, and may modify the current test during its
administration based on the partial analysis data 36. For example,
during the administration of the test, the test engine 12 may
enlarge previously displayed targets of the test and/or shorten the
distance between the previously displayed targets and/or change
targets between moving and stationary states. Alternatively or
additionally, where the test dynamically displays targets during
its administration, the test engine 12 may display new targets that
are larger than those previously displayed, or at distances that
are shorter than the distances between pairs of previously
displayed targets, or having a different moving status compared to
previously displayed targets.
[0090] FIG. 2 depicts an example of a network system 50, including
an example 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 for use to implement various methods remotely at user
devices indicated at 54, 56 and 58. Each of the user devices 54, 56
and 58 is connected to or can communicate with the system server 52
via a network 60. The network 60 may include a local area network
(LAN), wide area network (WAN) (e.g., the Internet) or a
combination of networks, including private and public domains, as
is known in the art.
[0091] In an example embodiment, the system server 52 may include a
web server having a plurality of different functions and methods,
each of which can be accessed via a corresponding resource locator,
such as a uniform resource location (URL). In an example, the
system server 52 includes an access control function 64 that
provides a level of security such that only authorized users can
access various other functions and methods of the system. The
access control function 64 can provide a log-in user interface
screen to each of the user devices 54, 56 and 58, which can require
a user ID and password for each user for authentication. Each user
ID and password can be associated with a corresponding level of
authorization to selectively provide access to one or more of the
other functions and methods to be provided by the server system 52.
While FIG. 2 illustrates devices 54, 56, and 58 as separate
devices, the operations of each may be performed on a single
device, but may be logically separated according to the log-in
information. In an example embodiment, a single set of log-in
information may provide authorization for access to operations of
more than one of the shown devices 54, 56, and 58.
[0092] For example, the user device 54 can be assigned a high or
unlimited authorization level and utilized to provide one or more
management user interfaces 66, for accessing each of the functions
and methods provided by the server system 52, including accessing
corresponding management functions and methods indicated
schematically at 67. Thus, the authorized user of the management
user interface 66 can access various management functions 67 for
the patient information, browsing test data and the like. For
example, the user interface 66 may be a clinician interface via
which a clinician may access test results for tests taken by the
clinician's patients or other related information. Examples of
selected management user interface screens are shown in FIGS. 26
and 27.
[0093] The user device 56 can include a patient user interface 68
that can provide a limited amount of access such as to the testing
functions and methods 70. For example, after logging in via the
access control function 64, a patient user interface 68 can be used
to access the test applications 70, which can be graphically
displayed in the patient GUI. The test applications can be provided
as interactive web pages programmed with functions and methods for
performing various tests and obtaining patient-controlled movement
information from the patient-user.
[0094] The test methods 70 that are provided to the patient user
device 56 via the patient user interface can be implemented using
ADOBE.RTM. FLEX.RTM. or another similar software or platform having
a high market penetration. By having a sufficiently high market
penetration, substantially no software needs to be installed or
loaded onto an individual user's device. In response to interaction
with the tests provided via the test methods 70, test data 30 to 32
may be obtained by the system server 52 for storage in a central
data storage 74 (described in further detail below). Methods of the
analysis engine 16 may be performed locally at the patient user
device 56 or remotely at the system server 52.
[0095] Examples of associated graphical user interfaces associated
with the testing functions and methods that can be presented to the
user are shown and described herein with respect to FIGS. 4-20.
[0096] Another user device 58 can include a research user interface
72 that provides access to relevant data (e.g., excluding patient
identifying information) such as to facilitate research and
analysis of the test data. For example, a researcher or other
authorized user can access a set of test data for a plurality of
patients and, in turn, perform statistical methods or other
mathematical operations on the set of data to ascertain relevant
information, such as correlations or likelihoods. A researcher
might also utilize the analysis data to draw correlations between
other information entered by the user (e.g., patient data,
including an identification of medications, dosage and the like)
relative to test results for each of a plurality of users. Such
analysis can provide information that can be stored in the central
data storage 74 for subsequent usage and review by other authorized
users. For example, correlations can be drawn between medication
and test results and changes in test results over time, which
correlations can be presented to a physician or other authorized
user via the management user interface 66.
[0097] Alternatively or additionally, certain patient devices can
be preconfigured having a preset authorization status, such that
authorization would not be required. Such devices known to the
system server 52 can access the system server 52 through the
network 60 or through a secure local area network or other suitable
connection. For instance, such preconfigured terminals can reside
at doctors' offices, hospitals or other institutions.
[0098] In an example embodiment, regardless of the configuration
and distribution of patient user devices 56, the test data is
consolidated into a database or other central data storage 74 that
is associated with the central system server 52.
[0099] The central data storage 74 can include raw test data 76 and
results data 78. While central storage 74 is shown as a single
storage device and/or logical storage location, in an example
embodiment, the raw test data 76 may be stored separate from the
results data 78, e.g., for quicker response time to results data
queries.
[0100] The raw test data 76 and results data 78 can be indexed by
patient and by individual test as well as include patient specific
information (in an example embodiment, excluding patient
identifying information other than perhaps a patient unique
identify number) for purposes of separating the patient data from
one patient from that of another patient. As described herein, the
test methods 70 and the system server 52 can be programmed to
perform calculations on the raw test data acquired from a patient
user interface via the testing application being implemented
thereon.
[0101] Similarities between patients residing in a given cluster
(i.e., patients who share certain characteristics) can be utilized
to facilitate treatment and diagnosis of other patient's having
similar conditions. For example, a clinician may enter information
(e.g., patient information with respect to medication (type and/or
dosage), stimulation parameters (e.g., of a DBS therapy), symptoms,
conditions, and/or diagnoses) via the management user interface 66,
which can be tagged (or programmatically linked) to the test data
and results data of the patient, such as to augment or provide
metadata that can be further evaluated or considered to facilitate
clustering of patients and understanding the respective conditions.
In this way, the test data for a more statistically significant
population can be maintained for performing statistical analysis of
test data, which can be mined or otherwise evaluated statistically
or otherwise, e.g., via the researcher user interface, to
understand the correlations of symptoms and conditions. For
example, the system of the invention may be queryable for test
result data by symptom(s) and/or diagnosis, in response to which
the system may return results data 78 concerning those patients
matching the symptom(s) and/or diagnosis, and/or averages and/or
other aggregate data of the results data 78.
[0102] In an example embodiment, the system and method of the
invention may provide for a clinician, using the user device 54, to
input a proposed change, e.g., with respect to medication (type
and/or dosage) and/or stimulation parameters (e.g., of a DBS
therapy), for a particular patient for whom patient information and
test results have been obtained. In response to a query triggered
via user-selection of a command at the user device 54, the system
server 52 may search the central data storage 74 for patients
associated with patient data and test data similar (by a
predetermined degree) to those of the particular patient. The
server may further search for those of the patients who have been
subjected to a change similar to that proposed for the particular
patient and for whom subsequent test results data have been
obtained. The server may output for display at the user device 54
an average of such subsequent test results, thereby indicating to
the clinician an expected change in the particular patient's
condition with the proposed change, measured in terms of expected
change in test results.
[0103] Alternatively or additionally, the system may output a
medical condition category corresponding to the average of such
subsequent test results. For example, different intervals of test
scores may be associated in memory with different categories of
cognitive and/or motor skills. The category under which the average
of the subsequent test results falls may be output. Alternatively
or additionally, the expected direction of change to the medical
condition classification may be output, e.g., whether the cognitive
and/or motor skills are expected to improve or decline.
[0104] FIGS. 3 through 27 show example screen shots or other
graphics for presentation in a user interface, to provide a general
understanding of the algorithms and functionality that can be
implemented by the systems 10 and 50 shown and described with
respect to FIGS. 1 and 2.
[0105] FIG. 3 depicts an example of a screen shot 100 including a
GUI element 102 that can be utilized for access control into the
system, as described in detail above. The GUI element 102 includes
user entry fields 104 that can be utilized for obtaining a user
name and access code for authorized use of the system. Graphical
buttons or others graphical interface elements 106 can be provided
for submitting or clearing information with respect to the user
entry fields 104.
[0106] FIG. 4 depicts an example of a screen shot 110 including an
example of a GUI element 112 for obtaining information pertaining
to a user's general health condition, state of mind and environment
in which the test is being taken. For example, the questions may
include: "How many hours of sleep did you get last night?"; "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
can be a drop down context menu 114 that can be utilized by the
user to identify and select one of a predetermined number of
responses. After answers to the question(s) have been entered, a
user can hit a continue user interface element (a graphical button)
116 to continue.
[0107] FIG. 5 depicts another screen shot example 120 that can be
utilized to obtain information about medication that a given
patient may be taking. The screen shot 120 includes a GUI element
122 having a variety of drop down context menus that can be
utilized to identify medication(s), dosage, number of times per day
the medication(s) is taken, and time(s) since last dosage of the
medication(s). After the particulars associated with a given
medication have been entered via the drop down context menus 124, a
user may enter them into the system via an add user interface
element 126. Similarly, an entry can be deleted or removed by
selecting it with a cursor or other user interface element and in
turn hitting a delete user interface element 128. After all
medications have been appropriately entered into the medication
form GUI element 122, a user can continue to the next phase of the
testing process by hitting a user interface element or button 130.
The medication information can be programmatically associated with
test data to allow correlations to be determined, such as described
herein. In an example embodiment, a clinician may enter some or all
of the medication and/or other therapy information into the
system.
[0108] FIG. 6 depicts an example of a screen shot 134 demonstrating
a calibration GUI 136 that can be implemented for calibrating a
remote user's computing device in a horizontal direction, according
to an example embodiment of the invention. The calibration GUI 136
presents the user a scale 138 having a plurality of spaced apart
markings or indicia, which are numbered consecutively in the
example of FIG. 6 from zero to thirty. The calibration GUI 136
presents instructions to the user to fold a 8.5''.times.11'' sheet
of paper in half and place the shorter end of the folded sheet of
paper adjacent the scale 138 with the one of the longer sides
against the zero, and to enter the number closest to the other of
the longer sides in a user entry dialogue box 140. The number
entered into the dialogue box 140 relative to the actual size of
the paper can be utilized to determine a size or dimensions of the
display area presented on a user screen during the testing process.
A similar calibration can be utilized in a vertical direction on a
user screen such that both the horizontal and vertical dimensions
can be known such that the results from the testing can be scaled
appropriately.
[0109] FIG. 7 depicts an example of a "welcome" screen shot that
can be presented to the user to inform the individual that a test
is about to begin and identify some additional information about
the types of the test and how they will proceed. It should be
understood that a few practice screens and tests can be implemented
before beginning an actual test to familiarize a user with the
testing process.
[0110] FIG. 8 depicts an example of an instruction GUI 146 that can
be provided before performing a first test. FIGS. 9 and 10 depict
an example of GUI 150 that includes a plurality of targets 152 for
use in performing a "seven's test", for which a user is instructed
to draw between targets a path having a shape similar to the number
"7", for testing cognitive and/or motor skill. Each of the targets
can be graphically constructed, as a graphical object that
encompasses a region in the X/Y coordinates of a patient's
graphical interface, such as in a screen. The GUI 150 corresponds
to an example of a traditional "seven's test" in which a user is
instructed to connect the dots using a pointing device such as a
mouse, stylus, touch screen or the like. In the example of FIG. 9
three targets numbered numerically 1, 2, 3 are presented on the
screen. A user is instructed to connect the targets 1 to 2 to 3 to
draw a shape similar to the number "7". The system may record test
data in association with performance of the test. The test data may
include, for example, the position and temporal information of the
path taken for connecting the targets. FIG. 10 shows an example of
an outcome of a patient having connected target positions 1 and 2
but not target positions 2 and 3. To indicate the successful
connection of target positions 1 and 2, the system has highlighted
targets 1 and 2, in contrast to target 3 which is not highlighted.
Referring back to FIG. 1, the test data acquired from FIG. 9 can
include an identification of the position of each of the targets, a
path taken by the patient for connecting or attempting to connect
the targets, and/or temporal information associated with the
path.
[0111] Thus, the information obtained with respect to the test
outcome shown in FIG. 10 may include the coordinates of each of the
targets 152 as well as the coordinates (or position) of the cursor
or other pointing element during the test as the cursor or other
pointing element moves between the respective targets and forms a
corresponding line or path 154.
[0112] FIG. 11 depicts an example of an instruction GUI 158 that
can be provided before performance of a second test.
[0113] FIGS. 12 and 13 depict an example of a GUI 160 that can be
provided in connection with performing a reaction time test with
subsequent movement, such as a center-out test, for testing, e.g.,
cognitive ability. Thus, the GUI 160 includes a plurality of
targets, including the center target 162 and a plurality of outer
targets 164 in circumscribing relation relative to the center
target. The center-out test can be performed to test reaction time
of the user by displaying one of the outer targets 164 in a
contrast color relative to the other targets 162 and 164 and in
turn storing the time interval from displaying the contrasted
target on the GUI 160 to the time the user begins to move a
pointing device for connecting the central target 162 to the
contrasted outer target 164. Additional information can be obtained
during the process, including the position over time of the cursor
relative to each of the respective graphical renderings of the
targets, e.g., representing a path taken by the user between the
center target 162 and the outer targets 164, and/or the
corresponding times.
[0114] In FIG. 13, a partially completed center-out test is
depicted showing one of the targets 166 having a contrast color
relative to the other targets thereby designating the intended
target for connection between the center target 162 and the
contrast target 166. The center-out test can be performed such that
different ones of the outer targets are selected in a predictable
or random order one or more times.
[0115] FIG. 14 depicts an example of another instruction GUI 168
that can be presented to a user for providing instructions for
performing a third test, including such as shown and described with
respect to FIGS. 15 and 16.
[0116] FIGS. 15 and 16 depict an example of a test GUI 170 that can
be utilized for performing a trail making test (Part A), for
testing cognitive and/or motor skill. The trail making test (Part
A) implemented via the GUI 170 may be used to assess both motor and
cognitive information concurrently. For instance, a plurality of
targets 172 are distributed across 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 (as instructed by the
instruction GUI 168 of FIG. 14) is to connect the targets in a
sequential order. The test engine can populate the display area for
the GUI 170 in a pseudo random fashion such that each of the
sequential targets can be interconnected by an ideal straight line
without crossing a line interconnecting any other sequential
targets. Thus, in addition to obtaining the position, velocity,
speed and/or acceleration information, crossing lines can also be
identified to provide a further indication of a patient's motor and
cognitive function.
[0117] FIG. 16 depicts an example in which a patient has connected
the first five targets with lines going from target 1 to target 2
to target 3 to target 4 and to target 5. Thus, from the example
tests of FIGS. 15 and 16 information corresponding to the position
of the cursor that is utilized to draw each line connected between
sequentially numbered targets can be recorded and stored as test
data in memory (e.g., local or associated with a server). In
addition to the position data, temporal data can be obtained with
each sample as well. Thus, the position and time data can then be
provided as test data to the analysis system for evaluation, such
as shown and described in further detail below.
[0118] Also depicted in FIG. 16 is a diagrammatic view of a line
used in an analysis that can be performed to characterize a degree
of error, e.g., by calculating an average error, a mean square
error, or a root mean square error, for each of a plurality of
respective lines interconnecting sequential targets 172 in the GUI
170. For example, referring to targets 2 and 3, this can be
performed, for example, by comparing the relative positions of
points along an ideal straight line 250 connected between targets 2
and 3 relative to a line segment 252 drawn by a patient (e.g.,
responsive to user-controlled movement with a pointing device)
between the same respective targets. For instance, the same number
of equally spaced sample points can be populated along the length
of each line segment 250 and 252 and a corresponding means square
error can be computed for differences between the sets of sample
points. For example, the error values recorded for the sample
points can be squared, then summed together, and then divided by
the number of sample point pairs to provide the mean square error
of the patient's line 252 relative to the ideal line segment 250.
Those skilled in the art will understand and appreciate various
types of estimators that can be utilized to compute a measure of
how close the user's line 252 is to the fitted ideal (straight)
line 250 between targets, such as including the sample mean, sample
variance, analysis of variance, root mean square error, standard
deviation as well as linear regression techniques.
[0119] For example, the resulting mean square error can further be
utilized to compute a root means square error by taking the
square-root of the mean square error for each of the line segments
between targets. The root means square error thus can provide
essentially an average measure of distance of the user's data
points on the line 252 from corresponding points on the ideal line
250.
[0120] FIG. 17 depicts an example of an instruction GUI 180 that
can be provided for instructing a user for a trail making test
(Part B) test such as shown in FIGS. 18 and 19, for testing
cognitive and/or motor skill.
[0121] FIGS. 18 and 19 depict an example of a GUI 182 that can be
presented to a user in connection with performing and recording
information associated with a trail making test (Part B). The GUI
182 presents a plurality of targets positioned in a display area
according to application data determined by a corresponding test
application. In the examples 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 portion
of the targets, indicated at 184, have letters ranging
consecutively from A through H and another corresponding portion of
the targets, indicated at 186, have numbers ranging consecutively
from 1 through 13. Those skilled in the art will understand that
the test engine can be programmed to automatically generate any
arrangement of targets consistent with the format of the trail
making test (Part B), which arrangement may be part of the test
data provided to the analysis engine 16.
[0122] The trail making test (Part B) implemented by GUI 182 may be
used to assess both motor and cognitive information concurrently.
For instance, the instructions (e.g., via the instruction GUI 180
of FIG. 17) specify that a user-patient is to alternate between
consecutive sequential letters and numbers by connecting respective
targets with straight lines, similar to what is shown in FIG. 19 up
to letter E, beginning with the lowest number to the lowest letter,
to the second lowest number, to the second highest letter, etc.
Thus, FIG. 19 shows an example outcome of a test in which a user
has used a cursor having a position that can be tracked via the
corresponding API. The system is configured to dynamically render a
graphical depiction of a line onto the display GUI 182 in response
to movements of the cursor, for example, via a corresponding
pointing element, such as a mouse, stylus or touch screen.
Information associated with the position and times associated with
the positions, representing times for each of the movements, can be
recorded for subsequent analysis and evaluation as described
herein.
[0123] FIG. 20 depicts an example embodiment in which the user
computing device for performing a test is implemented as a tablet
personal computer (PC). Thus, in this example a user holds a stylus
(similar to a pen) on a corresponding touch screen for drawing
interconnecting lines between targets, such as is shown in FIG. 18.
It is understood that a user could use the user's fingers to draw
the interconnecting lines.
[0124] FIG. 21 depicts an example of analysis data that can be
generated and displayed in a GUI 190 as a function of test data
acquired from a respective test. The resulting analysis data can be
presented in a variety of formats, which may be selected by a user.
In the GUI 190, a plurality of different plots are shown for
depicting different information that can be computed based upon the
acquired position and respective time data for a given test. Each
of the plots in the top row indicates position information, the
middle row of plots indicates velocity information and the bottom
row indicates acceleration information for a given test. The
position data can be correlated into corresponding velocity and
acceleration information by analysis of change over time of the X
and Y coordinates of the cursor obtained from the samples recorded
during a test. In the example of FIG. 21, the GUI depicts analysis
data for a "seven's test" such as shown and described with respect
to FIGS. 9 and 10. The GUI 190 can be presented via a management
user interface 66 or researcher user interface 72 for analysis and
evaluation by an authorized user.
[0125] By way of example, the plot 192 depicts X position versus Y
position, thereby showing the graphical object as a pair of
interconnected line segments in a relative coordinate system based
upon user input with a corresponding pointing device, representing
the path the user took between the targets of the "seven's test." A
representative plot 194 shows the X data of plot 192 plotted as a
function of time, and plot 196 shows the Y data of plot 192 plotted
as a function of time.
[0126] A plot 198 depicts the velocity information corresponding to
the changes in the X,Y positions plotted in plot 192 over the time
period in which the changes occurred, i.e., during the test. Plot
200 depicts velocity in the X direction with respect to time such
as by taking the change between plotted positional points in plot
194 over the plotted time in which such change occurred. Similarly,
plot 202 depicts velocity of the Y direction with respect to time
such as by taking the change between plotted positional points in
plot 196 over the plotted time in which such change occurred.
[0127] Similarly, FIG. 23 depicts a plot 230 of velocity as a
function of time (cm/sec) that can be obtained from data acquired
from a trail making test (Part A) implemented according to an
aspect of the invention. An enlarged view of a portion 232 of the
waveform formed by the plot 230 is depicted in FIG. 24 at 240.
[0128] Referring again to FIG. 21, another set of plots 204, 206
and 208 depict the acceleration for each of the respective curves.
For instance, the plot 204 displays a plot of acceleration versus
time, such as by taking the change in velocity values of plot 198
over the corresponding time period in which such change occurred.
The plot 206 corresponds to the acceleration in the X direction
versus time such as by taking the change in velocity values of plot
200 over the corresponding time period in which such change
occurred. Similarly, the plot 208 displays a plot of acceleration
in the Y direction versus time, such as by taking the change in
velocity values of plot 202 over the corresponding time period in
which such change occurred.
[0129] The separate data concerning the movement in the X and Y
directions, respectively, e.g., one or more of plots 194, 196, 200,
202, 206, and 208, may be used, for example, to characterize skill
with respect to different directions.
[0130] As noted above, the index calculator 40 may calculate a
score characterizing a test taker's performance on one or more
administered tests. In an example embodiment of the invention, the
analysis engine 16 may compare an overall curve shape of one or
more of the types of graphs shown in FIG. 21, e.g., which plot
velocity and/or acceleration, to stored graph shapes. For example,
the shape of a plotted velocity or acceleration may be compared to
a stored smooth bell-shaped curve, which may be considered to
represent ideal motion by a healthy person when taking a test. The
analysis engine 16 may score the graphs of the test taker's motion,
such that the closer the shapes of the graphs to the stored graph
shapes, the higher the score. Similarly, the analysis engine 16 may
determine the extent (with respect to number and/or degree) to
which the graph(s) include spikes, where such spikes may be used as
indications of low quality movement including significant and/or
many corrective and/or tremor-like motions.
[0131] The graph shape score(s) may be used, for example, by the
index calculator 40, to calculate the index, which may be stored
and output, for example, via the management user interface 66. It
is noted that the index may be based on a number of factors. In an
example embodiment, different factors, e.g., including the graph
shape score, may be multiplied by respective weighting values, for
example, depending on ranked significance with respect to the
overall index.
[0132] By way of further example, FIG. 22 is a reproduction of the
GUI 190 shown in FIG. 21 in which selected portions of position and
kinematic data have been identified by circles 220 corresponding to
relevant data that can be utilized by the cognitive calculator 38
for computing dwell time. For example, dwell time can correspond to
an amount of time that a cursor or other user-controlled graphical
interface element resides within a bounded region, such as a
defined border of a target. Such bounded regions can be identified
in the testing data according to position data (e.g., X and Y
coordinates) for each of the targets populating a test GUI. The
identified regions for which dwell time is calculated can be
identified by identifying the X and Y positions corresponding to
each time during which no change occurs in the X and Y position or
a period in which there is no velocity (e.g., from plot 198).
[0133] FIG. 25 depicts example plots 2050 and 2052 of dwell time
that can be computed for a trail making test (Part A) and a trail
making test (Part B), respectively. The abscissa corresponds to
target number displayed in the trail making tests, and the ordinate
corresponds to the percentage of the overall time of the duration
of the administered tests in which the cursor dwelled in the
corresponding target. As described above, the dwell time
corresponds to a time during which a cursor/pointing object is
within a given pre-defined X-Y position range that encompasses a
displayed target. Thus, the dwell time can be determined by
correlation of position information (e.g., indicating that the
cursor is within a bounded target) and velocity information (e.g.,
indicating that the cursor is either not moving or is moving within
the bounded target at a rate that is below a predetermined
threshold). It will be appreciated that motor function information
can also be acquired concurrently with cognitive data represented
by dwell time by computing and analyzing corresponding kinetic
information. For example, in PD patients, tremor predominantly
occurs when the patient is in a resting condition. That is, when
the patient's hand, for example, is purposefully moving, there is
little, if any, tremor, while, when the patient's hand is not
purposefully moving and is in an essentially resting position,
there may be significant tremor, e.g., at a substantially constant
3-8 Hz frequency. Accordingly, the dwell time information may be
used as an indicator of which data is significant for measuring
tremor in PD patients. For example, the system may determine a
measure for tremor from data corresponding to where there was a
determined dwell period, in which the user's hand was essentially
in a resting position. This concurrent kinetic information can be
employed to assess motor function (e.g., the patient could have
some small movements during this time, especially if they have
tremor) while cognitive function (e.g., pertaining to information
processing and set switching) during this time is also
analyzed.
[0134] In an example embodiment of the invention, the system and
method may be used to administer and obtain data for certain tests
used to measure only motor function, e.g., related to finger
tapping or tapping between two points. In an example embodiment of
the invention, the system and method may be used to administer and
obtain data for certain tests used to measure only cognitive
function, e.g., the Mini Mental State Exam or Raven's Progressive
Matrices tests.
[0135] FIG. 26 depicts an example of a management GUI 260 that can
be utilized to provide access to test data by a user having an
appropriate level of authorization. Each set of test data can be
associated with a patient via name or other identifying
information. As shown in the GUI 260, there can be any number of
raw data elements for a given patient, which may generally depend
on the test or tests that have been conducted. Each set of raw
data, for example, can correspond to a separate set of test data
for a given one of the tests or phases (or repetition) of a given
test.
[0136] FIG. 27 depicts a GUI 270 that can be utilized for managing
protocols such as through a management user interface 66
implemented in a system. The protocols management GUI 270 can be
utilized, for example, for identifying testing protocols being
utilized for a given patient test process. In the example of FIG.
27, the GUI includes selection interface elements 272 that can be
utilized to identify protocols set for a patient, such as
indicating whether a deep brain stimulator was on or off during the
respective tests or the medications and/or dosage thereof
administered to the patient at the time of the respective tests. A
user, such as a clinician, thus can select a set of protocols
associated with a given patient to help understand the effect a
given condition has relative to the set of test data acquired for
each patient during a given test session.
[0137] For instance, these protocols can be implemented and
corresponding sets of test data evaluated to ascertain the effects
on various conditions such as whether a DBS is on during the test
or off as well as whether a patient is on their medication at a
prescribed dose or not, and the effect of such a condition on the
performance of a test. Those skilled in the art will understand
various other protocols and combinations of protocols that can be
utilized for specifying patient control parameters associated with
a given set of tests.
[0138] In view of the foregoing, it will be appreciated that
systems and methods have been described that can be implemented to
provide a battery of cognitive and motor tests to remotely assess
neurological disorders such as neurocognitive and neuromotor
disorders such as, for example, PD, Alzheimer's disease, multiple
sclerosis, dementia, amyotrophic lateral sclerosis (ALS),
Parkinsonian syndrome, trauma-induced brain injury, stroke and
multiple systems atrophy(MSA). The systems and methods enable the
testing to be performed remotely by a patient-user, and the
collection of data in a central data repository, such as to provide
access to such information by a clinician and to facilitate further
research.
[0139] In view of the foregoing structural and functional
description, those skilled in the art will appreciate that portions
of the invention may be embodied as a method, data processing
system, or computer program product. Accordingly, these portions of
the invention may take the form of an entirely hardware embodiment,
an entirely software embodiment, or an embodiment combining
software and hardware, such as shown and described with respect to
the computer system of FIGS. 1 and 2. Furthermore, portions of the
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 including, but not limited to, static and dynamic
storage devices, hard disks, optical storage devices, and magnetic
storage devices.
[0140] Certain embodiments of the invention have been described
herein with reference to block illustrations of methods, systems,
and computer program products. It will be understood that blocks of
the illustrations, and combinations of blocks in the illustrations,
can be implemented by computer-executable instructions. These
computer-executable instructions may be provided to one or more
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus (or a combination
of devices and circuits) to produce a machine, such that the
instructions, when executed by the processor, implement the
functions specified in the block or blocks.
[0141] These computer-executable instructions may also be stored in
computer-readable memory that can direct a computer or other
programmable data processing apparatus to function in a particular
manner, such that the instructions stored in the computer-readable
memory result in an article of manufacture including instructions
which implement the function specified in the flowchart block or
blocks. The computer program instructions may also be loaded onto a
computer or other programmable data processing apparatus to cause a
series of operational steps to be performed on the computer or
other programmable apparatus to produce a computer implemented
process such that the instructions which execute on the computer or
other programmable apparatus provide steps for implementing the
functions specified in the flowchart block or blocks.
[0142] In this regard, FIG. 28 illustrates one example of a
computer system 500 of the type that can be utilized to implement
one or more embodiments of the systems and methods described herein
for testing and analyzing motor and cognitive function of a
patient. The computer system 500 can be implemented on one or more
general purpose networked computer systems, embedded computer
systems, routers, switches, server devices, client devices, various
intermediate devices/nodes and/or stand alone computer systems.
Additionally, the computer system 500 or portions thereof can be
implemented on various mobile or portable clients such as, for
example, a laptop or notebook computer, a personal digital
assistant (PDA), and the like.
[0143] The system 500 may include a computer 502, which may
function, for example, as any of the user devices 54, 56, and 58
and/or the server 52. The computer 502 may include a system bus 508
may include any of several types of bus structures, including, for
example, a memory bus or memory controller, a peripheral bus, and a
local bus using any of a variety of conventional bus architectures
such as peripheral component interconnect (PCI), video electronics
standards association (VESA), Microchannel, industry standard
architecture (ISA), and extended industry standard architecture
(EISA), to name a few. The system memory 506 may include read only
memory (ROM) 510 and/or random access memory (RAM) 512. A basic
input/output system (BIOS), containing the basic routines that help
to transfer information between elements within the computer 502,
such as during start-up, may be stored in ROM 510.
[0144] The computer 502 also may include, for example, a hard disk
drive 514, a magnetic disk drive 516 (e.g., a floppy drive), e.g.,
to read from or write to a removable disk 518, and an optical disk
drive 520 (e.g., a CD-ROM drive), e.g., for reading from or writing
to a CD-ROM disk 522 or other optical media. 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 drives and their associated
computer-readable media provide nonvolatile storage of data, data
structures, computer-executable instructions, etc. for the computer
502. Although the description of computer-readable media above
refers to a hard disk, a removable magnetic disk and a CD, it
should be appreciated by those skilled in the art that other types
of media which are readable by a computer, such as magnetic
cassettes, flash memory cards, digital video disks, Bernoulli
cartridges, and the like, may also be used in the exemplary
operating environment 500, and further that any such media may
contain computer-executable instructions for performing the methods
of the invention.
[0145] A number of program modules may be stored in the drives and
RAM 512, including an operating system 530, one or more application
programs 532, other program modules 534, and program data 536. The
operating system 530 in the computer 502 could be any suitable
operating system or combinations of operating systems. The
application programs 532, other program modules 534, and program
data 536 can cooperate to provide motor and cognitive testing on a
patient computer device, such as shown and described above.
Additionally, application programs 532, other program modules 534,
and program data 536 can be used for computation of an indication
of motor, cognitive or a combination of motor and cognitive
functions of a patient based on the testing data, such as shown and
described above.
[0146] 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, a joystick, a game pad, a scanner,
touch screen, or the like. The mouse or other pointing device can
be utilized to perform a point-and-click action, which includes the
action of a computer user moving a cursor to a certain location on
a screen (point) and then pressing a mouse button, usually the left
button (click), or other pointing device. Such point-and-click can
be used with any number of input devices varying from mice, touch
pads, keyboards, joysticks, scroll buttons, and roller balls. The
information associated with such point and click operations can be
provided (e.g., to a central server) as part of the test data for
each of the respective tests, such as described herein.
[0147] These and other input devices are often connected to a
processing unit 504 through a serial port interface 542 that is
coupled to the system bus 508, but may be connected by other
interfaces, such as a parallel port, a game port or a universal
serial bus (USB). A display device 544, such as a monitor, is 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.
[0148] The computer 502 may operate in a networked environment
using logical connections to one or more remote computers 560. The
remote computer 560 may be a workstation, a server computer, a
router, a peer device, or other common network node, and typically
includes many or all of the elements described relative to the
computer 502, although, for purposes of brevity, only a memory
storage device 562 is illustrated in FIG. 28. The logical
connections depicted in FIG. 28 may include a LAN 564 and/or a WAN
566. Such networking environments are commonplace in offices,
enterprise-wide computer networks, intranets, and the Internet.
[0149] When used in a LAN networking environment, the computer 502
is connected to the LAN 564 through a network interface or adapter
568. When used in a WAN networking environment, the computer 502
typically includes a modem 570, or is connected to a communications
server on an associated LAN, or has another circuitry arrangement
for establishing communications over the WAN 566, such as the
Internet. The modem 570, which may be internal or external, is
connected to the system bus 508 via the serial port interface 542.
In a networked environment, program modules depicted relative to
the computer 502, or portions thereof, may be stored in the remote
memory storage device 562 (and/or locally). It will be appreciated
that the network connections shown are exemplary and other
arrangements for establishing a communications link between the
computers 502 and 560 may be used.
[0150] Another example embodiment of a system and methods relating
to the assessment of cognitive and neuromotor functioning will now
be described. The approach can be utilized to aggregate performance
variables that characterize cognitive and neuromotor functioning of
a patient, each of which can involve one or more tests and
corresponding test data.
Analysis Engine
[0151] FIG. 29 depicts an example of an analysis engine 17 that may
be used as an alternative to the analysis engine 16 in FIG. 1. The
analysis engine 16 may include at least one of a cognitive
functioning calculator 33 and a neuromotor functioning calculator
35. Each of the respective calculators can compute performance
variables characterizing a respective function of the user based on
test data, collectively demonstrated at 31, which can be stored in
memory, such as can be obtained in response to user interactions
with a device used to perform the test, such as disclosed herein.
When implemented with the test engine 12 of FIG. 1, the test engine
12 may provide for cognitive-related, and neuromotor-related test
data 17 to be transmitted to or otherwise made accessible by the
analysis engine 17 for analysis.
[0152] The analysis engine thus can combine test results from
cognitive and neuromotor domains. The cognitive domain may include
functions such as memory/recall, information processing ability and
set switching. Cognitive functioning may be evaluated using a Sport
Concussion Assessment Tool (SCAT) test (which is a standard
questionnaire test for concussion injuries) and testing working
memory, set-switching or delayed recognition. Additionally or
alternatively, cognitive functioning can be evaluated using one or
more of a sevens test, a trail making test, a clock drawing test, a
center-out test, an Archimedes spiral test, a judgment of line
orientation test or the like, such as shown and disclosed herein in
relation to FIGS. 1-27. Thus, the cognitive functioning can be
evaluated according one or more of the cognitive and/or neuromotor
testing and analysis approaches shown and described above.
[0153] The neuromotor domain may include reaction time and
coordination. Neuromotor functioning may be evaluated by testing
reaction time, such as disclosed herein. Examples of reaction time
tests can include simple reaction time (SRT) and choice reaction
time (CRT). For example, SRT may involve displaying an image and
instructing the patient to press a button as soon as the image is
displayed. CRT may involve displaying an image in one of a
plurality of display locations (e.g., a left side or a right side
of the display) and instructing the user to select the correct
display location (e.g., touching the image when the image is
displayed on a touch screen).
[0154] In an example embodiment of the invention, the calculators
33/35 are programmed to compute variables or parameters relevant to
assessing the functions of their respective domains based on
corresponding test data 17. For example, the neuromotor functioning
calculator 35 may compute one or more variables relating to SRT or
CRT, e.g., the duration between when a stimulus is displayed and
when the patient inputs a valid response.
[0155] The index calculator 39 may be programmed to compute one or
more indices (e.g., also referred to herein as scores) based upon
the output results determined by each of the calculators 33/35 for
a patient. For example, the index calculator 39 can aggregate the
analysis data determined for a given set of test data acquired for
a given patient to compute an index (or score) having a value
indicative of postural stability for the given patient based on the
aggregate set of test data (e.g., gyroscope data may be aggregated
with accelerometer data to determine postural sway). Alternatively
or additionally, the index calculator 39 can compute indices (or
scores), each value of which is indicative of one of a SCAT or
SCAT2 test (e.g., a SCAT score), working memory, set-switching,
delayed recognition, SRT or CRT. The resulting output for the index
calculation can be provided and stored as part of analysis data 41
for subsequent analysis, e.g., by a clinician who may access the
stored analysis data 41. The analysis data 41 may be input to a
visualization module 43 for subsequent display, as disclosed
hereinabove (see, e.g., FIGS. 1-27).
[0156] What have been described above are examples and embodiments
of the invention. It is, of course, not possible to describe every
conceivable combination of components or methodologies for purposes
of describing the invention, but one of ordinary skill in the art
will recognize that many further combinations and permutations of
the invention are possible. For example, the dimensions and
configurations of the targets and the types of user-controlled
movement task each patient is instructed to perform can vary from
the particular examples shown and described herein.
* * * * *