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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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... The study also included a 10-fold cross-validation that predicted the first fall within 12 months. Trying to go past the small population sample issue, Greene et al. [8] conducted a study over 5 years (2007 to 2012) to obtain enough data (616 older adults over 70 years old), combining cognitive impairment data with informations extracted from sensors, generating a more generalized model using logistic regression. The model obtained a sensitivity of 70% and specificity of 39%, which implied that the model mainly predicted that a given older adult would fall. ...
... However, even though they provide a broader data collection, sensors require a higher budget to develop accurate models. In the work of Noh et al [5], similar sample quantity to Greene et al [8] was used (620 entries with 210 fallers and 410 non fallers between the age of 55 and 97 years old). The variables of the study only included older adult's clinical information (disease and medications) but their analysis was much deeper, dividing attributes into different groups : MFS scale attributes (M1), similar covariance VOLUME 4, 2016 attributes (M2) and older adult's conditions (M3) that were progressively added. ...
... Looking at the results, we observed that a higher number of variables led to increased results. Previous fall risk assessments mentioned used few variables (5)(6)(7)(8)(9)(10)(11)(12) in each test. However some studies [2] carriedout a much more in depth analysis, looking at polypharmacy, time-related fall risk. ...
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Falls are a leading cause of unintentional trauma-related deaths worldwide, and a significant contributor to elderly dependence. To address this, the goal of this project was to predict recurrent falls in the older population using machine learning techniques, with the aim of reducing the number of falls and their consequences.To achieve this, a dataset obtained from Getafe University Hospital’s Geriatric Falls Unit was used (obtained from the Hospital’s Electronic Health Records). This extensive dataset was one the key strengths of our work. Feature extraction was performed through natural language processing, which recognized pre-defined patterns and helped build the profiles of the 304 older adults who composed the dataset. The proposed data system was comprised of four main blocks: the senior’s profile and environment, clinical information and tests carried out in the hospital, medications, and different diseases they presented. Using the extracted attributes and data from those 304 older adults, this project compared the performance of various machine learning techniques in their ability to classify older adults between future fallers and non fallers. Training different models and ensembles and comparing the results, we obtained that Bagging with Random Forest as base model is the best classifier, predicting accurately 75.8% of the data with 70.0% sensitivity and 80.5% specificity. Ultimately, this research project aimed at setting the first stone to a larger study that could help monitoring older adults and obtaining dynamic and automatic predictions of falls.
... Risk factors have also been identified at the level of task performance, such as walking, Timed-up-and-Go (TUG) or one limb stance [32,76,77]. Machine learning techniques have been used to derive fall risk prediction models, based on multiple candidate prognostic factors [21,23,30,33,65,69,82]. So far, candidate prognostic factors such as step length, step time, cadence and harmonic ratio have been assessed from accelerometer signals recorded in the lab (during gait or TUG) [23,30,65,69,79] or in daily life (10-20 s gait bouts) [21,32,82]. ...
... Machine learning techniques have been used to derive fall risk prediction models, based on multiple candidate prognostic factors [21,23,30,33,65,69,82]. So far, candidate prognostic factors such as step length, step time, cadence and harmonic ratio have been assessed from accelerometer signals recorded in the lab (during gait or TUG) [23,30,65,69,79] or in daily life (10-20 s gait bouts) [21,32,82]. However, the success rate for fall risk prediction varies depending on the locomotion task, with very disparate levels of reported sensitivity (55-100%), specificity (15-100%) and accuracy (62-100%) [29,54,65,79,81]. ...
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The objective of this systematic review is to identify motion analysis parameters measured during challenging walking tasks which can predict fall risk in the older population. Numerous studies have attempted to predict fall risk from the motion analysis of standing balance or steady walking. However, most falls do not occur during steady gait but occur due to challenging centre of mass displacements or environmental hazards resulting in slipping, tripping or falls on stairs. We conducted a systematic review of motion analysis parameters during stair climbing, perturbed walking and obstacle crossing, predictive of fall risk in healthy older adults. We searched the databases of Pubmed, Scopus and IEEEexplore. A total of 78 articles were included, of which 62 simply compared a group of younger to a group of older adults. Importantly, the differences found between younger and older adults did not match those found between older adults at higher and lower risk of falls. Two prospective and six retrospective fall history studies were included. The other eight studies compared two groups of older adults with higher or lower risk based on mental or physical performance, functional decline, unsteadiness complaints or task performance. A wide range of parameters were reported, including outcomes related to success, timing, foot and step, centre of mass, force plates, dynamic stability, joints and segments. Due to the large variety in parameter assessment methods, a meta-analysis was not possible. Despite the range of parameters assessed, only a few candidate prognostic factors could be identified: older adults with a retrospective fall history demonstrated a significant larger step length variability, larger step time variability, and prolonged anticipatory postural adjustments in obstacle crossing compared to older adults without a fall history. Older adults who fell during a tripping perturbation had a larger angular momentum than those who did not fall. Lastly, in an obstacle course, reduced gait flexibility (i.e., change in stepping pattern relative to unobstructed walking) was a prognostic factor for falling in daily life. We provided recommendations for future fall risk assessment in terms of study design. In conclusion, studies comparing older to younger adults cannot be used to explore relationships between fall risk and motion analysis parameters. Even when comparing two older adult populations, it is necessary to measure fall history to identify fall risk prognostic factors.
... A diverse collection of machine learning methods were employed to predict fall risk, including logistic regression [7,11,12], Support Vector Machine (SVM) [13][14][15], Random forest predictive model [16], neural networks [13], Elastic Net [17], ridge regression [17], lasso regression [9,18], Convolutional Neural Network (CNN) [19] and Hierarchical classification model [20]. In general, statistical features characterizing gait pattern of the elderly were extracted from the preprocessed sensor data. ...
... It was observed that SVM or Logistic regression using data from accelerometer and gyroscope on the low back and shanks [14,15] [7,11,12] were the commonly used method for fall risk prediction. Overall, SVM performed better than the compared machine learning methods. ...
... Building these models can either consist of finding the optimal set of parameters that best fit the data or of using similar instances of input to determine the output [27,30]. Up to now, fall-risk classification models for screening purposes based on the aforementioned risk factors are lacking [31,32]. Also, current screening tools show limited predictive validity to differentiate between low-and high-risk fallers [33,34]. ...
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Background Falls are a major problem associated with ageing. Yet, fall-risk classification models identifying older adults at risk are lacking. Current screening tools show limited predictive validity to differentiate between a low- and high-risk of falling. Objective This study aims at identifying risk factors associated with higher risk of falling by means of a quality-of-life questionnaire incorporating biological, behavioural, environmental and socio-economic factors. These insights can aid the development of a fall-risk classification algorithm identifying community-dwelling older adults at risk of falling. Methods The questionnaire was developed by the Belgian Ageing Studies research group of the Vrije Universiteit Brussel and administered to 82,580 older adults for a detailed analysis of risk factors linked to the fall incidence data. Based on previously known risk factors, 139 questions were selected from the questionnaire to include in this study. Included questions were encoded, missing values were dropped, and multicollinearity was assessed. A random forest classifier that learns to predict falls was trained to investigate the importance of each individual feature. Results Twenty-four questions were included in the classification-model. Based on the output of the model all factors were associated with the risk of falling of which two were biological risk factors, eight behavioural, 11 socioeconomic and three environmental risk factors. Each of these variables contributed between 4.5 and 6.5% to explaining the risk of falling. Conclusion The present study identified 24 fall risk factors using machine learning techniques to identify older adults at high risk of falling. Maintaining a mental, physical and socially active lifestyle, reducing vulnerability and feeling satisfied with the living situation contributes to reducing the risk of falling. Further research is warranted to establish an easy-to-use screening tool to be applied in daily practice.
... The search terms were: ("accelerometer" OR "wearable sensors" OR "gyroscope" OR "magnetometers") AND ("fall" OR "fall risk assessment") AND ("gait analysis" OR "signal processing" OR "feature extraction") AND ("aged" OR "geriatric" OR "gerontology" OR "senior" OR "elder" OR "old" OR "older adult") AND ("general" OR "community-dwelling"). These search terms were validated by reviewing the retrieval of representative articles [32,33]. ...
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Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.
... 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.
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In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.
Conference Paper
Gait behavior is considered an important indicator for the assessment of the general health status and provides a diagnostic observation for neuro-degenerative and musculo-skeletal diseases. Individual changes in gait behavior often reflect a deterioration of the current health status in a general sense and therefore provide significant information for clinicians and care-givers. In this work, we have used an unobtrusive sensor setup comprising three inertial measurement units (IMUs) located at the wrist, the chest and the thigh to obtain an objective measure of the human locomotion. We conducted a clinical trial in a movement laboratory environment to obtain a database of gait data at different walking speeds and conditions. The aging-simulation suit GERT was used to deteriorate the individual gait behavior during the experiments. Treadmill walking trials were used to train different classifiers to discriminate normal walking from GERT-affected walking patterns. Level-ground walking trials were used to validate the previously generated classifiers. A five-fold cross validation during the training process yielded overall F1-scores between 0.965 and 0.986. The validation tests showed promising results with prediction accuracies of more than 80%. Clinical relevance- The clinical relevance of this contri-bution can be considered two-fold. First we demonstrate the possibility of an unobtrusive monitoring system to iden-tify individual deterioration of gait behavior. Second we also validate the use of aging-simulation suits to introduce individual changes of gait patterns in healthy subjects to create a database of simulated yet realistic gait impairments associated with aging.
Fall is a major threat to stroke survivors with the problems of gait and balance disorders in the rehabilitation phase following severe consequences on quality of life and a heavy burden to their families. Many solutions have been proposed to assess fall risk for elders based on inertial sensor‐based signals, however, there still exists a great challenge of transferring them from elderly populations to the stroke‐survivors populations as gait disorder patterns are significant difference between elders and stroke survivors. In this study, we conduct a pilot study to collect inertial sensor‐based signals from stroke survivors when they performed the timed up and go test, and build an automatic fall risk assessment model with the architecture of Siamese network, with a merit of mitigating the problem of small sample size. Specifically, the proposed automatic fall risk assessment model consists of two parallel convolutional neural networks, each of which is composed of three convolutional layers, two max‐pooling layers, and three fully connected layers. To utilize the space relation among accelerator‐based and gyroscope‐based signals, two‐dimensional discrete wavelet transform extracts image‐like features, wavelet coefficients, from inertial sensor‐based signals as the input. Experimental results show that the proposed fall risk assessment model has achieved a promising results, which outperform cutting‐edge methods with a big margin. The proposed fall risk assessment model with low computational complexity and limited memory consuming can be deployed on an embedded system to provide fall risk assessment service for stroke survivors in point‐of‐care environments or community settings.
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The field of fall risk testing using wearable sensors is bustling with activity. In this Letter, the authors review publications which incorporated features extracted from sensor signals into statistical models intended to estimate fall risk or predict falls in older people. A review of these studies raises concerns that this body of literature is presenting over-optimistic results in light of small sample sizes, questionable modelling decisions and problematic validation methodologies (e.g. inherent problems with the overly-popular cross-validation technique, lack of external validation). There seem to be substantial issues in the feature selection process, whereby researchers select features before modelling begins based on their relation to the target, and either perform no validation or test the models on the same data used for their training. This, together with potential issues related to the large number of features and their correlations, inevitably leads to models with inflated accuracy that are unlikely to maintain their reported performance during everyday use in relevant populations. Indeed, the availability of rich sensor data and many analytical options provides intellectual and creative freedom for researchers, but should be treated with caution, and such pitfalls must be avoided if we desire to create generalisable prognostic tools of any clinical value.
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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.
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.