Setting the Criteria for the MATHOV + QAVS Tool

Qualitative and Quantitative Aspects for Wearable Fall Prediction

Mario S

aenz Espinoza and Miguel Velhote Correia

Faculty of Engineering, University of Porto, Porto, Portugal

INESC-TEC, Porto, Portugal

Keywords:

Fall Prediction, Qualitative Assessment, Quantitative Assessment, Wearable Technologies, Screening Tool.

Abstract:

For the ﬁrst time in history, the world shows a clear trend towards aging. This poses an intrinsic hazard for

the ever growing population, which becomes more vulnerable to common age-related illnesses and conditions.

One of the most serious risks elders face is falling, as it is responsible for countless admissions to geriatric care

institutions and thousands of deaths each year. In an effort to improve elders’ safety and quality of life many

groups have address the fall prevention issue, coming to several different results as of what variables are the

most important to consider in a fall prediction tool. These variables range from qualitative aspects (history of

falls, dementia, use of medication, etc.) to quantitative ones (total walked distance per day, walking cadence,

center of mass, etc.), but none of them per se seems to deliver a deﬁnite and complete answer to the problem

at hand. The paper herein aims to present a new hybrid approach, which combines both the highest co-related

qualitative and quantitative biovariables in a single tool: the MATHOV + QAVS, which is proposed as a new

fall assessment screening tool and eventually as baseline criteria for a complete elder fall prediction system.

1 INTRODUCTION

The World Health Organization (WHO) deﬁnes a fall

as “an event which results in a person coming to rest

inadvertently on the ground or ﬂoor or other lower

level” (World Health Organization, 2012). Each year

37.3 million falls are severe enough to require med-

ical attention (424000 of them fatal) and according

to Peel, falls are the leading cause of both fatal and

non-fatal unintentional injuries in people older than

65 years —accounting for 40% of all injury-related

deaths worldwide (Peel, 2011).

Falling also implies a huge ﬁnancial burden in

health care services all over the world. Based on Dol-

lar purchasing power parities, Heinreich et al. deter-

mined in his study that the mean costs per fall vic-

tim, per fall and per fall-related hospitalization ranged

from $2044 $ to $25955 $; $1059$ to $10 913$ and

$5654$ to $42840$ respectively (Heinrich et al.,

2010). This is more impressive in terms of Gross

Domestic Product (GDP): within the European Union

the amount sums up to 0.85%, whereas in a high-cost

health care model —such as in the United States—,

the total cost of medical care for elderly people has

risen from 0.8% of the GDP in 1975 to 2.7% in 2008

—more than half of the US 2013 military budget (Sir-

acuse et al., 2012).

Falling has become an actuality topic because of

the worldwide trend towards aging. It is estimated

that there will be two thousand million persons over

60 years in the year 2050, and by that time the oldest

and most vulnerable segment of population (aged 80

and over) are expected to represent 20% of the total

elder population. If preventive measures are not taken

in an immediate future, the number of injuries caused

by falls is projected to be 100% higher in less than 20

years (World Health Organization, 2007). As stated

by the WHO “Population aging is a triumph of hu-

manity, but also a challenge to society”; unfortunately

this statement forgets to remark the fact that the aging

issue is not only a social and economical challenge,

but also a technological one.

Predicting falls in order to prevent them consti-

tutes a serious question for both the health and en-

gineering ﬁelds. The former has long tried to eval-

uate qualitatively factors, aiming to ﬁnd characteris-

tics that might be precursors to falls and actions or

conditions that inﬂuence the likeliness of falling (De-

mura et al., 2012; Grundstrom et al., 2012; Neumann

et al., 2013); whereas engineers have just recently

picked up the task: their approach is focused on ac-

quiring measurable data (Verghese et al., 2009; Liu

Sáenz Espinoza M. and Velhote Correia M..

Setting the Criteria for the MATHOV + QAVS Tool - Qualitative and Quantitative Aspects for Wearable Fall Prediction.

DOI: 10.5220/0004914200690075

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2014), pages 69-75

ISBN: 978-989-758-011-6

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

et al., 2011; Karlsson et al., 2012) for further quanti-

tative analysis using state vector machines (Lai et al.,

2008), neural and dynamic Bayesian networks (Gi-

ansanti et al., 2008a; Cuaya et al., 2013) and other

probabilistic/statistic models.

Fall prediction is very complicated in nature, as it

tries to answer what physically-acquirable data is of

interest, what quantitative bio-variables can be related

to falling and ﬁnally which tests are to be conducted to

actually gather this information. Moreover, all these

aspects only represent an entry level, since the subse-

quent challenge is what type of algorithms could be

used to process this information. The work presented

herein addresses the issues regarding the selection of

these bio-variables and proposes a set of them in the

form of the MATHOV + QAVS tool, which is part of

the ﬁrst stage of an on-going project for a complete

wearable fall prediction solution (Espinoza, 2013).

The tool is based on an hybrid approach, i.e., uses

both qualitative and quantitative methods. Their se-

lection has been through an extensive bibliographical

research and uses only bio-variables that have been

tested and validated before by large studies with pa-

tients. It has been developed in an effort to deliver a

promptly and modern solution for improving elders’

health security and quality of life issue, either by be-

coming a standard screen tool for fall risk or by setting

the criteria for future fall prediction algorithms.

2 QUALITATIVE &

QUANTITATIVE VARIABLES

As previously mentioned, there is still an on-going de-

bate whether a qualitative or quantitative approach is

the best way to engage fall prediction. Deﬁning the

criteria to separate fallers from non-fallers has proven

to be a difﬁcult task, and many researchers have tried

—and successfully managed— to come up with vi-

able results. However, a completely hybrid-based so-

lution using wearable devices has not yet been devel-

oped (Espinoza, 2013). This new approach holds con-

siderable promise, as both the qualitative and quanti-

tative aspects that the MATHOV+QAVS tool takes in

consideration have already been proven, tested and

validated by different experts in different countries

with a considerable number of patients.

2.1 Qualitative Aspects

The tool’s name, MATHOV, is composed by the ﬁrst

letters of the ﬁve qualitative variables that have been

identiﬁed as the most common in fall assessment

studies, and most of the authors seem to agree they

have the highest co-relation with falls in the elderly.

These variables are Medication, Age, Toileting, His-

tory of falls and Vision impairment.

Of these ﬁve variables, history of falls has been re-

currently noted as the closest variable related to a near

future (usually a 12 months period) fall. This should

not come as a surprise, since a previous fall means

that the patient already presents some level of inabil-

ity to sustain balance, and it is likely the patient also

suffers from an injury caused by any of the previous

falls. These previous falls and lesions can limit the

patient’s mobility, reduce their conﬁdence and nega-

tively contribute to their overall well-being.

Many of the medication drugs commonly pre-

scribed to elders —either psychotropics, painkillers

or blood pressure control drugs, just to name a few—

include side effects such as dizziness and drowsiness,

which naturally increase the risk of falling, specially

under an unsupervised scenario.

The Toileting factor also comes from somewhat

the same background as medication: the elders’ fre-

quently prescribed drugs (specially as with the case

of diuretics for treating blood pressure) usually come

hand in hand with frequent toileting. There also

seems to be a general consensus among the stud-

ied authors that frequent toileting is an indicator of a

muscular detriment to some degree, which could also

translate into lack of strength in the lower limbs and

thus incrementing the possibility of falling. Regard-

less if there is a overlap relationship between med-

ication and toileting, these two variables have been

constantly pointed out in the scientiﬁc literature as

highly inﬂuential factors prior to a fall —according

to both statistical approaches and assessment by elder

care nurses (Sherrington et al., 2010).

Vision impairment also plays a major role, as seen

by the amount of times it was repeatedly reported by

the studied authors. Limited vision is a very common

condition amongst elders and is natural to assume

that any type of limitation or impairment for walk-

ing —such as by needing a walking aid, low muscular

strength or, as in this case, poor visibility— increases

the risk of falling. This can be even worse if the pa-

tient walks around without his vision aid (glasses), as

this could represent a serious hazard to bump and fall

—specially in high-risk environments with lots of fur-

niture, carpets, steps, uneven ﬂoors and other com-

mon obstacles—, with possible fatal consequences.

Although the Age variable was not as recurrent

as others on Table 1, it is still considered a serious

aggravate, specially by health associations such as

the WHO (World Health Organization, 2007) and the

Pennsylvania Patient Safety Advisory (Feil and Gard-

ner, 2012). The later found over more than 400 stud-

BIOSIGNALS2014-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

Table 1: Qualitative Aspects for Fall Assessmet (by author).

Author Subject Count Medication Age Toileting Fall History Vision

(Perry, 1982) 1780 X X

(Oliver et al., 1997) 1780 X X X

(Lord et al., 2005) 684 X X

(Neyens et al., 2006) Bibliographic research X X X

(Gama and G

omez, 2008) Bibliographic research X X X

(Marschollek et al., 2009) 119 X X

(Russell et al., 2009) 344 X

(Sherrington et al., 2010) 533 X X

(Muir et al., 2010) 117 X

(Deandrea et al., 2010) Bibliographic research X X X

(Bongue et al., 2011) 1759 X X

(Fong et al., 2011) 554 X

(Grundstrom et al., 2012) 120923 X X

(Demura et al., 2012) 1122 X X X X X

(Feil and Gardner, 2012) Bibliographic research X X X X

(Aizen and Zlotver, 2013) 1013 X

(Neumann et al., 2013) 4735 X

ies a high co-relation between falling and advanced

age, and the importance of using this information as

a general risk stratiﬁcation method: the older the pa-

tient, the higher the risk of falling.

Perhaps the most important characteristic that all

the MATHOV variables share is the ease of acquisi-

tion. Getting the information can be carried out in

very short time, does not require any special instru-

ments or technicians and a simple questionnaire to

the patient, a close family member, friend or care

provider will sufﬁce.

2.2 Quantitative Aspects

The second part of the tool’s name, QAVS, stands for

Quantitative Assessment Variable Selection. As well

as with MATHOV, it was developed after a state of the

art research on fall prediction (Espinoza, 2013).

It is important to note that what is shown on Ta-

ble 2 are actually the tests that return the variables of

interest. For example, the 6 m walking test (SMWT) is

used to measure the time it takes the patient to walk

just over 6m on a straight line. A non-faller should

take less than 5.4 seconds, whereas a recurrent faller

(2 or more falls a year) takes on average 5.9 s or more

to complete the SMWT (Karlsson et al., 2012).

When the SMWT is accompanied by the num-

ber of steps for the 6 m walk, new classiﬁcation cri-

teria can be set: recurrent fallers take more than 9.7

steps to complete the SMWT, and since the informa-

tion of time and number of steps is available, cadence

can also be obtained and compared with other stud-

ies (Menz et al., 2003).

The timed up and go test (TUGT) consists in mea-

suring the time it takes for the patient to get up from

a rest position (usually sitting) and walking a small

straight-forward distance (often 3m). As its name

suggests, the test is timed and then the threshold is set

accordingly. Contrary of SMWT, there is no statis-

tical information available in the literature for TUGT

based on large scale experiments, as the one by Karls-

son et al. Furthermore, each of the authors mentioned

on Table 2 that have experimented with TUGT have

used their own variations, almost turning TUGT into

a non-standard test. Nevertheless, the getting-up-and-

walking essence of TUGT has been extensively used

along the years, and can be easily adapted to unsuper-

vised tests using wearable technologies, thus its in-

corporation into the QAVS.

Other quantitative factors for fall risk, such as to-

tal walked distance per day and energy expenditure

have been recently and extensively evaluated by many

authors (Marschollek et al., 2008; Narayanan et al.,

2010). This features are particularly useful when

dealing with elders’ own perception on mobility: it

is common to see elders reporting their activities of

daily living as much more intense and longer than

they actually are. Total walked distance and energy

expenditure can provide a true value of the patient’s

activity, and could also be used to extended beneﬁts,

such as calculating his right caloric intake according

to his lifestyle and activities.

Body mass index (BMI) also seems to play a major

role as a variable of interest in fall prediction. Morbid

obesity is usually related to reduced life expectancy

in part due to several illnesses and conditions asso-

ciated to it —such as diabetes, hypertension, choles-

terol, etc.—, as well as high risk of falling due to the

limited mobility obese patients present. This situation

seems to aggravate as it usually creates a vicious cir-

cle: limited mobility tends to discourage patients to

practice exercise, which further reduces their mobil-

SettingtheCriteriafortheMATHOV+QAVSTool-QualitativeandQuantitativeAspectsforWearableFallPrediction

Table 2: Quantitative Aspects for Data Gathering (by author).

Author Subject Count TUGT SMWT Steps for 6m

(Menz et al., 2003) 100 X X

(Tiedemann et al., 2008) 362 X

(Marschollek et al., 2008) 110 X X X

(Gietzelt et al., 2009) 241 X

(Verghese et al., 2009) 597 X

(Narayanan et al., 2010) 68 X

(Greene et al., 2010) 349 X

(Liu et al., 2011) 68 X

(Shimada et al., 2011) 213 X X

(Karlsson et al., 2012) 10998 X X

ity, directly affects on the patients’ strength and health

and increases their risk of falling (Rosengren et al.,

2011; Grundstrom et al., 2012).

3 DISCUSSION

After an extensive bibliographical research regarding

both the qualitative and quantitative aspects related to

falling in the elderly, it seems the conclusion still re-

mains the same as with many other studies: there is no

silver bullet to tackle the problem (Espinoza, 2013).

No speciﬁc factor or test can deliver a complete de-

cisive variable or threshold to be used systematically

with the elder population, suggesting that a combi-

nation of factors —essence of the MATHOV + QAVS

tool— could be the best approach.

One of the latest trends in quantitative fall as-

sessment has been the use of wearable technologies

(micro-electromechanics) which allow constant mon-

itoring of the patient in an unobtrusive way (Shany

et al., 2012a; Shany et al., 2012b). Furthermore,

if used accordingly to the proposed methods of this

research project, wearable technologies could also

avoid the laboratory settings needed for data acqui-

sition, reduce the white coat effect, eliminate the ne-

cessity of a supervisor and at the same time give com-

plete freedom and autonomy to the patients to do their

activities of daily living.

The MATHOV + QAVS tool merges what seem

to be the most extensively studied qualitative fac-

tors with quantitative variables easily acquirable with

micro-electromechanics’ technology (MEMs), which

are not also increasingly smaller over time and hold

a great amount of autonomy —thus, ideal for wear-

able projects—, but are also capable of acquiring bio-

variables in an secure, discrete, prolonged and non

invasive manner.

Table 3 summarizes the MATHOV + QAVS tool’s

variables. The qualitative variables are extremely

easy to assess by simply asking the patient or a

close family member. Of course some others —as

the amount of medication the patient is taking and

the level of visual impairment— can be assessed by

other means, i.e., by the patient’s physician or medi-

cal records. This straightforward form of evaluation

is ideal for low-tech and low-cost scenarios —such

as with developing countries— or simply for easiness

and practicality.

As an example, Figure 1 shows a possible device

for biodata gathering for the MATHOV + QAVS tool:

Kinematix’s WalkinSense is an in-shoe sensor array

located under the shoe’s insole capable of acquiring

plantar pressure, walking acceleration, speed, step

length, number of steps, tibial angles and their respec-

tive times (Kinematix, 2013). Furthermore, due to the

plantar pressure measurements, it is possible to deter-

mine if the patient is sitting (low plantar pressure) or

standing (high plantar pressure), which is extremely

convenient for the timed up and go test (TUGT).

Figure 1: WalkinSense: Example of a device for wearable

gait biodata acquisition.

As with the qualitative parameters of the tool,

quantitative tests are easy to perform, and the data

gathered can be used to calculate several indirect pa-

rameters, such as cadence, by using the number of

steps for 6m and dividing them by the time it takes

to perform the SMWT. Gait speed can also be calcu-

lated —either by integrating the acceleration obtained

with the wearable accelerometers or by indirect calcu-

lation using the distance and time—, which in return

can also be used to obtain the total energy expenditure

—using the patient’s weight from the data obtained

for the body mass index.

Other quantitative parameters known in the liter-

BIOSIGNALS2014-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

Table 3: MATHOV + QAVS Tool’s Criteria.

Qualitative Quantitative

Medication Time: Timed up and go test (TUGT)

Age Time: Six meter walk test (SMWT)

Frequent toileting Number of steps for 6m

History of falls Total daily walked distance

Vision impairment Body mass index

Walking cadence

Daily energy expenditure

ature, such as center of mass (McGrath et al., 2012)

and gait sway (Stalenhoef et al., 2002; Lord et al.,

2005; Giansanti et al., 2008b; Marschollek et al.,

2008) have been studied and have returned interest-

ing results, but pose a technological challenge to ac-

quire with wearable and autonomous devices: either

due to battery life constrains or the supervised or con-

trolled laboratory environment required. Since the

tool’s scope is focused on an approach as unstruc-

tured and unsupervised as possible, only tests that can

be performed completely unmonitored, using micro-

electromechanics and without any complicated tests

—like previously design walking courses or speciﬁc

researcher-designated trials— have been taken into

consideration while designing the tool.

The MATHOV + QAVS tool could also be used by

clinicians to constantly evaluate a patient’s risk of fall

and assess if the patient requires preventive or correc-

tive measures —namely, physical therapy—, yielding

the opportunity for timely and oriented interventions.

Similarly, it could also be used to prescribe walking

aids (canes, walkers or wheelchairs) if the falling risk

is too high and prophylactic measures should be con-

sidered instead.

Some recent studies have shown that what causes

most falls among elders is the lack or proprioception

and wrong perceptions of their own mobility capabil-

ities (Lafargue et al., 2013; Robinovitch et al., 2013).

Both proprioception and self-consciousness of their

moving limitations can be boosted by opportunely

prescribing the patients with exercise, which would

also delivers other beneﬁts like greater energy con-

sumption, a more active living/aging, reduced fatal-

ities due to falls and reduce costs to both healthcare

systems and patients.

In summary, the tool’s variables were selected due

to their gathering simplicity and close relationship

with falling, keeping in mind the patient’s autonomy,

privacy and user-friendliness. The tool intends to con-

ciliate the autonomous, ubiquitous and unobtrusive

nature of wearable devices with the unsupervised and

unstructured testing methods for data acquisition.

4 CONCLUSIONS

Perhaps what stands as the most interesting ques-

tion is why hybrid (qualitative with quantitative) ap-

proaches had not been tried in the past. The answer

could reside with the fact that MEM technology has

just recently come into place, as well as the emerg-

ing ﬁeld of biomedical engineering, which tries to ad-

dress issues converging both the medical an engineer-

ing point of view. Technology limitation and absence

of an specialized research ﬁeld may have delayed the

launch of a hybrid approach, but it is undoubtedly that

these mixed tools will likely replace the one-sided

approaches and, consequently, increasingly improve

over time.

Furthermore, a great amount of conﬁdence has

been deposited in the MATHOV + QAVS tool, as its

eclectic nature was speciﬁcally designed to meet the

expectations of clinicians asking for a fall risk screen-

ing tool as well as engineers looking for data to feed

their fall prediction algorithms. The success of the

tool can only be measured after extensive testing, but

due to its easy implementation, low cost technology

and intrinsic unobtrusive characteristics for the pa-

tient its likely and encouraging to anticipate a substan-

tial use of the tool by fall prediction research groups.

Although this tool has not yet been validated, fu-

ture research and trials will provide the necessary data

to conclude whether if it is of use or not. However,

taken into consideration the results obtained by other

groups with similar qualitative variables, it is tempt-

ing to suggest the expected results are at least as good

as the ones obtained by such authors, since the pre-

sented tool herein should be improved by its quantita-

tive component.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the support of

QREN project #13850: NeWalk/COMPETE

SettingtheCriteriafortheMATHOV+QAVSTool-QualitativeandQuantitativeAspectsforWearableFallPrediction

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