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Fall Risk Assessment Through Automatic Combination of Clinical Fall Risk Factors and Body-Worn Sensor Data

  • Kinesis Health Technologies

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Falls are the leading global cause of accidental death and disability in older adults, and are the most common cause of injury and hospitalization. Accurate, early identification of patients at risk of falling, could lead to timely intervention and a reduction in the incidence of fall-related injury and associated costs. We report a statistical method for fall risk assessment using standard clinical fall risk factors (N=748). We also report a means of improving this method by automatically combining it, with a fall risk assessment algorithm based on inertial sensor data and the timed up and go (TUG) test. Furthermore, we provide validation data on the sensor based fall risk assessment method using a statistically independent data set. Results obtained using cross-validation on a sample of 292 community dwelling older adults, suggest that a combined clinical and sensor-based approach yields a classification accuracy of 76.0%, compared to either 73.6% for sensor-based assessment alone, or 68.8% for clinical risk factors alone. Increasing the cohort size by adding an additional 130 subjects from a separate recruitment wave (N=422), and applying the same model building and validation method, resulted in a decrease in classification performance (68.5% for combined classifier, 66.8% for sensor data alone, and 58.5% for clinical data alone). This suggests heterogeneity between cohorts may be a major challenge when attempting to develop fall risk assessment algorithms which generalize well. Independent validation of the sensor-based fall risk assessment algorithm on an independent cohort of 22 community dwelling older adults yielded a classification accuracy of 72.7%. Results suggest that the present method compares well to previously reported sensor based fall risk assessment methods in assessing falls risk. Implementation of objective fall risk assessment methods on a large scale has the potential to improve quality of care and lead to a reduction in associated hospital costs, due to fewer admissions and reduced injuries due to falling.
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Abstract Falls are the leading global cause of accidental
death and disability in older adults, and are the most common
cause of injury and hospitalization. Accurate, early identification
of patients at risk of falling, could lead to timely intervention and
a reduction in the incidence of fall-related injury and associated
We report a statistical method for fall risk assessment using
standard clinical fall risk factors (N=748). We also report a
means of improving this method by automatically combining it,
with a fall risk assessment algorithm based on inertial sensor
data and the timed up and go (TUG) test. Furthermore, we
provide validation data on the sensor based fall risk assessment
method using a statistically independent data set.
Results obtained using cross-validation on a sample of 292
community dwelling older adults, suggest that a combined
clinical and sensor-based approach yields a classification
accuracy of 76.0%, compared to either 73.6% for sensor-based
assessment alone, or 68.8% for clinical risk factors alone.
Increasing the cohort size by adding an additional 130 subjects
from a separate recruitment wave (N=422), and applying the
same model building and validation method, resulted in a
decrease in classification performance (68.5% for combined
classifier, 66.8% for sensor data alone, and 58.5% for clinical
data alone). This suggests heterogeneity between cohorts may be
a major challenge when attempting to develop fall risk
assessment algorithms which generalize well. Independent
validation of the sensor-based fall risk assessment algorithm on
an independent cohort of 22 community dwelling older adults
yielded a classification accuracy of 72.7%.
Results suggest that the present method compares well to
previously reported sensor based fall risk assessment methods in
assessing falls risk. Implementation of objective fall risk
assessment methods on a large scale has the potential to improve
quality of care and lead to a reduction in associated hospital
costs, due to fewer admissions and reduced injuries due to
BRG is a director of Kinesis Health Technologies Ltd, a company with a
license to commercialise this technology.
We would like to acknowledge the help and support of the staff of the
Irishtown & Ringsend Primary Care Centre Physiotherapy Department, and
the TRIL Clinic, St James’ Hospital, Dublin, and the participants involved in
this study.
B.R Greene is with Kinesis Health Technologies, Dublin, Ireland (e-mail:
S. J Redmond is with the Graduate School of Biomedical Engineering
University of New South Wales, Sydney, Australia. (e-mail:
B. Caulfield, Insight centre and School of Physiotherapy and
Performance Science, University College Dublin, Ireland (e-mail:
An ageing global population coupled with increasing
healthcare costs has led to prevention of falls in the elderly to
become an increasingly pressing healthcare issue. Roughly
one in three adults over 65 fall each year [1] and it has been
estimated that the annual medical costs of fall-related injuries
for people aged 65 years and older, in the USA alone, is
US$30 billion [2, 3]. The incidence of falls and their
associated costs will increase significantly [3, 4] with the
continued ageing demographics. The causes of falls are
complex with a wide variety of social [5], physical and
environmental [6] risk factors. However, gait and mobility
impairments are considered to be leading risk factors for falls
[7, 8], but crucially have been shown to be modifiable
through exercise intervention [9].
In current clinical practice patient’s risk of falling is usually
assessed by a variety of specialists: physical therapists,
geriatric specialists, and community nurses or occupational
therapists. There are a number of widely used clinical
assessment tools for assessing risk of falls [10-12]. However
these current methods are somewhat subjective in nature and
often require specialist clinical expertise. An objective,
evidence-based method that facilitates screening for fall risk,
suitable for use by non-specialists, would free up specialists’
time and allow for identification of people who need onward
referral to specialist services. This would allow such
specialist services to concentrate their efforts on detailed
examination to identify the specific factors leading to the
level of risk identified, and subsequent implementation of
appropriate remedial interventions to ameliorate the risk. This
should lead to improved quality of care and a reduction in
associated hospital costs, due to fewer admissions and
reduced injuries due to falling.
In recent years, there has been a proliferation of research
reporting a variety of inertial sensor-based methods for
assessing fall risk in older adults [13-18]. These are usually
based on the use of quantitative data obtained during
prescribed motor tasks, such as quiet standing, walking or a
TUG (timed-up-and-go) test. The majority of studies have
validated their methods using cross-sectional (retrospective)
fall data, i.e., each patient’s self-reported history of falling
prior to assessment [19, 20]. A smaller number of trials have
reported a prospective validation of their methods, i.e., each
subject was followed-up and their falls tracked, for a period
of time after the assessment is performed; these fall data were
then used to validate the methodsperformance in predicting
falls [15, 21]. Previous research [22] has reported that fall
Fall risk assessment through automatic combination of clinical fall
risk factors and body-worn sensor data
Barry R. Greene, Senior IEEE Member, Stephen J. Redmond Senior IEEE Member, Brian Caulfield,
IEEE Member
history data were more effective in classifying healthy
controls than two year prospective data (fall counts during a
follow-up period).
Recently Shany et al. [23] and Howcroft et al. [19] have
pointed out weaknesses in the methodologies of many of
these reported papers which aim to assess fall risk and/or
predict falls. Specifically, they noted insufficient validation
and inappropriate use of statistical modelling methods in
some or all of the papers published to date. In this paper we
attempt to address some of these concerns by means of a re-
examination of previous research findings from our group, as
well as validation of risk assessment models with independent
data. In doing so, we will address three issues that were raised
by Shany et al. [23], as well as highlighting a potentially
dominant data analysis issue that should be considered in
future studies. Firstly, we examine the impact of applying best
practice statistical modelling methods, specifically, a nested
cross-validation approach. We also examine the impact that
heterogeneity in terms of patient data and in outcome
measures, has on the efficacy of sensor-based fall risk
assessment algorithms.
This paper introduces two new methods for fall risk
assessment; the first is based on standard clinical risk factors
obtained from questionnaires; the second is a means of
improving this questionnaire-based method by automatically
combining it with a sensor-based method for fall risk
assessment. In addition, we quantify the validity of a
previously-published sensor-based fall risk assessment
algorithm from our group using a fully independent dataset.
A. Training and validation data set
Seven hundred and forty eight (519 female, age 73.6±7.4
years) subjects were assessed as part of the TRIL project, a
large ageing research project in Dublin, Ireland. All subjects
provided informed consent and had their mobility assessed
using the TUG test instrumented with inertial sensors in the
TRIL clinic, St James’s Hospital (SJH), Dublin, Ireland.
Subjects were assessed between 2007 and 2012; data from
two separate phases of the trial (waves) are included here.
The Wave 1 data set contained 616 subjects, while Wave 2
contained 132 subjects. Inclusion criteria were subjects 60
years and older, with no history of stroke, able to walk
without assistance. Ethical approval was received from the
local research ethics committee in each instance as well as
informed consent from each subject.
History of falls in the past 5 years for each subject was
obtained by means of a questionnaire. A fall was defined as
an event which resulted in a person coming to rest on the
lower level regardless of whether an injury was sustained,
and not as a result of a major intrinsic event or
overwhelming hazard [24]. Fall outcome data were verified
using available hospital records as well as information
provided by relatives.
Each subject completed at least one TUG test,
instrumented with inertial sensors. Due to issues with
hardware failure and data storage during sensor data
acquisition, the sensor data for some subjects were
incomplete. Subjects with incomplete sensor data were
excluded from the analysis. Inertial sensor data were available
for 422 subjects (308 female; 292 from Wave 1, 130 from
Wave 2).
B. Clinical assessment
Clinical fall risk factors were captured for each subject by
means of a clinical assessment [21]. Eyesight was assessed
using the Pelli-Robson contrast sensitivity scale and the
binocular logMAR scale. Each subject was checked for
orthostatic hypotension (defined as orthostatic systolic blood
pressure drop > 20mmHg), using a Finometer (Finapres
Medical Systems, Amsterdam, Netherlands). Each subject’s
prescription medications were reviewed to determine if they
had a polypharmacy issue, where polypharmacy is defined as
the use of four or more prescription medications.
C. Independent data set
An independent data set of community-dwelling older adults
was obtained to validate the expected performance of the
sensor-based fall risk assessment on unseen data. Twenty-five
older adults (22 female), were assessed at the Irishtown &
Ringsend Primary Care Centre (Dublin 4, Ireland). Subjects
were aged 64-87 years (mean 75.4 years) and recruited
through a community fall prevention service. TUG tests were
conducted on 25 subjects using the inertial sensor method
described above. Sensor data from one recording was not
usable due to improper sensor placement while falls history
data from two subjects were not available, leaving a total of
22 valid tests, one test per participant.
Each subject completed a questionnaire on their history of
falls in the past 12 months. 11 of 22 patients reported a
history of falls. These data were used to validate the
performance of the fall risk assessment tool. Prospective data
were not available, and previous falls history is considered a
good surrogate for future falls [25, 26]. The use of an
independent data set means that a valid confidence interval
can be calculated. A binomial proportion confidence interval
was used to estimate the confidence interval for classification
accuracy [27].
Figure 1: Medio-lateral shank angular velocity signal obtained from an 85
year old female with a history of falls while performing a TUG test. Initial
contact (IC) and terminal contact (TC), as well as mid-swing points, are
A. TUG test protocol
The TUG test is a standard test of mobility, used all over the
world to screen for gait and balance issues in the elderly [28-
30]. The test requires the subject to get up from a chair, walk
three metres, turn through 180º at a designated spot, return to
the seat and sit back down. The time taken to perform the test
is recorded by a clinical staff member using a stopwatch. The
performance of older adults prone to falling can be very
different from those who do not fall [31-33]; longer TUG
times are thought be associated with falling. However,
research has shown that the TUG test alone is only
moderately accurate in assessing fall risk [31, 33]. Subjects
were asked to complete the TUG test, ‘as fast as safely
possible’. A standard chair (46 cm high seat, 65 cm) with
armrests was used. The timer was started the moment the
clinician said ‘go’, and stopped the moment the subject’s
back touched the back rest of the chair. Each subject was
given time to become familiar with the test and the test was
demonstrated to them beforehand.
B. Sensor data acquisition
For each TUG test, subjects were fitted with two wireless
inertial sensors which were attached by a clinician or research
assistant, using elasticated bandages, to the mid-point of the
left and right anterior shank (shin)[13]. Sensors were oriented
to capture movement about the anatomical medio-lateral axis.
Inertial sensor data (see Figure 1) were synchronously
acquired in real-time via Bluetooth using dedicated software
(QTUG, Kinesis Health Technologies; Dublin, Ireland). Each
sensor was tri-axial and contained an accelerometer and
gyroscope. Sensor data were sampled at 102.4 Hz with a full
scale range of 500 °/s and a sensitivity of 2 mV/°/s. Sensors
were calibrated using a standard method [34]. The raw tri-
axial gyroscope signals were low-pass filtered (zero-phase 2nd
order Butterworth filter, 20 Hz corner frequency). Data
acquisition software automatically connected to the relevant
sensors, allowing the clinician to start and stop the recording
manually as with a stopwatch. After each test, data were
saved to text format for analysis.
C. Clinical risk factor based fall risk assessment
The AGS/BGS offer guidelines aimed to capture the main
clinical risk factors linked to falls in older adults [35]. These
guidelines concentrate on self-reported data on standard fall
risk factors, captured as part of a clinical assessment. We
aimed to create an easily reproducible classifier model based
on these standard self-reported risk factors.
A logistic regression model was created using a number of the
self-reported factors discussed in the AGS/BGS guidelines,
the factors selected were those that were also present in the
TRIL data set. All available data were used. The features
included and used to classify subjects according to falls
history were as follows: gender, height, weight and age on
date of assessment, polypharmacy, vision problems and
orthostatic hypotension.
Two candidate logistic regression models, used to produce a
fall risk estimate (FREclinical), are trained using either data
from Wave 1 (2007) alone or Wave 1 and Wave 2 (2012)
combined. An estimate of the classifier performance of each
model on unseen data was obtained using ten repetitions of
ten-fold cross-validation [36].
D. Inertial sensor-based fall risk assessment
Previously, we reported an inertial sensor-based method to
quantify, standing, walking and turning during the TUG test.
We used these quantitative TUG parameters along with a
supervised classifier model to obtain a statistical fall risk
estimate (FREsensor) for older adults. The method was
validated using both prospective (two year follow-up) [21]
and cross-sectional (falls history) falls data [13]. Previously,
testing this method on two sets of healthy control data found
that classifier models trained using falls history data was far
more accurate in correctly distinguishing healthy control
subjects (defined as healthy older adults, aged greater than
50, with no history of falls) from fallers, and so less prone to
false positives than models trained using prospective falls
data [22]. In this study, only falls history data were used to
validate performance.
The sensor-based fall risk assessment method used a
regularized discriminant (RD) classifier model [37], with
regularization parameter values set to λ=0.1 and r=0.1 prior
to analysis. Ten repetitions of ten-fold cross validation [36]
was used to estimate the generalized classifier performance.
Using only the training data for each iteration of the cross-
validation routine, a potential feature set was evaluated using
a second inner cross-validation loop. Once a feature set is
identified using the training data, it is tested using the
withheld data for this iteration of the outer cross-validation
loop [38], a process known as ‘nested’ cross-validation.
Training and testing sets were randomly selected for each
Classifier models were trained using the subset of subjects
with sensor data, from either data from Wave 1 (2007) alone
or Wave 1 and Wave 2 (2012) combined.
E. Combined clinical and inertial sensor fall risk assessment
A combined fall risk estimate (FREcombined) is obtained by
applying classifier combination theory [37] and averaging the
posterior probabilities produced for a given subject by the
sensor-based FRE (FREsensor) and the clinical FRE (FREclinical)
to produces a combined FRE (FREcombined):
 
clinicalsensorcombined FREFREFRE
To examine the performance of FREcombined relative to
FREsensor and FREclinical, the classification performance is
compared (see Table 1 below) on two common data sets
(Wave 1 as well as the combination of Wave 1 and Wave 2).
The data sets used were the subset that had both sensor and
clinical data. The classifier performance for each model was
estimated using Leave One Out (LOO) cross-validation,
where N-1 samples were used to train the classifier model and
the remaining sample used to test the performance, with this
process repeated for each sample. The FREsensor feature and
model selection was conducted using the nested cross-
validation as discussed above, where model selection is
conducted using 10 repetitions of 10-fold cross-validation
using only the training data, within the LOO procedure.
F. Independent validation of inertial sensor fall risk
Cross-sectional data (self-reported fall history) from the two
independent data sets were used to validate the previously
reported sensor-based fall risk assessment method (FREsensor)
[22] to obtain a statistically independent evaluation of the
algorithm performance on a cohort of independent subjects.
G. Classifier performance metrics
Classifier performance for each fall risk assessment algorithm
was assessed using standard classifier performance measures.
Accuracy (Acc) is defined as the percentage of subjects
correctly classified by the algorithm as being a faller or
non-faller (a faller is defined as having one injurious fall or,
more than one previous fall); sensitivity (Sens) is defined as
the percentage of the fallers identified correctly; similarly,
specificity (Spec) is defined as the percentage of the non-
fallers correctly identified. Positive predictive value (PPV) is
defined as the proportion of subjects the algorithm classified
as fallers, who are correctly classified; negative predictive
value (NPV) is the proportion of subjects the algorithm
classified as non-fallers, who are correctly classified. The
values reported for each performance metric were averaged
across the folds of the cross-validation routine.
A. Clinical risk factor based fall risk assessment
Cross-validated results for the FREclinical logistic regression
classifier model are detailed in Table 1. Wave 1 data (N=616)
yielded a classification accuracy of 70.37%, while combining
Wave 1 and Wave 2 data (N=748) yielded a slightly lower
accuracy of 67.13%.
B. Combination of clinical risk factor classifier with sensor
data classifier
Cross-validated results for the FREcombined, FREsensor, and
FREclinical classifier models are detailed in Table 2. Results
are shown for the subsets of Wave 1 data (N=292) and Wave
1 and 2 data combined (N=422), with both clinical and sensor
data. The combined model was 76.03% accurate for Wave 1
and 68.48% for the combined Wave 1 and Wave 2 data set.
The inertial sensor-based method achieved 73.63% accuracy
for Wave 1 data and was 66.82% accurate for the
combination of Wave 1 and Wave 2. The clinical risk factor-
based model was 68.84% accurate for Wave 1 data and
58.53% accurate for Wave 1 and Wave 2 data combined.
C. Validation of sensor fall risk assessment using
independent data set
Results for a statistically independent validation of the
sensor-based fall risk assessment algorithm, using fall history
data from a community-dwelling population are detailed in
Table 3, below. Sixteen of the 22 tests were correctly
identified as being a ‘faller’ or a ‘non-faller’ yielding a
72.70% classification accuracy. A binomial proportion
confidence interval calculation yielded a confidence interval
for classification accuracy of 54.12-91.34%.
This paper aimed to introduce a number of new results to the
field of sensor-based assessment of fall risk. Specifically, we
report a method for obtaining a statistical estimate of fall risk
based on a questionnaire on standard fall risk factors. We also
report a classifier fusion approach that combines the classifier
derived from clinical risk factors, with a classifier derived
from inertial sensor data obtained during the TUG test. In
Wave 1
Wave 1
Wave 1/2
Acc (%)
Sens (%)
Spec (%)
PPV (%)
NPV (%)
Acc (%) [95% CI]
72.70 [54.12, 91.34]
Sens (%) [95% CI]
90.91 [78.90, 100.0]
Spec (%) [95% CI]
54.50 [33.69, 75.31]
PPV (%) [95% CI]
66.67 [46.97, 86.37]
NPV (%) [95% CI]
85.71 [71.09, 100.0]
addition, we report a validation of a sensor-based falls risk
assessment algorithm on a completely independent data set.
For a single wave (Wave 1) of 293 subjects, a classifier
trained using only questionnaire based clinical risk factors
was 68.84% accurate in classifying subjects as fallers or non-
fallers; these results are consistent with results (0.64 area
under the receiver operating curve (AUC)) reported for the
FRAT-up questionnaire-based classifier model, by Cattelani
et al. [39].
For the subset of Wave 1 data that had sensor data, the
sensor-based method [22] for assessing fall risk was 73.63%
accurate in classifying subjects according to history of falls.
Results from a study [13] on the same (Wave 1) data set
showed that TUG time taken alone was 60.1% accurate in
classifying subjects according to falls history, while the Berg
balance scale [10] was 61.4% accurate.
Combining the sensor-based fall risk assessment classifier and
clinical fall risk factor classifier using classifier fusion led to
an improved classification accuracy of 76.03%, compared to
either clinical or sensor data taken alone. Combining subjects
from two waves (N=422) and applying the same methods
yielded reduced classification accuracies for all three
classifiers; 58.53% (FREclinical), 66.82% (FREsensor) and
68.48% (FREcombined).
Validation of the sensor based fall risk assessment algorithm
on an independent data set (N=22) of community-dwelling
older adults yielded classification accuracy of 72.7% [54.12,
This study sought to address some deficiencies in the
literature as highlighted by recent commentary [23]. We
employed rigorous statistical validation (nested cross-
validation) on a large data set to obtain performance
estimates. We also provide a validation of the sensor based
algorithm on a statistically independent data set; this is
significant as a criticism of much of the sensor-based fall risk
assessment research to date, is that there has been no
independent validation of methods on completely
independent data sets. The results of this validation using
independent data show high sensitivity but relatively low
specificity, suggesting that the sensor-based method
recognize some subjects as being at risk of falling although
they had no prior history of falling. Prospective validation of
the method on a large data set, using best practice for
prospective studies on falls (i.e., one year follow-up for falls
with daily falls diaries, collected on a weekly basis) would be
required to definitively assess the method’s performance in
predicting falls.
Our results suggest that improved automatic assessment of
fall risk can be achieved through the automatic combination
of clinical fall risk factors and a sensor-based physical
assessment. It has been noted in the literature that different
classifier models, trained on the same outcomes with different
data sets, offer potentially complementary information about
the patterns to be classified, which can be harnessed to
improve overall performance [37, 40]. Theoretical results
demonstrate that combining classifiers from different modes
with generalized knowledge of the patterns to be classified
often yields improved, more robust, classification
Classification results from Wave 1 and 2 combined were
consistently lower than those from Wave 1 alone. This
suggests that heterogeneity between waves is a major issue
affecting classifier performance. Sources of heterogeneity
include: the fact that Wave 2 data were captured several years
after Wave 1; and the assessments performed by different
personnel (with potential differences in the instructions given
on how to complete the test); different sensors (with
variations in calibration) were used; Wave 1 data used a 5
year interval for falls history, while Wave 2 used a 12 month
interval; while subjects in both waves consisted of community
dwelling older adults. Another significant source of
heterogeneity is the fact that Wave 1 subjects were largely
recruited through the hospital system, while Wave 2 subjects
were almost entirely recruited through advertisement in the
Results for the sensor-based fall risk assessment method
reported here agree broadly in terms of accuracy with
previously reported results on the same data [13, 22],
suggesting that the effect of bias/overlearning arising from
model selection deviating from best practice (as discussed by
Shany et al. [23]), on previous results that did not employ
nested cross-validation, was relatively small. We would
suggest instead that heterogeneity is a bigger issue to consider
in the application of sensor-based fall risk assessment
algorithms to broader subject populations.
The cross-sectional falls data is a significant limitation of this
study. Subjects were classified according to their history of
falling, which is an established risk factor for future falls [8,
25] and so aims to produce a noisy estimate of the subjects
propensity to fall. However, this retrospective design is a
clear limitation, given that self-reported historical falls data
can be unreliable [41] in terms of subject recall bias, and gait
changes due to previous falls. The current best practice for
the use of outcome data in falls injury prevention trials, as
recommended by the consensus guidelines [42], is the use of
prospective fall diaries. Prospective fall data can suffer from
issues with recall; however, if data are collected according to
best practice, the inaccuracy due to recall error can be
minimized. An additional limitation of this study is the small
size of the independent data set. Future studies may allow for
a larger scale independent validation. A significant number of
the subjects in this study were self-referred, which might
indicate differences when compared to a cohort of hospital in-
patients or patients in long term care. The results reported
here generalized well across the study cohort, however if a
comparison were made against broader patient populations,
differences may be apparently due to the fact the study cohort
were considered to quite physically robust.
In summary, we have introduced a statistical method for
assessing fall risk using standard clinical risk factors, as well
as an algorithm that automatically combined classifiers of
clinical risk factors and inertial sensors parameters, to
produce improved assessment of fall risk in older adults.
While it is difficult to compare performance estimates across
studies, the present results suggest our method may compare
well with previously reported methods for sensor based
assessment of fall risk and compares favourably with the
theoretical maximal accuracy for fall risk assessment
algorithms of 81% suggest by Palumbo et al. [43]. The
algorithm was validated using nested cross-validation to
reduce the possibility of biased performance estimates due to
model overfitting. In addition, we report validation results,
using fall history, of a sensor based fall risk assessment
algorithm on an independent data set. We also investigated
the effect of heterogeneity on classification results for sensor-
based fall risk assessment algorithms using data from two
waves of a large ageing research study. Heterogeneity appears
to account for a large discrepancy in accuracy. Future studies
employing a statistically meaningful representative sample
would be necessary to overcome the effect of heterogeneity, a
topic which has not been adequately explored in this field to
Implementation of objective fall risk assessment methods on a
large scale has the potential to prevent falls through early
intervention, reduce health care costs, and improve the quality
of care offered to patients at risk of falling. Although
identification of those at risk is but a first step in reducing the
incidence of falls, it is a critical element of an effective and
efficient end-to-end falls prevention service. Early
identification of patients at risk of falling would allow more
timely intervention, such as targeted physiotherapy,
medication changes, or occupational therapy. It is important
to recognize that such improvements in care and reductions in
cost will not be realized, unless the identification of falls risk
is coupled with an effective service that further explores the
specific factors leading to risk at an individual level, leading
to implementation of appropriate remedial strategies.
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... Previous research from our group has shown promising results when instrumenting the TUG test with inertial sensors (QTUG), combining signal processing and machine learning algorithms to produce a statistical estimate of the patient's risk of having a fall [27,38] as well as a statistical estimate of their level of frailty [39]. QTUG has been shown to be reliable in the measurement of gait and mobility [40], as well as accurate in predicting falls in PD [22] and community dwelling older adults [38,41]. ...
... Previous research from our group has shown promising results when instrumenting the TUG test with inertial sensors (QTUG), combining signal processing and machine learning algorithms to produce a statistical estimate of the patient's risk of having a fall [27,38] as well as a statistical estimate of their level of frailty [39]. QTUG has been shown to be reliable in the measurement of gait and mobility [40], as well as accurate in predicting falls in PD [22] and community dwelling older adults [38,41]. We believe a statistical model based on inertial sensor measures of movement has the potential to be used as a surrogate measure of falls counts in patients with PD. ...
... The data used to train all the statistical models (Training Dataset (TD)) were obtained from the TRIL research project, which examined technologies to support positive ageing and included a focus on the prevention of falls. These data were combined with a number of other smaller datasets arising from separate research studies to form a reference dataset, used to train the falls risk estimate (FRE) classifier models and mobility risk scores included in the Kinesis QTUG™ product [27,38,42,43]. ...
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People with Parkinson’s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson’s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R2 value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.
... However, in 2011, 6/7 of the reported upper body sensors were used in one of the articles where a total of ten sensors were used [20], and only one article was included from 2015. The two articles published in 2014 [27] and 2017 [36] have the same main author and report on the use of sensors located at the shin/shank. The two included articles from 2016 use five different sensor locations, and 6/7 of the sensors were used in one article [29] where most of them were located on the lower body (shins/shanks and under the feet soles), and two of them on the upper body (head and pelvis). ...
... In the studies using classification models without machine learning algorithms (Table 5), three articles from the same main author [27,36,49] used over 40 sensor features in regularized discriminant classifier models. The other two studies [22,23], which both used regression models, utilized 10 and 14 sensor features respectively. ...
... The majority of the sensors used in the studies presented in Tables 5 and 6 were located on the lower body with shin/shank being the most common location. Three of the studies in Table 5 [27,36,49] used TUG as the assessment task. All of them positioned the sensors on the shin/shank, i.e., the same location as in the recommended triad (6) above for TUG which included temporal-shins. ...
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Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring indi- viduals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in- sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.
... Each participant was evaluated using a CGA to establish any clinical characteristics that may contribute to their fall risk. This assessment of fall risk factors was implemented in accordance with guidelines set out by the British and American Geriatrics Societies [31] and has been utilised in previous studies in this cohort [32]. Vision was tested for sharpness (Binocular logMAR) and contrast sensitivity (Pelli-Robson). ...
... A logistic regression classifier model, which has been validated in a previous study [32], was created based solely on the clinical risk factor data captured in the CGA to establish fall risk estimate known as FREclinical. The clinical parameters that were included in the model were as follows: ...
Ageing incurs a natural decline of postural control which has been linked to an increased risk of falling. Accurate balance assessment is important in identifying postural instability and informing targeted interventions to prevent falls in older adults. Inertial sensor (IMU) technology offers a low-cost means for objective quantification of human movement. This paper describes two studies carried out to advance the use of IMU-based balance assessments in older adults. Study 1 (N=39) presents the development of two new IMU-derived balance measures. Study 2 (N=248) reports a reliability analysis of IMU postural stability measures and validates the novel balance measures through comparison with clinical scales. We also report a statistical fall risk estimation algorithm based on IMU data captured during static balance assessments alongside a method of improving this fall risk estimate by incorporating standard clinical fall risk factor data. Results suggest that both new balance measures are sensitive to balance deficits captured by the Berg Balance Scale (BBS) and Timed Up and Go test. Results obtained from the fall risk classifier models suggest they are more accurate (67.9%) at estimating fall risk status than a model based on BBS (59.2%). While the accuracies of the reported models are lower than others reported in the literature, the simplicity of the assessment makes it a potentially useful screening tool for balance impairments and falls risk. The algorithms presented in this paper may be suitable for implementation on a smartphone and could facilitate unsupervised assessment in the home.
... Nineteen quantitative balance features were calculated for each balance assessment including a balance score (a percentile based metric calculated by comparison against the reference data set) [23] and a fall risk estimate (FRE combined , a statistical prediction of falls derived using a combination of inertial sensor and questionnaire data, previously validated using a sample of community dwelling older adults [24,30]). FRE combined uses a classifier fusion approach to combine two logistic regression classifiers, one each based on the questionnaire and inertial sensor data [23][24][25]. ...
... To our knowledge, the present study is the largest to date and the only study that reports the performance of algorithms that were trained prior to deployment in the app on an independent data set. The algorithms employed here have been previously validated and shown to be valid and accurate in assessing balance and falls risk [6,24,25,30]. Statistically significant differences were observed between quantitative balance features obtained using the smartphone data and those from the reference set. These significant differences in demographic and inertial sensor features persisted even when only those participants (from the smartphone data set), aged 60 or above, were included in the analysis in an attempt to age-match the samples. ...
Full-text available
Assessment of health and physical function using smartphones (mHealth) has enormous potential due to the ubiquity of smartphones and their potential to provide low cost, scalable access to care as well as frequent, objective measurements, outside of clinical environments. Validation of the algorithms and outcome measures used by mHealth apps is of paramount importance, as poorly validated apps have been found to be harmful to patients. Falls are a complex, common and costly problem in the older adult population. Deficits in balance and postural control are strongly associated with falls risk. Assessment of balance and falls risk using a validated smartphone app may lessen the need for clinical assessments which can be expensive, requiring non-portable equipment and specialist expertise. This study reports results for the real-world deployment of a smartphone app for self-directed, unsupervised assessment of balance and falls risk. The app relies on a previously validated algorithm for assessment of balance and falls risk; the outcome measures employed were trained prior to deployment on an independent data set. Results for a sample of 594 smartphone assessments from 147 unique phones show a strong association between self-reported falls history and the falls risk and balance impairment scores produced by the app, suggesting they may be clinically useful outcome measures. In addition, analysis of the quantitative balance features produced seems to suggest that unsupervised, self-directed assessment of balance in the home is feasible.
... In addition, the machine learning methods based on tree models were also widely used for data prediction, such as basic decision tree models and related integrated models such as random forests. erefore, the research on human resource forecasting using machine learning plays an important role in improving forecasting accuracy [26][27][28][29][30]. ...
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Human resources are the core resources of an enterprise, and the demand forecasting plays a vital role in the allocation and optimization of human resources. Starting from the basic concepts of human resource forecasting, this paper employs the backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) to analyze human resource needs and determine the key elements of the company’s human resource allocation through predictive models. With historical data as reference, the forecast value of current human resource demand is obtained through the two types of neural networks. Based on the prediction results, the company managers can carry out targeted human resource planning and allocation to improve the efficiency of enterprise operations. In the experiment, the actual human resource data of a certain company are used as the experimental basic samples to train and test the two types of machine learning tools. The experimental results show that the method proposed in this paper can effectively predict the number of personnel required and can support the planning and allocation of human resources.
... Based on the existing methods, this paper proposes a textile industry foreign investment risk prediction method by combination of LSTM and ResNet [30][31][32][33]. First, an indicator system is established for investment risks in the textile industry, and feature vectors are constructed to describe the risk levels in the current state. ...
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With the decline of China's economic growth rate and the uproar of antiglobalization, the textile industry, one of the business cards of China's globalization, is facing a huge impact. When the economic model is undergoing transformation, it is more important to prevent enterprises from falling into financial distress. So, the financial risk early warning is one of the important means to prevent enterprises from falling into financial distress. Aiming at the risk analysis of the textile industry's foreign investment, this paper proposes an analysis method based on deep learning. This method combines residual network (ResNet) and long short-term memory (LSTM) risk prediction model. This method first establishes a risk indicator system for the textile industry and then uses ResNet to complete deep feature extraction, which are further used for LSTM training and testing. The performance of the proposed method is tested based on part of the measured data, and the results show the effectiveness of the proposed method.
... Wiginton et al. first proposed the logistic regression model for credit evaluation [20]. Logistic regression uses the sigmoid function to convert the value obtained after linear regression into a probability value and sets an empirical threshold between 0 and 1 to realize the binary classification problem [23][24][25]. e risk assessment model based on machine learning has gradually emerged in recent years, showing its superiority compared with traditional risk assessment methods. Common modern machine learning methods include BP neural network, K nearest neighbors (KNN), support vector machine (SVM), etc. ...
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Scientific risk assessment is an important guarantee for the healthy development of an enterprise. With the continuous development and maturity of machine learning technology, it has played an important role in the field of data prediction and risk assessment. This paper conducts research on the application of machine learning technology in enterprise risk assessment. According to the existing literature, this paper uses three machine learning algorithms, i.e., random forest (RF), support vector machine (SVM), and AdaBoost, to evaluate enterprise risk. In the specific implementation, the enterprise’s risk assessment indexes are first established, which comprehensively describe the various risks faced by the enterprise through a number of parameters. Then, the three types of machine learning algorithms are trained based on historical data to build a risk assessment model. Finally, for a set of risk indicators obtained under current conditions, the risk index is output through the risk assessment model. In the experiment, some actual data are used to analyze and verify the method, and the results show that the proposed three types of machine learning algorithms can effectively evaluate enterprise risks.
... 5-24 15.4% [26,29,32,47,63,68,73,74] 25- 49 19.2% [27,28,34,35,51,55,58,59,67,72] 50-99 32.7% [30,31,33,36,37,39,50,52,53,56,57,60,61,62,65,71,77] 100-149 13.5% [38,45,48,49,69,70,78] 150-199 3.8% [64,75] 200-299 9.6% [25,40,43,46,76] ...
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Falls represent a major burden on elderly individuals and society as a whole. Technologies that are able to detect individuals at risk of fall before occurrence could help reduce this burden by targeting those individuals for rehabilitation to reduce risk of falls. Wearable technologies especially, which can continuously monitor aspects of gait, balance, vital signs, and other aspects of health known to be related to falls, may be useful and are in need of study. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) 2009 guidelines to identify articles related to the use of wearable sensors to predict fall risk. Fifty four studies were analyzed. The majority of studies (98.0%) utilized inertial measurement units (IMUs) located at the lower back (58.0%), sternum (28.0%), and shins (28.0%). Most assessments were conducted in a structured setting (67.3%) instead of with free-living data. Fall risk was calculated based on retrospective falls history (48.9%), prospective falls reporting (36.2%), or clinical scales (19.1%). Measures of the duration spent walking and standing during free-living monitoring, linear measures such as gait speed and step length, and nonlinear measures such as entropy correlate with fall risk, and machine learning methods can distinguish between falls. However, because many studies generating machine learning models did not list the exact factors being considered, it is difficult to compare these models directly. Few studies to date have utilized results to give feedback about fall risk to the patient or to supply treatment or lifestyle suggestions to prevent fall, though these are considered important by end users. Wearable technology demonstrates considerable promise in detecting subtle changes in biomarkers of gait and balance related to an increase in fall risk. However, more large-scale studies measuring increasing fall risk before first fall are needed, and exact biomarkers and machine learning methods used need to be shared to compare results and pursue the most promising fall risk measurements. There is a great need for devices measuring fall risk also to supply patients with information about their fall risk and strategies and treatments for prevention.
In recent years, with the acceleration of the aging of the population, the safety of the elderly living alone has attracted great attention, and the falls have become one of the main factors leading to elderly casualties. In order to obtain a high precision and low cost fall detection system for the elderly, a fall detection system based on infrared array sensor and multi-dimensional feature fusion is proposed in this paper. First, we propose a new data acquisition method using infrared array sensor, which effectively enlarges the detection area. Then the personnel positioning is performed before fall detection, which can ensure real-time detection while reducing computational complexity. In addition, a sliding window algorithm is developed and four representative features of a fall are extracted from the collected data, which is fitful to the online detection. Among them, the four characteristics include the change in the center of mass of the falling process, the change in the speed, the change in the area of the person, and the change in variance. Finally, based on the refined features, the support vector machine (SVM) classifier is introduced to identify falls and improve the classification accuracy. The experimental results validate that the proposed fall detection system shows good fall detection accuracy and great practicability.
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Applications within mobile devices, although useful and entertaining, come with security risks to private information stored within the device such as name, address, and date of birth. Standards, frameworks, models, and metrics have been proposed and implemented to combat these security vulnerabilities, but they remain to persist today. In this review, we discuss the risk calculation of android applications which is used to determine the overall security of an application. Besides, we also present and discuss the permission-based access control models that can be used to evaluate application access to user data. The study also focuses on examining the predictive analysis of security risks using machine learning. We conduct a comprehensive review of the leading studies accomplished on investigating the vulnerabilities of the applications for the Android mobile platform. The review examines various well-known vulnerabilities prediction models and highlights the sources of the vulnerabilities, prediction technique, applications and the performance of these models. Some models and frameworks prove to be promising but there is still much more research needed to be done regarding security for Android applications.
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The following article is a summary of the American Geriatrics Society/British Geriatrics Society Clinical Practice Guideline for Prevention of Falls in Older Persons (2010). This article provides additional discussion of the guideline process and the differences between the current guideline and the 2001 version and includes the guidelines' recommendations, algorithm, and acknowledgments. The complete guideline is published on the American Geriatrics Society's Web site (
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About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls. The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up. FRAT-up is based on the assumption that a subject's fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators. The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration. FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface. NCT01331512 (The InChianti Follow-Up Study); (Archived by WebCite at
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Falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. Accurate identification of patients at risk of falls could lead to timely medical intervention, reducing the incidence of falls related injuries along with associated costs. The current best practice for studies of falls and falls risk recommends the use of prospective follow-up data. However, the majority of studies reporting sensor based methods for assessment of falls risk employ cross-sectional falls data (falls history). The purpose of this study was to compare the performance of sensor based falls risk assessment algorithms derived from cross-sectional (N=909) and prospective (N=259) datasets in terms of false positive rate. The utility of any classification algorithm is clearly limited by a high false positive rate. An estimate of the false positive rate for both cross-sectional and prospective algorithms was determined using an inertial sensor data set of 611 TUG tests from 55 healthy control subjects, with no history of falls. We aimed to determine which falls risk assessment algorithm is more effective at classifying falls risk in healthy control subjects. The cross-sectional algorithm correctly classified 94.11% of tests, while the prospective algorithm, correctly classified 79.38% of tests. Results suggest that sensor based falls risk assessment algorithms generated using cross-sectional falls data, may be more effective than those generated using prospective data in classifying healthy controls and reducing associated false positives.
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The Timed Up and Go test (TUG) is a commonly used screening tool to assist clinicians to identify patients at risk of falling. The purpose of this systematic review and meta-analysis is to determine the overall predictive value of the TUG in community-dwelling older adults. A literature search was performed to identify all studies that validated the TUG test. The methodological quality of the selected studies was assessed using the QUADAS-2 tool, a validated tool for the quality assessment of diagnostic accuracy studies. A TUG score of >=13.5 seconds was used to identify individuals at higher risk of falling. All included studies were combined using a bivariate random effects model to generate pooled estimates of sensitivity and specificity at >=13.5 seconds. Heterogeneity was assessed using the variance of logit transformed sensitivity and specificity. Twenty-five studies were included in the systematic review and 10 studies were included in meta-analysis. The TUG test was found to be more useful at ruling in rather than ruling out falls in individuals classified as high risk (>13.5 sec), with a higher pooled specificity (0.74, 95% CI 0.52-0.88) than sensitivity (0.31, 95% CI 0.13-0.57). Logistic regression analysis indicated that the TUG score is not a significant predictor of falls (OR = 1.01, 95%CI 1.00-1.02, p = 0.05). The Timed Up and Go test has limited ability to predict falls in community dwelling elderly and should not be used in isolation to identify individuals at high risk of falls in this setting.
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Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population. Forty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes. Inertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%). Inertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.
The "get-up and go test" requires patients to stand up from a chair, walk a short distance, turn around, return, and sit down again. This test was conducted in 40 elderly patients with a range of balance function. Tests were recorded on video tapes, which were viewed by groups of observers from different medical backgrounds. Balance function was scored on a five-point scale. The same patients underwent laboratory tests of gait and balance. There was agreement among observers on the subjective scoring of the clinical test, and good correlation with laboratory tests. The get-up and go test proved to be a satisfactory clinical measure of balance in elderly people.
Summary Background: Falls are a prevalent and burdensome problem in the elderly. Tools for the assessment of fall risk are fundamental for fall prevention. Clinical studies for the development and evaluation of prognostic tools for falls show high heterogeneity in the settings and in the reported results. Newly developed tools are susceptible to over-optimism. Objectives: This study proposes a probabilistic model to address critical issues about fall prediction through the analysis of the properties of an ideal prognostic tool for falls. Methods: The model assumes that falls occur within a population according to the Greenwood and Yule scheme for accident-proneness. Parameters for the fall rate distribution are estimated from counts of falls of four different epidemiological studies. Results: We obtained analytic formulas and quantitative estimates for the predictive and discriminative properties of the ideal prognostic tool. The area under the receiver operating characteristic curve (AUC) ranges between about 0.80 and 0.89 when prediction on any fall is made within a follow-up of one year. Predicting on multiple falls results in higher AUC. Conclusions: The discriminative ability of current validated prognostic tools for falls is sensibly lower than what the proposed ideal perfect tool achieves. A sensitivity analysis of the predictive and discriminative properties of the tool with respect to study settings and fall rate distribution identifies major factors that can account for the high heterogeneity of results observed in the literature.
The rapid aging of the world's population, along with an increase in the prevalence of chronic illnesses and obesity, requires adaption and modification of current healthcare models. One such approach involves telehealth applications, many of which are based on sensor technologies for unobtrusive monitoring. Recent technological advances, in particular, involving microelectromechnical systems, have resulted in miniaturized wearable devices that can be used for a range of applications. One of the leading areas for utilization of body-fixed sensors is the monitoring of human movement. An overview of common ambulatory sensors is presented, followed by a summary of the developments in this field, with an emphasis on the clinical applications of falls detection, falls risk assessment, and energy expenditure. The importance of these applications is considerable in light of the global demographic trends and the resultant rise in the occurrence of injurious falls and the decrease of physical activity. The potential of using such monitors in an unsupervised manner for community-dwelling individuals is immense, but entails an array of challenges with regards to design c onsiderations, implementation protocols, and signal analysis processes. Some limitations of the research to date and suggestions for future research are also discussed.