U.S. patent application number 14/021321 was filed with the patent office on 2014-04-10 for computerized systems and methods for stability-theoretic prediction and prevention of falls.
This patent application is currently assigned to Cemer Innovation, Inc.. The applicant listed for this patent is Cemer Innovation, Inc.. Invention is credited to Douglas S. McNair.
Application Number | 20140100487 14/021321 |
Document ID | / |
Family ID | 44342232 |
Filed Date | 2014-04-10 |
United States Patent
Application |
20140100487 |
Kind Code |
A1 |
McNair; Douglas S. |
April 10, 2014 |
Computerized Systems and Methods for Stability-Theoretic Prediction
and Prevention of Falls
Abstract
A system, methods, A system, methods and computer-readable media
are provided for the automatic identification of patients according
to near-term risk of sudden kinematic injury (falling). Embodiments
of the invention are directed to event prediction, risk
stratification, and optimization of the assessment, communication,
and decision-making to prevent falling in humans, and in one
embodiment take the form of a platform for wearable, mobile,
unteathered monitoring devices with embedded decision support.
Inventors: |
McNair; Douglas S.;
(Leawood, KS) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cemer Innovation, Inc. |
Kansas City |
KS |
US |
|
|
Assignee: |
Cemer Innovation, Inc.
Kansas City
KS
|
Family ID: |
44342232 |
Appl. No.: |
14/021321 |
Filed: |
September 9, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
12982631 |
Dec 30, 2010 |
8529448 |
|
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14021321 |
|
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Current U.S.
Class: |
600/595 |
Current CPC
Class: |
A61B 5/7405 20130101;
A61B 5/00 20130101; A61B 5/742 20130101; A61B 5/6831 20130101; A61B
5/6898 20130101; A61B 5/7275 20130101; A61B 5/6891 20130101; A61B
5/7203 20130101; A61B 5/746 20130101; A61B 5/1117 20130101; A61B
5/0452 20130101; A61B 5/747 20130101; A61B 5/0004 20130101; A61B
5/7264 20130101 |
Class at
Publication: |
600/595 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/11 20060101 A61B005/11 |
Claims
1. Computer-readable media having computer-executable instructions
embodied thereon that when executed, facilitate a method for
predicting a sudden kinematic event (falling) in humans, the method
comprising: receiving motion information representative of motion
of an individual; determining a level of instability of motion
dispersion or other measurements based on the motion information;
and determining that the individual has an increased risk for
falling based on the determined level of instability of motion
dispersion or other measurements; wherein the motion information
comprises information about the individual's motion over one or a
plurality of previous time intervals.
2. The computer-readable media of claim 1, wherein determining the
level of instability involves detecting the presence of chaos or
trajectory divergence instability.
3. The computer-readable media of claim 1, wherein determining a
level of instability of motion dispersion or other measurements
comprises determining a motion dispersion stability index (MdSI)
based on the received motion information.
4. The computer-readable media of claim 3, wherein the motion
dispersion stability index (MdSI) is determined utilizing an
objective function.
5. The computer-readable media of claim 4, wherein the objective
function evaluates digitized kinematic waveforms from the one or a
plurality of previous time intervals to classify the likelihood of
a cascade of events leading to falling within a future time
interval.
6. The computer-readable media of claim 4, wherein the objective
function comprises a timeseries calculated from serially-acquired
waveform data embodying the Lyapunov exponent.
7. The computer-readable media of claim 4, wherein the results of
the objective function are used by a decision-support algorithm to
determine a quantitative risk for falling.
8. The computer-readable media of claim 1, wherein the motion
information is received at a mobile communication device carried by
the individual.
9. A method to prevent injury from a sudden kinematic event
(falling) in a population of humans, the method comprising
providing an electronic notification to a caregiver of a member of
the population, or a member of the population, who exhibits an
above-reference value of a predicted risk for falling calculated
from a timeseries of motion information acquired from the member
over one or a plurality of previous time intervals.
10. The method of claim 9 wherein the motion information comprises
translational or rotational kinematics from accelerometer or
gyroscopic signals representative of movements of an
individual.
11. The method of claim 9 wherein the electronic notification
comprises a visual display, audible alarm, call, email, http, SMS
text-message, or other form of radiofrequency communication, that
the user has an increased likelihood of a near-term future
abnormality or fall occurrence.
12. The method of claim 9 wherein the reference value is determined
based on parameters associated with the individual including at
least one or age, mobility, and falling history.
13. The method of claim 9 wherein the predicted risk for falling
calculation involves utilizing an objective function for evaluating
digitized kinematic waveforms from the one or a plurality of
previous time intervals to classify the likelihood of a cascade of
events leading to falling within a future time interval.
14. The method of claim 9 wherein the predicted risk for falling
calculation involves utilizing an objective function comprising a
timeseries calculated from serially-acquired waveform data
embodying a Lyapunov exponent.
15. The method of claim 14, wherein the results of the objective
function are used by a decision-support algorithm to determine a
quantitative risk for falling.
16. Computer-readable media having computer-executable instructions
embodied thereon that when executed, facilitate a method for
determining a motion dispersion stability index for an individual,
the method comprising: identifying translational or rotational
kinematics from accelerometer or gyroscopic signals representative
of movements of an individual; and determining a level of motion
dispersion instability based on the kinematics from one or a
plurality of previous time intervals, for determining a likelihood
of falling within a future time interval.
17. The computer-readable media of claim 16, wherein the
accelerometer or gyroscopic signals are derived from a mobile
communication device carried by the individual.
18. The computer-readable media of claim 16, wherein determining
the level of motion dispersion instability involves calculating and
storing Lyapunov exponents.
19. The computer-readable media of claim 16, further comprising
determining that the level of motion dispersion instability exceeds
a reference value, and based on the determination, providing an
electronic notification that the individual has an increased risk
for falling.
20. The computer-readable media of claim 19 wherein the reference
value is determined based on parameters associated with the
individual including at least one or age, mobility, and falling
history, and wherein the electronic notification comprises a call,
email, http, or SMS text-message, visual display, or audible alarm.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of the U.S.
nonprovisional patent application having Ser. No. 12/982,631, filed
on Dec. 30, 2010, which claims the benefit of priority of U.S.
Provisional Application No. 61/291,657, filed Dec. 31, 2009, which
is expressly incorporated by reference herein in its entirety.
BACKGROUND
[0002] Among people over the age of 65 years, fall-related injuries
are the leading cause of death from injury. Forty percent of
hospital admissions among people over the age of 65 years are
reported to be the result of fall-related injuries, resulting in an
average length of stay of 11.6 days. Each year, an estimated one
third of older adults fall, and the likelihood of falling increases
substantially with advancing age. The average medical cost of a
fall is more than $20,000, and the total cost of falls is expected
to reach $32.4 billion in 2020.
[0003] In 2005, a total of 15,802 persons over 65 years died as a
result of injuries from falls. However, the number of older adults
who fall and who sustain only minor or moderate injuries and seek
treatment in clinics or physician offices is unknown. To estimate
the percentage of older adults who fell during the preceding 3
months, the CDC has analyzed data from the 2006 Behavioral Risk
Factor Surveillance System (BRFSS) survey. The results of that
analysis indicated that approximately 5.8 million persons aged 65
years or older, or 15.9% of all U.S. adults in that age group, fell
at least once during the preceding 3 months, and 1.8 million
(31.3%) of those who fell sustained an injury that resulted in a
doctor visit or restricted activity for at least 1 day. The
percentages of women and men who fell during the preceding 3 months
were similar (16.4% versus 15.2%, respectively), but women reported
significantly more fall-related injuries than men (35.7% versus
24.6%, respectively).
[0004] Procedural prevention programs attempting to reduce the
incidence of falls have to-date had mixed effectiveness, in part
because the preventive measures address only a subset of the
antecedent factors that lead to falls and in part because they
place almost all of the burden of falls-prevention upon personnel
other than the person who is at risk of falling and making the
faller a passive non-participant. In that connection, a motivation
for some embodiments of the invention is that, were non-demented
people whose near-term risks of falling are elevated or increasing
notified of that risk, many of those fallers would respond to such
notifications by proactively self-initiating preventive measures,
including temporarily refraining from transfers or other risky
movements and contacting caregivers for help. Psychologically, this
is far preferable to patient passivity and reactive responses by
caregivers, insofar as persons at risk of falling not only fear
falls; they also fear loss of independence and freedom. They do not
like being disenfranchised in decisions about their own care, and
they do not adhere to prevention programs that "medicalize" their
situation and displace control to other authorities, including
caregivers.
[0005] Falling is associated with common chronic diseases, such as
Alzheimer's or other forms of dementia; peripheral neuropathies
associated with diabetes or other conditions; Parkinson's disease;
tremor; extrapyramidal dyskinesias that may be associated with
psychiatric medications; cerebrovascular accident or transient
ischemic attacks; cardiac problems including cardiac arrhythmias;
diminished visual acuity; muscle weakness; lower-extremity joint
replacements; and other conditions. However, while the absence of
such conditions does reduce fall risk to a degree, it does not
exclude the possibility of falling. It is for this reason that so
much effort has been expended over the past 30 years on developing
predictive models, such as the Berg Balance Scale, the Timed
[Get]-Up-and-Go Test, and other metrics.
[0006] Mechanisms and types of falling have been the subject of
several studies. Slips account for a high percentage of falls and
subsequent injuries in community-dwelling older adults but not in
young adults. This phenomenon suggests that although active and
healthy older adults preserve a mobility level comparable to that
of young adults, these older adults may have difficulty generating
efficient reactive postural responses when they slip. This study
tested the hypothesis that active and healthy older adults use a
less effective reactive balance strategy than young adults when
experiencing an unexpected forward slip occurring at heel strike
during walking. This less effective balance strategy would be
manifested by slower and smaller postural responses, altered
temporal and spatial organization of the postural responses, and
greater upper trunk instability after the slip. Kinematic data were
collected from the right (perturbed) side of the body. Although the
predominant postural muscles and the activation sequence of these
muscles were similar between the two age groups, the postural
responses of older adults were of longer onset latencies, smaller
magnitudes, and longer burst durations compared to young adults.
Older adults also showed a longer coactivation duration for the
ankle, knee, and trunk agonist/antagonist pairs on the perturbed
side and for the knee agonist/antagonist pair on the nonperturbed
side. Behaviorally, older adults became less stable after the
slips. This was manifested by a higher incidence of being tripped
(21 trials in older vs. 5 trials in young adults) and a greater
trunk hyperextension with respect to young adults. Large arm
elevation was frequently used by older adults to assist in
maintaining trunk stability. In an attempt to quickly reestablish
the base of support after the slips, older adults had an earlier
contralateral foot strike and shortened stride length. Thus the
combination of slower onset and smaller magnitude of postural
responses to slips in older adults may result in an inefficient
balance strategy. Older adults needed secondary compensatory
adjustments, including alengthened response duration and the use of
the arms, to fully regain balance and prevent a fall. The shorter
stride length and earlier contralateral foot strike following the
slip indicate use of a more conservative balance strategy in older
adults.
[0007] Typical stability assessments characterize performance in
standing balance despite the fact that most falls occur during
dynamic activities such as walking. The objective of one study was
to identify dynamic stability differences between fall-prone
elderly individuals, healthy age-matched adults, and young adults.
Three-dimensional video-motion analysis kinematic data were
recorded for 35 contiguous steps while subjects walked on a
treadmill at three speeds. From this data, we estimated the vector
from the center-of-mass to the center of pressure at each
foot-strike. Dynamic stability of walking was computed by methods
of Poincare analyses of these vectors. Results revealed that the
fall-prone group demonstrated poorer dynamic stability than the
healthy elderly and young adult groups. Stability was not
influenced by walking velocity, indicating that group differences
in walking speed could not fully explain the differences in
stability. This pilot study supports the need for future
investigations using larger population samples to study fall-prone
individuals using nonlinear dynamic analyses of movement
kinematics.
[0008] Maintaining balance and postural stability while performing
functional activities is critical to an individual's independence
and quality of life. When individuals are unable to maintain their
total-body center of mass (COM) within the base of support, a loss
of balance may result, leading to a fall. Effective interaction
between the environment and the neuromuscular and musculoskeletal
systems allows an individual to generate the ground reaction forces
relative to the COM necessary for maintaining and recovering
balance during expected and unexpected situations. The swing and
support legs have a role in regulating angular impulse during fall
recovery and the balance recovery strategies used by younger adults
and older adult nonfallers and fallers is different. The multijoint
dynamics and neuromuscular control used during fall recovery at the
total-body, joint, and muscle levels are relevant aspects that are
considered. Understanding the fall recovery mechanisms successfully
used by younger and older adults allows us to begin to identify
effective intervention strategies that target specific
populations.
[0009] It is because of these factors that an improved
predictive-preventive method and system would be valuable, and in
embodiments of such methods and systems, prediction classification
or decision-support alert signals emitted by the system are
provided at logistically convenient times far enough in advance of
a fall's occurrence to allow for effective preventive intervention
in a majority of cases. Moreover, embodiments of such a method and
system should be inexpensive and suitable for a much larger
population who are at moderate risk of falls. Such a system would
find use as a tool not only for surveillance and triaging the
general medical-surgical patients in hospitals and other acute-care
venues but also for ambulatory, free-living individuals such as
athletes and the general elderly population who have one or more
risk-factors for falls.
[0010] Effective fall preventive interventions vary, and optimal
selection and personalized tailoring of them will depend upon the
patient's context, gender, age, medications, neurological
conditions such as Parkinsonism, history of previous falls, and
other factors. In the case of a previously asymptomatic ambulatory
person, effective preventive interventions may include consultation
with the personal physician or nurse or physiotherapist, or
presentation at a nearby outpatient department for diagnostic
assessment and monitoring. In the case of a person with existing,
known neurological conditions, effective preventive interventions
may include admission to hospital for observation and neurological
exams, provision of visiting nurse services, placement in an
assisted-living or other long-term care facility, consideration for
adjustment of medication regimen, or other alternatives.
[0011] Conventional pressure- and proximity- and
accelerometry-based monitoring apparatus has been shown to have
inadequate statistical sensitivity and specificity for the purpose
of predicting falls.
[0012] When measurements rely upon motion patterns as the trigger
or sentinel event for predicting incipient falling, the predictions
are generally only relevant when the person is ambulating.
Additionally, the advance notice provided by disturbed respiratory
pattern signals is so short (milliseconds to seconds) as to
preclude effective interventions to prevent the predicted falling
occurrences. For example, the Bed-Ex.TM. Patient Occupancy
Monitoring System and Motion Knowledge System's FallSaver.TM. and
other `proximity mat` and `pressure mat` monitors for bed or chair
surfaces have been used to detect and remotely signal unattended
patient ambulation or [attempted] transfer-in-progress, and thereby
predict patient falls. However, these often do not give a warning
or alarm far enough in advance to enable nurses or other caregivers
to reach the patient in time to assist them and prevent the
fall.
[0013] Many prior art methods involve cumbersome, complex,
expensive and/or invasive instrumentation, or require a skilled
operator in attendance.
[0014] The most accurate predictive methods, such as multi-axis
accelerometry, are expensive, are not widely available, are only
performable by subspecialty-trained providers, and are only
applicable to a small subset of patients who are already known to
be at risk of falling based on other attributes.
[0015] The methods involve expensive measurements, such as genomic
or proteomic laboratory tests that are not widely available and
that have a performance turnaround time of many hours or days
before the results and prediction are available for use, such that
the prediction or classification is not timely with respect to
interventions aimed at preventing the predicted occurrences.
[0016] The methods are sensitive to, and may be compromised or
entirely confounded by, individual variations in patient anatomy
and activities, such as transfers from chairs or wheelchairs or
beds, transfers with slide-boards or grab-bars other prosthetics,
patient movement and positioning, diurnal variations, etc.
[0017] The methods are sensitive to, and may be compromised or
entirely confounded by, individual variations in operator
positioning of proximity or pressure or accelerometer sensors on
the patient's body or variations in the timing and method of
acquiring the specimens or data that will enter into the prediction
and classification.
[0018] A major deficiency of prior art is false-negative error rate
and the absence of immunity to differences in daily activities and
behavior mix. A further deficiency is activity-specificity, for
example, the ability to detect or predict forward-falling while
walking but not backward-falling and not falling while climbing
stairs or running Stride length decreases with advancing age, and a
further deficiency of prior art is a restricted range of
applicability in terms of gait and stride length.
[0019] Still a further deficiency is that existing systems do not
take into account diurnal variations in persons' capabilities. For
example, Parkinsonian patients tend to have greater stability
deficits early and late in the day, and lesser deficits in the
middle of the day. Whereas, some embodiments of the invention are
sensitive to time-varying patterns in fall-risk.
[0020] Still a further deficiency is that some existing systems
make or rely on assumptions about the cognitive status of the
subject, this despite the fact that dementia and other cognitive
and psychological factors clearly affect the precautions or lack
thereof that are taken by fallers.
[0021] Still a further deficiency is that existing systems are
unable to account for orthostatic hypotension, visual acuity,
medication use, basic or instrumental activities of daily living,
and other factors.
[0022] Still a further deficiency is that existing systems do not
account for rotational acceleration, this despite the fact that
various recovery movements that interrupt falls involve rotation of
the torso and despite the fact that some types of falling involve
rotations. Accelerometers are primarily able to measure 3-axis
3-degree-of-freedom acceleration in 3-D Cartesian coordinates. And,
while it is theoretically possible to impute rotations (pitch,
roll, yaw) from 2 or more 3-axis accelerometers, the angular
precision and accuracy of doing so is presently inferior to the
precision and accuracy of measuring rotations with a digital
gyroscope.
[0023] Still a further deficiency of existing systems is that
calibration and periodic recalibration of accelerometer output in
V/m/sec2 in all three dimensions (which may be expensive and
time-consuming) are required for accuracy. In contrast, some
embodiments of methods and systems of the invention produce
accurate predictions that are insensitive to accelerometer offset
and drift; that require only infrequent checks to be sure that all
three axes of acceleration detection are still functional; that
permit the outputs in the measured 3 axes to diverge considerably
from each other in gain or scale so long as each one is itself
maintains approximately linear response; and that use `relative`
instead of `absolute` acceleration readings, and thereby offer a
distinct advantage in terms of ease-of-use and long-term
cost-of-ownership.
[0024] Still further, no mathematical or biomechanical models have
to-date appeared that are able to predict falling from a wheelchair
or other prosthetic devices that are prevalent in rehabilitation or
long-term care venues.
[0025] Moreover, an important consideration for widespread
acceptability of a system and method for fall prediction and
prevention is that the apparatus not unduly stigmatize the subject.
The elderly staunchly protect their independence and resist most
measures taken to protect them that might have a second-effect or
indirect consequence of alerting their caregivers to diminished
capability, causing the caregivers to reactively restrict the
person's autonomy.
[0026] The necessity of moving an elderly person to a nursing home
often is revealed by evidence denoting the risks attendant to
allowing the person to remain at home. The fear of being placed in
a nursing home is sufficiently strong for many that they will
aggressively hide evidence or obfuscate occurrences of falling that
may lead caregivers or authorities to take the decision to place
them in a nursing home.
[0027] However, an apparatus that reinforces the autonomy of the
wearer--enabling the wearer to accurately recognize and predict
risks or trends in risks and self-initiate appropriate mitigations,
refraining from motions or types of activity while the elevated
risk is present, thereby preventing occurrences of falling--would
be welcomed. The adverse outcomes would be prevented, and the
wearer would remain independent and in control of their activities
for a longer period of time than typically would otherwise happen.
They would not be distressed in connection with autonomy-preserving
cover-ups and obfuscation.
[0028] None of the prior art has examined mathematical stability
properties of the measured variables, however; nor has the prior
art made use of continuous realtime measurements over long periods
of many hours. Despite the existence of pressure and proximity and
accelerometer monitor type recording equipment for approximately 20
years, the analysis of long-timeseries data is traditionally
restricted to abnormal patterns denoting falls' occurrence, and
calculation and study of antecedent timeseries patterns, and other
parameters are never performed. Only small selected portions of the
recorded data are subjected to detailed analysis, and the rest are
discarded unexamined or ignored.
SUMMARY
[0029] A system, methods and computer-readable media are provided
for the automatic classification of patients according to near-term
risk of unstable ambulation and transfers and resulting risk of
falling. Embodiments of the invention are directed to event
prediction, risk stratification, and optimization of the
assessment, communication, and decision-making to prevent falls in
humans.
[0030] For at least the reasons outlined above, embodiments of the
present invention aim to alert the wearer to significant changes in
stability at least some minutes in advance of markedly increased
likelihood of falling, so that the person has adequate time to
cease performing whatever detected pattern(s) of motions has (have)
led to the predicted increase in fall risk. In some embodiments,
when the case of upward or downward dose-titration of medications
that are associated with fall risk, the signal denoting changed
risk of falls may be sensed over a period of hours or days, not
minutes.
[0031] Compared to traditional techniques, some embodiments of
invention allow automatic processing of continuously acquired
digital gyroscopic and accelerometer measurement of angular
velocity in 3 axes (pitch, roll, yaw) and acceleration in 3 axes
(X,Y,Z) and quantitative prediction of fall risk based on stability
metrics derived from one or a plurality of motion parameters.
Moreover, in comparison with manual methods, automated
method-embodiments offer advantages in terms of absolute
repeatability of measurements, immunity from errors related to
observer fatigue, lapses of attention, and transcription, as well
as efficiency and cost considerations that permit either more
extensive and rigorous testing for the same cost as manual methods,
or more rapid testing at lower cost.
[0032] In embodiments, a method for automatically predicting
ventricular arrhythmias in an individual that are likely to result
in sudden kinematic death (falling) is provided. The method
includes the step of obtaining motion signals representative of
electrical activity of the heart of an individual. The method also
includes the steps of detecting the presence of instability of
motion dispersion or other measurements in the signals, and
determining, utilizing an objective function, a motion dispersion
stability index (MdSI) from the signals, and determining the
difference between the index and a reference value to detect the
presence of instability of motion interval dispersion or other
measurements in said signals, wherein a significant difference is
indicative of an increased risk of said individual of falling. In
one embodiment, the objective function comprises a timeseries
calculated from serially-acquired waveform data embodying a
Lyapunov exponent of one or a plurality of motion or other
physiologic variables as functions of time. In one embodiment, the
method further includes providing a notification when an increased
risk for falling is determined. In some embodiments, this
notification may be communicated to a health care provider and/or
may be communicated to the individual by means of an audible alarm,
text message, or phone call.
BRIEF DESCRIPTION OF THE DRAWINGS
[0033] The present invention is described in detail below with
reference to the attached drawing figures, wherein:
[0034] FIG. 1A depicts aspects of an illustrative operating
environment suitable for practicing an embodiment of the
invention.
[0035] FIG. 1B depicts aspects of an illustrative operating
environment suitable for practicing an embodiment of the
invention.
[0036] FIG. 2 depicts aspects of an illustrative operating
environment suitable for practicing an embodiment of the
invention.
[0037] FIG. 3 depicts a flow diagram of an exemplary method for
automatically predicting ventricular arrhythmias in an individual
that are likely to result in sudden kinematic death, in accordance
with embodiments of the invention;
[0038] FIG. 4 depicts a flow diagram of an exemplary method for
determining a motion dispersion stability index for an individual,
in accordance with embodiments of the invention;
[0039] FIG. 5 depicts a flow diagram of an exemplary method for
determining a motion dispersion stability index for an individual,
in accordance with embodiments of the invention;
DETAILED DESCRIPTION
[0040] The subject matter of the present invention is described
with specificity herein to meet statutory requirements. However,
the description itself is not intended to limit the scope of this
patent. Rather, the inventors have contemplated that the claimed
subject matter might also be embodied in other ways, to include
different steps or combinations of steps similar to the ones
described in this document, in conjunction with other present or
future technologies. Moreover, although the terms "step" and/or
"block" may be used herein to connote different elements of methods
employed, the terms should not be interpreted as implying any
particular order among or between various steps herein disclosed
unless and except when the order of individual steps is explicitly
described.
[0041] As one skilled in the art will appreciate, embodiments of
our invention may be embodied as, among other things: a method,
system, or set of instructions embodied on one or more computer
readable media. Accordingly, the embodiments may take the form of a
hardware embodiment, a software embodiment, or an embodiment
combining software and hardware. In one embodiment, the invention
takes the form of a computer-program product that includes
computer-usable instructions embodied on one or more computer
readable media.
[0042] Computer-readable media include both volatile and
nonvolatile media, removable and nonremovable media, and
contemplates media readable by a database, a switch, and various
other network devices. By way of example, and not limitation,
computer-readable media comprise media implemented in any method or
technology for storing information. Examples of stored information
include computer-useable instructions, data structures, program
modules, and other data representations. Media examples include,
but are not limited to information-delivery media, RAM, ROM,
EEPROM, flash memory or other memory technology, CD-ROM, digital
versatile discs (DVD), holographic media or other optical disc
storage, magnetic cassettes, magnetic tape, magnetic disk storage,
and other magnetic storage devices. These technologies can store
data momentarily, temporarily, or permanently.
[0043] Embodiments of the present invention provide a computerized
system, methods, and computer-readable media for automatically
identifying persons who are at risk for falling through the use of
a system, which in one embodiment, includes noninvasive, portable,
wearable electronic device and sensors equipped with
signal-processing software and statistical predictive algorithms
that calculate stability-theoretic measures, such as a
translational and rotational motion dispersion stability index
(MdSI) for the individual, derived from a digital kinematic-signal
timeseries acquired by the device. The measurements and predictive
algorithms embedded within the device provide for unsupervised use
in the home or in general acute-care and chronic-care venues and
afford a degree of robustness against variations in individual
anatomy and sensor placement. In embodiments, the present invention
provides a leading indicator of near-term future abnormalities,
proactively alerting the user (for example, 2 hours or more in
advance, in one embodiment) and providing the wearer and/or care
providers with sufficient advance notice to enable effective
preventive maneuvers to be undertaken. In one exemplary embodiment,
the device is equipped with radiofrequency telecommunication
capabilities that enable integration with case-management software,
electronic health record decision-support systems, and consumer
personal health record systems.
[0044] By way of example and not limitation, a user using an
embodiment of the invention may be able to go about his or her
daily routine but be provided an advanced warning of any
abnormalities such as a detonation or improvement of the user's
condition or an increased likelihood of an event such as falling,
Sudden Cardiac Death (SCD) COPD, asthema, TIA, stroke, or other
conditions, for example. In one embodiment, the user may don one or
more sensors capable of acquiring gyroscopic or accelerometer
measurement, or both, of angular velocity and acceleration, which
could be a chest-strap sensor, a badge sensor attached to or
integrated into the user's clothing, a watch-sensor or other sensor
in approximate contact with the user and that is wirelessly
communicatively-coupled to a smart phone located on or near the
user's body. In this exemplary embodiment, the smart-phone may
include an app which when executed receives user data from the
sensors, calculates the stability-theoretic measures, and
communicates the results with the user, the user's health care
provider, case-management software, decision-support systems, or
personal health record systems. For example, the phone may notify
the user in advance, via an alarm or vibration, and may also notify
a family member, the user's health care provider, electronic-health
record decision-support systems or personal health record systems,
via a call, email, http, sms text-message, or other form of
radiofrequency communication, that the user has an increased
likelihood of a near-term future abnormality or fall occurrence.
This enables the user or care providers to take preventative
measures.
[0045] An exemplary operating environment for the present invention
is described in connection to FIGS. 1A, 1B and 2, and relates
generally to the description of a mobile wearable system for
stability-theoretic prediction and prevention of events such as
sudden kinematic injury (falling), for use in some embodiments of
the invention, and described below in connection to FIGS. 1A, 1B
and 2. Referring to the drawings in general, and initially to FIG.
1A in particular, an exemplary operating environment 100 is
provided suitable for practicing an embodiment of our invention. We
show certain items in block-diagram form more for being able to
reference something consistent with the nature of a patent than to
imply that a certain component is or is not part of a certain
device. Similarly, although some items are depicted in the singular
form, plural items are contemplated as well (e.g., what is shown as
one data store might really be multiple data-stores distributed
across multiple locations). But showing every variation of each
item might obscure the invention. Thus for readability, we show and
reference items in the singular (while fully contemplating, where
applicable, the plural).
[0046] As shown in FIG. 1A, environment 100 includes one or more
sensors 116. In one embodiment, sensors 116 include one or more
transducers or types of sensors operable for providing electrical
signals corresponding to measurements of various conditions,
states, of movements of a user. Embodiments of sensor 116 may
further include a power supply, processor, memory operable for
acquiring and storing user-information and programming
instructions, and communication component for communicating the
resulting measurements of user-information with brick 130. In some
embodiments, the transducer may be a standard electrode, such as a
single-terminal electrode, or a specialized multi-segment or
noise-reduction electrode.
[0047] In some embodiments one or more specialized noise-reduction
electrodes may be integrated on a wearable fabric elastomeric band
positioned on the user, such as around the user's chest, thereby
eliminating or reducing noise, interference, distortion, or
artifacts and also improving ease-of-use and patient compliance. In
some embodiments sensor 116 includes one or more accelerometeric or
gyroscopic transducers operable to determine gyroscopic and
accelerometer measurement of angular velocity in at least one of 3
axes (pitch, roll, yaw) and acceleration in at least one of 3 axes
(X,Y,Z) and to provide motion signals corresponding to this angular
velocity or acceleration. For example, in some embodiments, sensor
116 includes one or more transducers, which can take the form of
standard MEMS accelerometer integrated circuit chips, for obtaining
electrical kinematic signals from the individual. In one
embodiment, a plurality of accelerometer sensors and at least one
gyroscope sensor, such as the one manufactured by InvenSense Inc
that is used in the Nintendo Wii.TM. Motion Plus.RTM. device, may
be deployed on a wearable fabric elastomeric band positioned around
the chest. Such an embodiment may be used to eliminate or reduce
noise, interference, distortion, or artifacts and improve
ease-of-use and patient compliance.
[0048] In some embodiments, the processor of sensor 116 is operable
to control the frequency of measurements; for example, to read a
transducer's output at certain intervals such as 50 times each
second; to pre-process or condition the signal, including applying
a threshold, noise-filter, or normalizing the raw user-derived
signal; read from or store the user-information in memory, and
communicate the acquired timeseries of user-information with brick
130 via a communication component of sensor 116. In one embodiment,
a floor-threshold is applied such that only movements of a certain
magnitude are acquired and communicated to brick 130. For example,
it may be desirable in some embodiments not to capture every
minuscule motion of the user, but rather only major movements such
as stumbles, twists, or sudden jerking motions.
[0049] Embodiments of sensor 116 may be designed to measure one or
more conditions, states, or movements of a user. For example, in
one embodiment sensor 116 obtains electrical signals corresponding
to motion of a user and may be worn as a chest-strap, necklace, or
a badge on the user's clothing, for example. In another embodiment,
sensor 116 obtains electrical cardiac signals of a user and may be
worn as a chest-strap, for example. Such a sensor may be designed
to measure electrical signals associated with the nerves of the
heart or the heart muscle or both. In another embodiment, sensor
116 may include an optical transducer for measuring chemicals in
the skin such as keytones, which may be used for determining
ketoacidosis of the user. Such an embodiment of sensor 116 may be
configured as a skin patch, arm- or leg-band, on the back of a
watch, or ankle band, for example. Another embodiment of sensor 116
includes one or more optical sensors for detecting an optical
signal across the skin to look at carbox-symmetry, CO2 levels, O2
levels, or a combination of these levels.
[0050] In some embodiments, these levels are measured at 10 to 50
times a second thereby resulting in a timeseries of
user-information that maybe communicated to brick 130. Other
embodiments of sensors 116 include sensors for measuring blood
pressure, heart rate, temperature, chemicals such as chemicals in
the blood, breath, or on the user's skin, skin or tissue
properties, oxygen levels, user motion, movement, or position, or
other variables associated with the user's condition, state, or
activity. Such sensors are configured to be positioned on or near
the user's body in an appropriate manner so that they may function
to sense user-data. For example, heart-related sensors may be
positioned on or near the chest or at other appropriate locations
on the user's body.
[0051] In some embodiments, sensor 116 may be worn in contact with
user, worn on user's clothes, or located in a user's seat, bed,
toilet, or elsewhere in the user's environment, depending on
specific type of user-information that the sensor is intended to
measure. In one embodiment, sensors 116 include one or more
accelerometers, gyroscopic meters, or combination of such devices
as to enable one or more sensors 116 to detect user motion, user
position or orientation, and sudden changes in user position. In
these embodiments the timeseries of user-information communicated
to brick 130 may comprise individual motion-events, with each new
motion-event adding a member to the timeseries. Thus unlike the
timeseries generated by a sensor measuring a physiologic variable
50 times each second, which would have 50 samples each second, the
timeseries motion-information is acquired as motions occur, which
may not occur at regular intervals. In other words, there could be
irregular periods of time between motions that are captured by
sensor 116.
[0052] In one embodiment, such a sensor 116 may be optimally
positioned on the user to measure motion and orientation, such as
inline with the user's spine. In one embodiment, the accelerometer
and gyroscopic chip-sets built into many smart phones may be used
as sensor 116. In such an embodiment, the smart phone, running a
program for determining stability-theoretic measures, may monitor
user motion-stability and provide to the user and health-care
provider early earning warning of a likelihood of increased risk
for falling.
[0053] In some embodiments, multiple sensors 116 may be employed on
or about the user. For example, it may be desirable to have more
than one sensor for measuring certain user information such as
ketones in the skin, for example, as circulation on certain users
varies in the user's body. Additionally, one or more sensors may
become compromised, and having multiple sensors provides for
robustness. For example a watch sensor may get wet when the user
washes his hands and fail to operate as normal, while a second
sensor located on the user's ankle may remain effective. In
embodiments detection motion of the user, multiple sensors at
different locations on the user's body may be employed to obtain
more accurate or thorough kinetic information. In such embodiments,
motion-signals corresponding to motion in a particular direction or
axis or angular motion may be averaged, or may be weighted or
scaled according to the location of the sensor. For example, motion
signals obtained from a sensor located on the users wrist may be
weighted less than motion signals obtained from a sensor worn on
the user's chest. In such embodiments, the weighted signals can be
combined and used for MdSI determination.
[0054] It is also contemplated that multiple sensors of different
sensor-types may be utilized to provide a combination of
user-information that may more accurately identify a condition or
state of the user or increased likelihood of a particular event
occurring. For example, a user suffering from the early conditions
of a stroke may exhibit multiple signs detectible by different
types of sensors 116, such as motion sensors 116, blood-pressure
sensors 116, and skin-chemical sensors 116.
[0055] Continuing with FIG. 1A, environment 100 includes
processing/communication brick 130. Exemplary embodiments of brick
130 are discussed in greater detail in connection to FIG. 1B, but
some embodiments of brick 130 include one or more processors
operable for processing user-sensor information and determining
stability-theoretic measures, a communication module for receiving
information from the user-sensors and for communicating results to
the user or health-care provider, and a memory for storing received
user-information, determined results, and programming instructions.
Brick 130 may worn on the user's body, such as clipped to a belt,
in a holster, or around the user's neck, or can be carried by the
user, such as in the user's pocket or purse, or may be kept with a
close enough proximity to the user as to communicate with sensor(s)
116. In some embodiments, sensor(s) 116 are housed within or on
brick 130.
[0056] In some embodiments, brick 130 is a smart phone running one
or more application programs or "apps" for receiving user-sensor
information, determining stability-theoretic measures, and
communicating results to the user and health care provider. In a
smart-phone embodiment, brick 130 uses the phone's communication
equipment for communicating user information to a backend, such as
a health care provider or decision-support knowledge agent. Brick
130 may use other communication features of the smart phone such as
Bluetooth or Wi-Fi to communicate with one or more sensors 116 and
in some embodiments, a base station or user computer.
[0057] A smart phone may be communicatively-coupled with an
additional component for facilitating communication with one or
more sensors 116, for processing user-information, or for storing
and communicating user results. For example, in one embodiment,
brick 130 is communicatively-coupled to a holster or other
component containing a communication module for communicating with
one or more sensors 116. Such an embodiment is useful where sensors
116 use a communication protocol that is not compatible with brick
130. For example, where sensors communicate using Bluetooth, but
brick 130 is embodied on non-Bluetooth enabled smart phone, the
user may attach a Bluetooth module to the smart phone to enable it
to communicate with sensors 116. Similarly, where sensors 116
communicate using Zigbee or another low-rate wireless personal area
network platform, a user may couple a Zigbee-enabled communication
module to their smart phone. In another example embodiment, a smart
phone may be communicatively-coupled with a base station (not
shown) located in the user's house. In one embodiment, the base
station could be a personal computer connected to a wireless router
or a laptop equipped with RF communication capability such as Wi-Fi
or Bluetooth. In one embodiment, the base station communicates with
backend 190.
[0058] In another embodiment, brick 130 communicates directly with
backend 190. Backend 190 includes the health care provider computer
system and devices, case-management software, electronic health
record decision-support systems and devices, and consumer personal
health record systems and devices. In some embodiments, brick 130
stores information on data store 192, which may be local or
remotely located, and which may be accessible by backend 190, in
some embodiments. In some embodiments, data stores 192 comprises
networked storage or distributed storage including storage on
servers located in the cloud. Thus, it is contemplated that for
some embodiments, the information stored in data store 192 is not
stored in the same physical location. For example, in one
embodiment, one part of data store 110 includes one or more USB
thumb drives or similar portable data storage media. Additionally,
information stored in data store 192 can be searched, queried,
analyzed via backend 190, such as by a health care provider or by a
decision-support knowledge agent, for example.
[0059] In some embodiments, sensors 116 communicate with other
sensors 116 and with brick 130 over a wired or wireless
communication protocol. In one embodiment, sensors 116 communicate
using Bluetooth, Wi-Fi, or Zigbee protocols. In some embodiments a
low-powered communication protocol is desirable in order to
preserve the batter life of the sensor 116. In some embodiments
using a communication protocol having a narrow bandwidth, such as
Zigbee, sensors 116 may also include a memory buffer for storing
user-derived information until it is communicated to brick 130.
Sensors 116 may also communicate with other sensors 116 or directly
with a base station, in some embodiments.
[0060] Turning now to FIG. 1B, an exemplary operating environment
suitable for practicing an embodiment of the invention is shown and
referenced generally as 150. As shown in FIG. 1B, brick 130 is
communicatively coupled to wearable motion sensor 112, which is one
embodiment of sensor 116, and docking station 120. In the
embodiment shown in FIG. 1B, docking station 120 recharges a
battery in brick 130 and in chest-strap sensor 112. Brick 130 is
also communicatively coupled to backend 190, and data store 192,
which are described previously in connection to FIG. 1A.
[0061] The embodiment illustratively depicted in FIG. 1B, may be
used for generating a Lyapunov exponent classifier and verifying
and validating whether such a detector achieves statistical
sensitivity and specificity in the intended mortality range of
deployment, sufficient for satisfactory performance in the use for
classifying patients according to in-hospital mortality
outcome.
[0062] In the embodiment shown in FIG. 1B, motion sensor 112
includes one or more accelerometers or gyroscopic transducers. In
this embodiment, the transducers are coupled to an instrumentation
operational amplifier, an analog filter, an analog-to-digital
converter, and a Bluetooth or similar RF communication component,
thereby enabling motion sensor 112, when positioned on the user, to
obtain raw motion signals of the user, capture and digitize the raw
motion signals, and communicate this information to brick 130.
Motion sensor 112 also includes a power supply made up of a battery
and multiple-output supply converter.
[0063] In the embodiment shown in FIG. 1B, brick 130 includes a
Bluetooth or similar RF communication component operable to receive
user-information from motion sensor 112 or from other sensors 116,
preprocessing and filtering components operable to condition and
format the received user information for the movement variability
index (MVI) stability processing, and a processor for determining
MdSI, which is described in connection to FIGS. 3 and 4, below.
Embodiments of brick 130 may also include a Bluetooth, cell-phone,
or Wi-Fi communication component for communicating results
ultimately to backend 190 and data store 192, and an alarm and
display for providing results, diagnostic feedback, power levels,
and other information to a user or for receiving inputs from a user
such as parameters and device settings. Embodiments of brick 130
may also include memory for storing parameters, settings, firmware
and programming instructions, and determined results. Embodiments
of brick 130 may also include a power supply which in one
embodiment comprises a battery and a battery balance circuit. In
one embodiment, brick 130 is a computer system with one or more
processors, memory, and input/output functionality.
[0064] In one embodiment, brick 130 is a computer system comprising
the following hardware and firmware components: a 32-bit 48 MHz
AT91SAM7S256 (ARM7TDMI) main microprocessor with 256 KB flash
memory and 64 KB RAM, an 8-bit 4 MHz ATmega48 microcontroller with
4 KB flash memory and 512 Bytes RAM, a 26 MHz CSR BlueCore 4
Bluetooth controller with 1 MB flash memory and 47 KB RAM, and
100.times.64 pixel LCD matrix display. In one embodiment,
motion-signal pre-processing, recursive IIR low-pass Bessel filter,
and MdSI calculation software algorithms were implemented in a
dialect of the C language (NXC) using the BricxCC compiler and
version 1.28 firmware for the ARM7 processor. It should be
understood that variations in hardware and firmware are
contemplated by and within the scope of the invention, and are
provide here for illustrative purposes.
[0065] FIG. 2 illustratively depicts aspects of an illustrative
operating environment suitable for practicing embodiments of the
invention and is referenced generally as 200. Environment 200
depicts a user 210 wearing various example types of sensors 116,
including: chest-strap sensor 212, badge-sensor 214, which may be
attached to a user's clothing or integrated into a user's clothing,
necklace sensor 216, skin-patch sensor 218, watch-strap sensor 220,
and ankle or leg sensor 222. User 210 is also wearing a brick 230
at the user's waist. Also depicted in environment 200 is a chair
205 having sensors 116 integrated into a seat cushion, shown as
sensors 225, and a bed 207 having sensors 116 integrated into the
bed shown as sensors 227. In some embodiments, environment 200
includes a base station 240, which may be communicatively coupled
to brick 230 or one or more sensors 116. As further described in
connection to FIG. 1A, in some embodiments, a base station, such as
base station 240, is communicatively coupled to a user's computer,
to a backend 190, or to data store 192.
[0066] Turning now to FIG. 5, a flow diagram 500 is provided
illustrating an exemplary method according to one embodiment. At a
high level, flow diagram 500 illustratively depicts a method for
determining a motion dispersion stability index (MdSI) for an
individual. The MdSI is determined by applying an objective
function to user-derived information such as motion-signal
information obtained from one or more sensors 116. Some embodiments
of the invention process the information in serial acceleration and
rotational velocity measurements associated with the individual's
movements and to calculate MdSI(t) timeseries, where t represents
time, as a function of the individual's instantaneous MdSI
determinations. As shown in flow diagram 500, a logistic regression
equation and algorithm based on Lyapunov stability measures of
motion dispersion as a continuous or discrete function of time is
utilized. A Lyapunov exponent (of MdSI(t) or any other timeseries
signal) is a quantitative measure of separation of trajectories
that diverge widely from their initial positions and is related to
how chaotic a system is. The larger the exponent, the more chaotic
the system. For periodic signals, the Lyapunov exponent is zero. A
random but stable signal will also have an exponent very close to
zero.
[0067] In another embodiment, a decision tree algorithm may be used
to evaluate the classification ability of several methods of
measuring motion dispersion. In yet another embodiment, a support
vector machine (SVM) algorithm utilizing timeseries of calculated
motion variables including width of root-mean-square (RMS) motion
dispersion is applied to generate a prediction of falling risk.
Still in yet another embodiment, a combination of a Lyapunov-based
algorithm, a descision tree algorithm, or a support vector machine
may be employed.
[0068] At a step 510, motion signals of a user are obtained using
one or more sensors 116. User-information representative of the
motion signals is communicated from one or more sensors 116 to
brick 130. In one embodiment, sensor 116 captures motion waveforms
corresponding to the user's movement, thereby resulting in a
timeseries of motion-signal intervals. It will be understood by
those skilled in the art that in some embodiments, other waveform
measures or physiologic timeseries may be used without departing
from the scope of the invention. For example, in some embodiments
timeseries variables relating to heart, respiratory, glucometry,
accelerometry, oximetry, capnometry, plethysmography (perfusion),
or other physiologic variables may be used.
[0069] In steps 520 through 550, the motion dispersion stability
index (MdSI) as a function of the continuous or discrete kinematic
timeseries is calculated. In some embodiments, any ectopic beats
and the sinus beats immediately preceding and following the ectopic
beats are first eliminated, as part of a step 520 before
calculating the maximal value of root-mean-square differences.
Low-pass filtering may be performed to remove baseline drift from
the electrical signal, in some embodiments. Normalizing the maximal
value of root-mean-square differences to the absolute magnitude of
the signal-averaged motions may also be performed, in some
embodiments, before calculating and updating the MdSI(t)
timeseries. Instructions carried on a computer-readable storage
medium (e.g., for identifying QT intervals and calculating MdSI(t))
can be implemented in a high level procedural or object oriented
programming language to communicate with a computer system, in one
embodiment. Alternatively in another embodiment, such instructions
can be implemented in assembly or machine language. The language
further can be compiled or interpreted language, in one
embodiment.
[0070] It is further contemplated that in some embodiments, the
MdSI-related processing occurring in steps 520 through 540 occurs
in realtime or near realtime, simultaneously, as electrical
kinematic signal-information is collected in step 510, thereby
allowing a skilled operator to monitor an individual's MdSI during
pharmacologic or exercise physiologic stress, if desired. More
generally, in some embodiments, processing steps 520 through 550
are performed substantially simultaneously with the step 510 of
collecting the kinematic signals in near real-time, so as to enable
the ambulatory consumer to go about their daily activities and
receive smartphone or other mobile alert messages from brick 130
device in case any elevated-risk conditions are detected.
[0071] At step 520, the collected kinematic signals are prepared.
In some embodiments this preparation includes pre-processing or
signal conditioning. Step 520 may be performed by sensor 116, by
brick 130, or a combination. In embodiments, thresholding, artifact
censoring, normalizing, noise filtering, or other DSP filtering, or
any combination of these, may be applied to the raw signal
information. In one embodiment a floor threshold is applied by
zeroing out the motion signals unless the amplitude, in the X,Y,Z
axis, angular (gyro) or a sum of these, exceeds a certain minimum
value. In some embodiment, the amplitudes of one or more acquired
signals, representing motions about the X,Y, and Z axis, and
angular (gyro) motions, are summed together resulting in a motion
signal where amplitude corresponds to motion in any of the one or
more X, Y, and Z axis, or angular motions used in the summation.
Furthermore, in some embodiments, the motion-signal that
corresponds to a motion along a particular axis or angular motion
may be weighted or scaled prior to the summation. For example, it
may be desirable to emphasize z-axis or angular motions (or both)
by assigning a higher weight to the amplitudes of signals
corresponding to z-axis motions or angular motions. Thus, in such
embodiments, a user's movement along the x and y axes (usually
horizontal) would be less significant for the MdSI determination
than movement along the z-axis (usually vertical) or angular
movement (such as twisting and turning).
[0072] In some instances, inconsistencies in accelerometry
measurements may occur in part because of skeletal muscle signal
artifact, patient position, time of day, or misplaced sensors.
Moreover, failure to adjust for sensor drift can also skew an
analysis. Accordingly, in some embodiments integrals of
acceleration and angular velocity are taken into account and
stability measurements that are generated shortly after any fall or
impact are ignored.
[0073] At steps 530, 540, and 550, MdSI timeseries is determined,
Lyapunov exponents are calculated, and used to determine stability
of the monitored condition of the user. By way of example and not
limitation, the methodology of the invention may be understood
through the following steps: Let L(x.sub.1, x.sub.2, . . . ,
x.sub.n) be a scalar function of n components of x, where the n
components (sampled timeseries values of the linear combination of
one or more accelerometer-axis outputs, such as a 3-axis
accelerometer output, and 1-axis gyro output) comprise the vector
x={x.sub.1, . . . , x.sub.n}. L(x) is positive-definite in a
neighborhood N of the origin if L(x)>0 for all x.noteq.0 in N
and L(0)=0. Let x*(t)=0, t.gtoreq.t.sub.0 be the zero solution of
the homogeneous system x =Ax where x(0)=x.sub.0=0. Then x*(t) is
globally stable for t>t.sub.0 if there exists L(x) with the
following properties in some neighborhood N of 0: (i) L(x) and its
partial derivatives are continuous; (ii) L(x) is positive-definite,
or L(x)>0; and (iii) dL(x)/dt is negative-definite, or
dL(x)/dt<0.
[0074] By (ii) the quadratic form L(x) exhibits an ellipsoid curve.
By (iii), the ellipsoid curve shrinks to zero. Choose
.epsilon.>0 such that N.epsilon..OR right.N above. Any half-path
starting in N.epsilon. remains in it because L(x) is a quadratic
form (by (ii)) which exhibits an ellipsoid curve that is continuous
as well as its partial derivatives (by (i)). The same holds for
every sufficiently small .epsilon.>0 and hence for every
sufficiently small neighborhood of the origin. The zero solution is
therefore globally stable.
[0075] In other words, the system (dx/dt)=Ax is globally stable if
and only if for some positive-definite matrix W, the equation:
A.sup.t H+HA=-W has a positive-definite matrix H. If for some
positive-definite matrix W, the equation A.sup.t H+HA=-W has a
positive-definite matrix H, let us show that (dx/dt)=Ax is globally
stable. Since H is positive-definite, then L(x)=x.sup.t Hx is
positive-definite (where x.sup.t is now the transpose of x and not
the time derivative), i.e. L(x)>0. Also, L(x) positive-definite
implies that V(x) and its partial derivatives are continuous.
Differentiating L(x), then: dL(x)/dt=(dx.sup.t/dt)Hx+x.sup.t
H(dx/dt) or, as dx/dt=Ax: dL(x)/dt=(Ax).sup.t Hx+x.sup.t
HAx=x.sup.t A.sup.t Hx+x.sup.t HAx=x.sup.t (A.sup.t H+HA) x. Thus,
as A.sup.t H+HA=-W: dL(x)/dt=x.sup.t (-W)x. W determined to be
positive-definite implies that -W is negative-definite, thus:
dL(x)/dt=x.sup.t (-W)x<0.
[0076] Finally, it is notable that (i) L(x) and its partial
derivatives are continuous; (ii) V(x) is positive-definite; (iii)
dL(x)/dt is negative-definite. As a result, dx/dt is globally
stable according to our previous theorem. Conversely, if dx/dt=Ax
is stable, then for some positive-definite matrix W, the equation
A.sup.t H+HA=-W has a positive-definite matrix H. dx/dt=Ax stable
implies all the eigenvalues of A are negative, i.e. .lamda.<0
for any eigenvalue .lamda. of A. Now, as .lamda.x=Ax, then
(Ax).sup.t=(.lamda.x).sup.t, which implies x.sup.t
A.sup.t=.lamda.x.sup.t. Thus, premultiplying A.sup.t H+AH by
x.sup.t and post-multiplying it by x, the following is obtained:
x.sup.t (A.sup.t H+HA)x=x.sup.t (-W)x; or: x.sup.t A.sup.t
Hx+x.sup.t HAx=x.sup.t (-W)x; or substituting in .lamda.x.sup.t and
.lamda.x: A x.sup.t Hx+x.sup.t HA x=x.sup.t (-W)x; or simply: 22
x.sup.t Hx=x.sup.t (-W)x. As -W is negative-definite, then x.sup.t
(-W)x<0, thus 2.lamda.x.sup.t Hx<0. As .lamda.<0 by the
assumption of stability, then it must be that x.sup.t Hx>0, or H
is a positive-definite matrix. Accordingly, a real n.times.n matrix
A is a stable matrix if and only if there exists a symmetric
positive-definite matrix H such that A.sup.t H+HA is
negative-definite. In one embodiment, a choice of W=I may be made
and H can be solved and solve for H in the equation A.sup.t
H+HA=-I. The solution has the form
H=.alpha.(A.sup.t).sup.-1A.sup.-1+.beta.1 where .alpha. and .beta.
are constants. Thus, choosing a Lyapunov function, L(x)=x.sup.t Hx,
this solution is used to determine H. The Lyapunov function or
thread may be executed continuously, under a real-time operating
system (RTOS), in some embodiments, enabling parameters and
timeseries information to be passed to the Lyapunov function or
thread in near realtime.
[0077] Furthermore, in some embodiments, a second-order polynomial
function f(x)=r*x*(1-x) is utilized to represent a system whose
stability may be characterized by the invention. In one embodiment,
the system may be characterized by a function of different order or
form. If the structure of a particular system is not known, the
structure may be developed by Taylor series regression, spectral
analysis or timeseries analysis techniques or other methods of
modeling known to those of skill in the art.
[0078] At a step 530 a dispersion time series is calculated. In one
embodiment, a standard deviation (SD) of the amplitude is
calculated on an M-wide time series array, such as, for example,
SD{A.sub.1, . . . , A.sub.N-M}, SD{A.sub.2, . . . , A.sub.N-M+1},
SD{A.sub.M+1, . . . , A.sub.N}, where A represents amplitude, and
each member of the timeseries corresponds to a motion event, such
as movement in either the X,Y or Z-axis direction, rotational
movement, or a combination of these. For example, in one
embodiment, each member of the time series represents a linear
combination of 3-axis accelerometer outputs plus 1-axis gyro
output. In one embodiment M may vary between 1000 to 300 samples;
with accuracy generally increasing as the size of M increases.
[0079] At a step 540, Lyapunov exponents are calculated for each
member of the time series, thus: SD{A.sub.1, . . . ,
A.sub.N-M}.fwdarw..lamda..sub.1, SD{A.sub.2, . . . ,
A.sub.N-M+1}.fwdarw..lamda..sub.2, . . . , SD{A.sub.M+1, . . . ,
A.sub.N}.fwdarw..lamda..sub.M+1. Accordingly, in some embodiments
each member of the time series of standard deviations of the linear
combination of 3-axis accelerometer outputs plus 1-axis gyro output
represents an MVI value. At a step 550, stability is assessed based
on the determined values of the Lyapunov exponents. In some
embodiments .lamda..sub.i>0 implies an unstable process. In some
embodiments, a threshold TH may be applied. For example, for
instability to be present, .lamda..sub.i>TH, which can account
for minor fluctuations that may occur in the user, such as
fluctuations that may arise when a user's activity level changes.
In other embodiments, such as in the example discussed later on,
the difference between .lamda..sub.i and a reference value is
determined, and instability is present where this difference
exceeds a certain threshold.
[0080] At a step 560 it is determined whether the user's motion is
showing signs of instability, based on the results of step 550. In
one embodiment, if the stability is present, then the process
returns to step 510 and additional motion signals or other
physiologic timeseries information is obtained from one or more
sensors 116. In one embodiment, new kinematic-signal information or
other physiologic timeseries information continuously collected as
it is available simultaneously as processing for determining
stability-theoretic measures occurs. In one embodiment, the
Lyapunov exponents are calculated on a sliding boxcar array that is
M-samples wide, with new Lyapunov exponents calculated each W
samples. In embodiments where W equals 1 motion event, then new
Lyapunov exponents are calculated on the M-wide timeseries array
for each new motion event. In some embodiments, W may represent
several samples. If at step 560, the results of step 550 indicate
the presence of instability, then the method proceeds to step 570.
At a step 570, a user, health care provider, or decision support
system is notified that the user is becoming unstable. In one
embodiment, this instability indicates that the user is facing an
increased likelihood of falling. In one embodiment, this
instability indicates a change in the patient's condition, which
may be for the better or worse. In one embodiment, the user may be
notified via brick 130 in the form of a text message, audible alarm
or vibration. In one embodiment, the health care provider maybe
notified via brick 130 in the form of a text message, call, or
other appropriate form of communication. In one embodiment, a
visual or graphical display of the electrical signals or a
numerical or digitized representation of the monitored motion
variables and stability indices may be presented on brick 130, a
user's computer communicatively coupled to brick 130, or a health
care provider's computer communicatively coupled to backend 190.
For example, in one embodiment, an audible alert sounds or a
vibration is emitted upon detection of patterns and MdSI values
indicative of actionable increased risk of falling. In one
embodiment, a radiofrequency message may be emitted to
security-/confidentiality-controlled, mated transceivers such as
BlueTooth smartphones, Wi-Fi connections with personal computers or
electronic medical records systems, and similar devices.
[0081] Turning to FIG. 3 a flow diagram 300 is provided
illustrating an exemplary method according to one embodiment. At a
high level, flow diagram 300 illustratively depicts a method for
determining a motion dispersion stability index (MdSI) for an
individual. The MdSI is determined by applying an objective
function to user-derived information such as kinematic signal
information obtained from one or more sensors 116. The method also
includes determining the difference between the stability index
value and a reference value to detect presence of instability of
motion dispersion or other measurements. It has been determined, as
further described below in connection to that a significant
difference between the two values indicates an increased risk of
falling for an individual. In one embodiment, the reference value
is selected based on other parameters associated with the user.
[0082] At a step 310, motion signals of a user are obtained using
one or more sensors 116. User-information representative of the
motion signals is communicated from one or more sensors 116 to
brick 130. In some embodiments, pre-processing and conditioning of
the motion signal information, which may include, for example,
thresholding or flooring, artifact censoring, normalization, or DSP
filtering, and other pre-processing and conditioning as described
in connection with step 520 in FIG. 5, takes place either at the
sensor 116 in brick 130, or both. At a step 320, MdSI is determined
in accordance with the method described in connection to steps 520
to 550 of FIG. 5. At a step 350, the difference between the motion
dispersion stability index and a reference value is determined.
Based on the results of this difference, at a step 360, a
determination is made as to whether the difference is
significant.
[0083] In one embodiment, significance is based on parameters
associated with the particular user. For example, a younger more
active user may be afforded a greater difference than a less active
user who has a known history of falling, stumbling, other motor
problems, or otherwise has a higher risk for falling. At step 360,
where the determined difference is not significant, the method
returns to step 310. In one embodiment, new kinematic information
or other physiologic timeseries information is continuously
collected as it is available simultaneously as processing for
determining stability-theoretic measures occurs, as described above
in connection to FIG. 5. At step 360, where the determined
difference is significant, the method proceeds to a step 370. At
step 370, notification of increased risk for falling is provided.
In one embodiment, the notification is provided in a manner as
described at step 570 in connection to FIG. 5.
[0084] Turning now to FIG. 4, a flow diagram 400 is provided
illustrating an exemplary method according to one embodiment. At a
high level, flow diagram 400 illustratively depicts a method for
determining a motion dispersion stability index (MdSI) for an
individual. At a step 415, translational or rotational kinematic
information is determined from accelerometer or gyroscopic signals
representative of movements of an individual. At a step 420 the
MdSI is computed, in accordance with the method described in
connection to steps 520 to 550 of FIG. 5, as a function of the
kinematics determined in step 415. In the embodiment shown in FIG.
4, at a step 422, the user-derived signal is prepared. In some
embodiments, this includes pre-processing and conditioning of the
motion signal information, which may include, for example,
thresholding, artifact censoring, normalization, or DSP filtering,
as described in connection with step 520 in FIG. 5. Such
preprocessing may be performed by sensor 116, brick 130, or both,
in some embodiments. At a step 424, the maximal value of
root-mean-square differences is determined. At a step 426, the
maximal value of root-mean-square differences are normalized to the
absolute magnitude of the signal-averaged motions. At a step 428,
the Lyapunov exponents are determined, in accordance with the
method described above in connection to FIG. 5.
[0085] By way of example using the embodiment of FIG. 1B, twelve
subjects between the ages of 75 and 92 and 14 control subjects with
no known risk factors for falling were studied. There were 11
falling events in this cohort. The control subjects were free of
known cardiovascular disease except for mild hypertension in one
subject.
[0086] Using the embodiment of FIG. 1B, the MdSI accurately
predicted falling as shown in Table 1 below, where P<0.005
Fisher Exact Test, two-tailed.
TABLE-US-00001 TABLE 1 Falling No Falling MdSI positive 9 1 MdSI
negative 2 14
[0087] In this initial study connected with the
reduction-to-practice of the present invention, the sensitivity of
the MdSI metric to predict falling was 83% and the specificity was
93%. The odds-ratio was 63 and the number-needed-to-treat (NNT) was
2.
[0088] Additionally, a small sample size of cases and controls was
available, so risk stratification by neurologic diagnosis or other
patient-grouping variables was not evaluated, here. In that regard,
it is important to identify those patients at high risk for falling
but who do not have symptoms or prior history of falling. In
follow-on studies, it is anticipated that specific submodels to
predict falling in the presence of those covariables will be
developed. Secondly, it should be noted that falling is not always
because of cerebellar or vestibular causes. For example, muscle
weakness or cognitive failures may be a more frequent cause of
falling in the elderly. It is thus anticipated that
stability-theoretic prediction models as set forth in the present
invention should be useful in these circumstances. Thirdly, the
cases and controls available to us were from acute-care
medical-surgical hospital-based settings.
[0089] Many different arrangements of the various components
depicted, as well as components not shown, are possible without
departing from the spirit and scope of the present invention.
Embodiments of the present invention have been described with the
intent to be illustrative rather than restrictive. Alternative
embodiments will become apparent to those skilled in the art that
do not depart from its scope. A skilled artisan may develop
alternative means of implementing the aforementioned improvements
without departing from the scope of the present invention.
[0090] It will be understood that certain features and
subcombinations are of utility and may be employed without
reference to other features and subcombinations and are
contemplated within the scope of the claims. Not all steps listed
in the various figures need be carried out in the specific order
described. Accordingly, the scope of the invention is intended to
be limited only by the following claims.
* * * * *