Stack, E., King, R., Janko, B., Burnett, M., Hammersley , N., Agarwal, V., ... Ashburn, AM

12 Pages·2017·2.43 MB·English

Stack, E., King, R., Janko, B., Burnett, M., Hammersley , N., Agarwal,

V., Hannuna, S. L., Burrows, A. B., & Ashburn, A. M. (2016). Could In-
home Sensors Surpass Human Observation of People at High Risk of
Falling? An Ethnographic Study. BioMed Research International,
2016, [3703745]. https://doi.org/10.1155/2016/3703745
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BioMed Research International
Volume 2016, Article ID 3703745, 10 pages
http://dx.doi.org/10.1155/2016/3703745
Research Article
Could In-Home Sensors Surpass Human Observation of People
with Parkinson’s at High Risk of Falling? An Ethnographic Study
EmmaStack,1,2RachelKing,3BalazsJanko,3MalcolmBurnett,1NicolaHammersley,4
VeenaAgarwal,1SionHannuna,5AlisonBurrows,5andAnnAshburn1
1FacultyofHealthSciences,UniversityofSouthampton,SouthamptonSO171BJ,UK
2NIHRCLAHRCWessex,UniversityofSouthampton,SouthamptonSO171BJ,UK
3SchoolofSystemsEngineering,UniversityofReading,ReadingRG15AQ,UK
4FacultyofMedicine,UniversityofSouthampton,SouthamptonSO171BJ,UK
5FacultyofEngineering,UniversityofBristol,BristolBS81TH,UK
CorrespondenceshouldbeaddressedtoEmmaStack;[email protected]
Received25September2015;Revised9December2015;Accepted11January2016
AcademicEditor:KamiarAminian
Copyright©2016EmmaStacketal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
Self-reportunderpinsourunderstandingoffallsamongpeoplewithParkinson’s(PwP)astheylargelyhappenunwitnessedathome.
Inthisqualitativestudy,weusedanethnographicapproachtoinvestigatewhichin-homesensors,inwhichlocations,couldgather
usefuldataaboutfallrisk.Oversixweeks,weobservedfiveindependentlymobilePwPathighriskoffalling,athome.Wemade
fieldnotesaboutfalls(prioreventsandconcerns)andrecordedmovementwithvideo,Kinect,andwearablesensors.Thethree
womenandtwomen(aged71to79years)havingmoderateorsevereParkinson’sweredependentonothersandhighlysedentary.
Wemostcommonlynotedbalanceprotection,loss,andrestorationduringchairtransfers,walksacrossopenspacesandthrough
gaps,turns,stepsupanddown,andtasksinstanding(allevidentwalkingbetweenchairandstairs,e.g.).Ourunobtrusivesensors
wereacceptabletoparticipants:theycoulddetectinstabilityduringeverydayactivityathomeandpotentiallyguideintervention.
Monitoringtheroutebetweenchairandstairsislikelytogiveinformationwithoutinvadingtheprivacyofpeopleathighriskof
falling,withverylimitedmobility,whospendmostofthedayintheirsittingrooms.
1.Background understandingoftheriskfactorsandcircumstancesinwhich
peopletendtofall.Weneedtounderstandmoreaboutnear-
People at high risk of falling spend most of their time at misses (“occasions on which individuals felt that they were
home, and, like many other manifestations of illness, falls going to fall but did not actually do so” [10]), falls (“events
happenpredominantlyunwitnessedathome.Therefore,our thatresultsinapersoncomingtorestunintentionallyonthe
understanding of what happens before, during, and after ground or another lower level, not as the result of a major
a fall is largely dependent on self-report (predominantly intrinsiceventoroverwhelminghazard”[11]),andthefearof
through interviews, diaries, and surveys). As Weis et al. [1] fallingtomanagetheircauses,consequences,andcosts[8,10].
stated,“Unfortunately,self-reportis...thegold-standardfor The quality and quantity of self-report depend on the
characterizing and quantifying fall frequency” but authors interviewee’s and interviewer’s motivations and abilities.
discusstheaccuracyofpatientrecallasalimitationoftheir Transientriskfactors(such asdizziness)contributeto falls,
workacrossarangeofconditions[2–7]. fear of falling, morbidity, and dependence [9] but fleeting
Costingoverm2billionperyear,fallsareanNHSpriority, signs of impending instability are difficult to describe and
with 30% of people aged 65 or older falling each year, and evaluate(andthereforemanage),unliketheobvioussignsof
50%ofthoseaged80orolder[8].“Unlessconcertedaction landing (injuries and environmental disruption). Someone
is taken,” falls are likely to become increasingly prevalent whohasfallenmaynotknowwhathappened,letalonewhy,
and costly as the population ages [9], despite our current assomeonefalls,bydefinition“unintentionally,”whiletheir
2 BioMedResearchInternational
attentioniselsewhere.Evenclearinsightsmayfadewithout acceptanceoftechnologytheyconsidered“generallyuseful”
immediate reporting or documentation. Some people may beforeinstallationdecreasedwithexperience.Toavoidsome
not want to report every (or any) event. To summarise, the oftheseunwantedoutcomes,weproposedaqualitativestudy
drawbacks of relying on self-reporting to understand fall- toinitiateourprogrammeofresearch.
eventsinclude Weplannedtoinvestigatethehealthcareapplicationsof
a sensor platform in the home (predominantly with elderly
(i)overrelianceonasinglewitnesswhoseattentionwas
people, as they make the greatest use of health services).
elsewhereduringanunexpectedevent,
People with Parkinson’s (PwP) are a very high-risk group
(ii)vague/transient warning signs that gave insufficient for falling at home; near-misses may herald the onset of
insightatthetimetopreventafall(orsoftenaland- significant postural instability and predict future falls [10,
ing)probablyleavingminimalevidenceafterwards, 16, 17]. If sensors could alert them to increasingly frequent
near-missesathome,individualswiththemostpotentialto
(iii)thefactthatifpeoplewanttodocumentand/orreport
benefit from rehabilitation [18] could instigate intervention
events, they need an opportunity to record or recall
beforeinjuriousfallsbecamelikely.Webeganourprogramme
thedetailsbeforeinsightsdiminish.
withanethnographicstudyinvolvingasmallgroupofpeople
User-friendly, minimally invasive video-based or wearable withsignificanthealthcareneeds.“Home-basedtechnologies
sensors in the home could tackle many of these issues. researchwitholderadultsneedstobeflexibleandpacedto
They could, for example, record deviations from normal fit their lives” [19], so we sought to gain insight into living
gait that a human observer might not notice, let alone athighriskoffalling,attitudestoin-homesensors,andthe
document.Sensorscouldbe“avirtualwitness,”recordingthe practicalitiesoftestingsensorsunderreal-lifeconditions.We
circumstancesthatprecede,surround,andfollowfall-events. aimedtoobservepeoplewithmoderateorsevereParkinson’s
By recording a baseline, deviations, and fall-events, sensors in their own homes to identify what types of sensors, and
could enhance the management and self-management of inwhichlocations,werecapableofmonitoringmobilityand
fall risk and inform clinicians about instability associated balanceinawaythatwouldbeacceptabletoparticipantsand
withfleetingsymptomsthataredifficulttorecount.Beyond meettheresearchers’needs.
theindividual/clinicalapplication,informationfromsensors
(that individuals “control” and are willing to share) could Objectives.Theobjectiveswereasfollows:
changeourcurrentthinkingabouttheevolutionoffallrisk
(1)Toobservepeopleathighriskoffallingmovingfreely
over time, the circumstances of falling, and behavioural
at home, noting, and recording (with video, Kinect,
changeafterfalling.
andwearabledevices):
For our understanding of falls to improve, we need to
observe many real events. Some fall-detection algorithms
(a)movementpatterns(e.g.,habitualactivities),
probably perform so poorly in the field, for example, 85
falsealarmsperday[12],becauseresearchersdevelopedthem (b)behaviours (likely to increase or decrease fall
from data collected on simulations. Volunteers throwing risk),
themselves to the ground (e.g., as Bourke et al. asked them (c)locations and actions associated with (historic
to do [13]) do not land unexpectedly. It would not require orobserved)fallsandnear-misses(collectively
many in-home sensors to capture more “real” falls than “fall-events”[10])andfearoffalling.
researchershavewitnessedtodate,generatingdatathatcould
refine detection algorithms. Beyond simply capturing the (2)Toobserveparticipantsrepeatedlydemonstratingone
mechanicsofanevent,sensorscouldhelpustounderstand habitual activity they associate with a particularly
what happens beforehand. Cameras showed, for example, highfallrisk(e.g.,descendingsteps),recordingfrom
that more falls in care homes occurred from standing and multiplecamerapositions.
while transferring and fewer during walking than reports
suggested[14]. 2.Methods
Understanding what happens before balance is lost has
apreventivevalue.Understandingwhathappensafterwards WithEthicsCommitteeapproval,wedistributedinformation
has value in preventing the deleterious consequences of packstopeoplewithParkinson’s(viapresentationstosupport
immobility and fear, such as isolation and dependence. But groups),aimingtorecruitthefirstfivevolunteerswhocould
identifyingwhatpeopleatriskoffallingdoathomeandhow
toextractusefuldataundertheless-than-idealconditionsof (1)walk at home without the assistance of another
thedomesticsettingarechallenges.Decidingwheretoposi- person,
tion the minimum number of sensors capable of capturing (2)describemultiplerecentfall-eventsthatcausedthem
useful data, unobtrusively, in appropriate locations requires tofearfalling(orfallingagain).
consideration. When Feldwieser et al. [15] trialled a fall-
detectionsysteminelderlypeople’shomes,15fallsoccurred We visited potential participants to secure their written in-
(over 1000-plus measurement days) but none within range formed consent and the consent of anyone else likely to be
of the Kinect sensor installed; algorithms falsely detected video-recorded(e.g.,aspouseathomewhilewewererecord-
multiplefallseveryday(4592intotal);andtheparticipants’ ing).
BioMedResearchInternational 3
Visit 1 (≈1 hour)
To describe sample To generate ground floor “Fall Map” To plan Visits 2–6
Data collected locations of
Data collected Data collected
(a) Recalled fall-events
(i) Participant age, gender, and (i) Usual daytime routine
(b) Ongoing fear of falling
living arrangements
(c) Grab rails/other aids
(ii) Parkinson’s duration
(years since diagnosis)
(iii) Parkinson’s severity Visit 2 (≈2 hours)
(Hoehn and Yahr grade [20])
To observe/engage in usual daytime routine
(iv) Parkinson’s management
Data collected-field notes on
(i) participants’ movements/behaviours
Analysis (e.g., activities, routes, effort, and fall risk)
(1) Summarise participant history/routines (ii) participants’ thoughts on
(2) Summarise field notes (a) in-home monitoring
(3) Identify fall risk in video records (b) sensor positions in Visits 3–6
(protecting, losing, and restoring balance) (iii) researchers’ perceptions about
(4) Combine and reflect on1–3 (a) participant’s fall risk
(b) acceptability of in-home monitoring
Visit 6 (<1 hour) Visits 3–5 (≈1 hour/visit)
To record one balance-challenging activity As Visit 2, and to introduce sensors
Data collected Data collected-field notes (as Visit 2) and
(i) Participant demonstrating activity (i) Kinect camera (throughout visit):
(a) Three demonstrations (a) in room in which participant is based
(b) Wearables record throughout (b) directed at participant’s chair
(c) Cameras in three positions (from front, back, and side) (c) capturing widest view possible
(ii) wearable sensors×5(throughout visit)
(iii) video camera (portable, digital):
(a) recording everywhere but bathroom
(transfers, walking, and standing tasks)
(b) recording when participant is active
Figure1:Summaryofthedatacollectionandanalysisprocess.
2.1. Data Collection and Analysis. Between September 2014
and February 2015, we saw participants six times (approxi-
mately weekly), engaging in their usual morning and after-
noonroutinesathome(seeFigure1).FromVisit3,wesup-
plementedreal-timeobservationwithvideo/audiorecording
whilewewerepresent.
Although we explored the use of Kinect and wearable
sensorswitheachparticipant,thispaperreportsononlythe
qualitativedata(derivedfromfieldnotesandvideoreview).
Participantsworefiveself-containedwatch-sizeddevicesthat Figure2:Prototypeinertialmeasurementslogger(asworn(×5)by
were under development fora larger research collaboration participants).
(ofwhichthisstudyformspart)andnotcommerciallyavail-
able (see Figure2). Each contained a triaxial accelerometer
andtriaxialgyroscopetomeasureaccelerationsandangular for later analysis. We charged them fully before use and
velocities.Thedevicesranonbatterypowerthroughoutdata secured them around the wrists and ankles and over the
collectionandloggeddatathatwedownloadedtoacomputer lumbarspineusingVelcrostraps.
4 BioMedResearchInternational
Visit 6 focused on an activity frequently challenging
WC
participants’ balance (identified from their history and our Hall
observations).Aphysiotherapistannotatedthevideos,iden-
tifying when and how participants (1) protected, (2) lost,
and(3)restoredtheirbalance(e.g.,usedsupport,swayedor
stumbled,andmadesavingreactions). V
T
3.Results
Figure3:ExampleofFallMap.Solidarrowshowsaroutethrough
thekitchen-diningroomthatfrequentlychallengesoneparticipant;
3.1. Sample. One participant withdrew after consenting,
circlesmarksignificantpreviousfall-events.Astepbetweenwhat
concerned about fitting data collection into a busy family were previously two rooms is less hazardous since the addition
and working life. Five completed the study, including two of grab rails on both sides. However, the participant relies on a
fallingatleastmonthlyandonewithanimplanteddeepbrain heavychairtoprovideadditionalsupport.Trianglesmarkcamera
stimulator.Ourparticipantswereallretired,livingaloneor positions.
with a spouse, and largely housebound without assistance.
We summarise their characteristics in Table1. Three had
significant healthcare needs besides Parkinson’s, including
neurologicalconditions,recurrentinfections,skeletaldefor-
mities,andchronicpainandonehadaspousewithsignificant
needs.Allfiveparticipants
(i)were under the care of Parkinson’s specialists and
multidisciplinary teams and followed their regular
regime of “anti-Parkinson” medication throughout
thestudy,soweobservedthematpeakdose(moving
mostfreely)andastheeffectsworeoff,
(ii)typically spent the day in a favourite chair in their Figure 4: Examples of people at high risk of falling “furniture
creeping.”Withorwithoutwalkingaids,participantsreliedonthe
receptionrooms(watchingtelevisionorusingacom-
supportoffurnituretomovesafelyacrossroomsandoftenappeared
puter,e.g.)and/orsittinginthekitchen,
vulnerableinopenspace.
(iii)describedfrequentnear-missesandafearoffalling,
atbestmakingthem“cautious,”atworst“inhibitory.”
(iv)Ascendinganddescendingstairs.
Althoughinmostfallsathometheparticipantshadnotsus-
tainedseriousinjuriesandhadrecoveredtotheirfeetwithout (v)Negotiatingstepsbetweenrooms.
anyone’sassistance,thereweretraumaticexceptions:onehad
(vi)Crossingopenspacesinlargerooms.
fallenbackwardsfromthetoptothebottomoftheirstaircase;
another had fractured a femur and waited an hour, alone
on the ground, for assistance. We highlight the locations 3.2.2. Participant’s Thoughts. Every participant agreed to
associatedwithoneparticipant’sfall-eventsandfearoffalling wearfivesensorsthroughouteverysession;noonesaidthey
onaFallMapinFigure3. were cumbersome; some remarked they had forgotten that
theywerewearingthem.Participantsaskedaboutthesensors’
functionsandpotentialuses.One,whohadalwaysdetested
3.2.ObservationintheHome:FieldNotes
“being watched” (e.g., by a supervisor at work), felt some
people might not welcome long-term video-surveillance at
3.2.1.Participant’sBehaviour. Wespentapproximatelyseven
home. Another felt that, as carers rarely went out, sending
hours with each participant, during which time they were
themalertsabouteveryfallmightmakethemfeeltheyhad
alllargelysedentary(stayingdownstairsthroughouttheday,
tohurryback,whenthatwasrarelywhatthefallerneededor
mostly in one favourite chair). We positioned the Kinect
wanted(asfallerskeepsomefallstothemselves).
inthesittingroom,facingtheparticipants’favouritechairs,
except once when we focused on the computer station in
3.2.3.Researcher’sPerceptions. Wefeltintrusivestaying“too
thediningroom.Theypredominantlyusedthefurnitureor
long,” aware that people had saved tasks for when we
wallsforsupport(ratherthanmobilityaidsorpurposelyfitted
were present, giving us “something to film.” We restricted
rails,seeFigure4)astheyshowedusaroundtheirhomesand
mostvisitsto90minutes,awarethatparticipantsandother
gardensanddemonstratedthefollowingactivities:
residentsmightfeeluncomfortablesayingtheyweretiredor
(i)Walkingbetweenrooms(e.g.,tocollectsomethingor neededprivacy.Everyparticipantwasathighriskoffalling
torelocate). (onespousehaddocumentedapproximately30fallsover18
months)andwefeltanxiouswhentheylosttheirbalance(e.g.,
(ii)Preparingdrinksorcooking.
“oops, nearly!”) or mentioned previous events (e.g., “this is
(iii)Sorting,washing,andhangingoutclothes. whereIhadmylastreallybadfall”).Noonefellwhenwewere
BioMedResearchInternational 5
gle in min
ID-673,female 8III,moderate Alone,familynearbyNeedshelptoleave(sinstorey)house UsesperchingstoolandtrolleyNostairsathome 0n/a Walkacrossopenspacesitting-diningroom 21timesin58min,0.4/(9/5/7)
p n
el mi
h
h 7/
cords. ID-576,female 7III,moderate WithhusbandUsuallyonlyleaves(detached)housewit Littleuseofaids >1/yearYes Negotiatingstairs 49timesin71min,0.(22/12/15)
e
r
heirfallhistories,andvideo ID-471,female 13IV,severe WithhusbandUsuallyonlyleaves(detached)housewithhelp MarkedfluctuationLittleuseofaids >1/yearYes Negotiatingstairs 33timesin19min,1.7/min(13/1/19)
t
acteristicsoftheparticipants, ID-279,male 5IV,severeWithwifeNeedshelptoleave(semidetached)houseUsesmobilityscooterMarkedfluctuationandfreezing;usingriserchair;limitedstairclimbing(havingstairlift) >12/yearNo Walkfromarmchair,acrosshalltotoilet 86timesin62min,1.4/min(66/1/19)
r
a
Ch ng min
Table1: ID-172,male 11IV,severeWithwifeNeedshelptoleave(semidetached)houseUsesmobilityscooter Usesstick,grabrails,andperchingstoolLimitedstairclimbing >12/yearYesWalkthroughkitchen-diner,negotiatistep 38timesin36min,1.1/(13/14/11)
)
]
0
[2 rs)
r e
Yah nsf
a
d r
Age(years),genderParkinson’sYearsdiagnosedSeverity(Hoehnan Livingarrangements Mobility RecentfallhistoryFallsFractures High-riskactivity VideoreviewInstabilitynoted(walking/standing/t min=minutes.
6 BioMedResearchInternational
Table2:Summaryofkeyobservationsfromallparticipants’videorecords,frequencyobservedbyactivity.
Walking Standing Sittingtostanding Standingtositting
Fallprevention
Usedarms ×58 ×33
Held/cruisedfurniture ×25 ×21 ×2
Heldbanisters/rails ×25
Heldkitchencounter ×3 ×14 ×1
Heldwallwithfreehand(s) ×13 ×4
Usedstick 1×throughout ×2(leanedon) ×6
Pause/adjustmidway ×14(turns,inspace,steps) ×13 ×8
Abortedattempt ×1(toreach) ×6
Observedinstability
“Sway”or“wobble”(e.g.) ×51(turns,steps) ×21(pointing,reaching) ×20(walkedstraightaway)
Nocontrol/heavy(nohands) ×23/×12
Stepped/swayedbackward ×6(step(s)back) ×11(toesofffloor)
Fellbackward ×5(intochair) ×11(feetofffloor)
Shuffledfeet ×25
Stumbledorcaughtfoot ×13 ×1
Froze ×13
Feetcrossed ×8
Balancerecovery
Staggered ×15 ×1 ×2
Grabbedfurniture ×5 ×3 ×2
Grabbedwall ×2
Grabbedbanister/rail ×2
Satquickly ×1 ×1withhelp ×1withhelp
Caughtbyresearcher ×1 ×1
Quicklygivenstick ×1
presentbutwe observed near-missesandremainedvigilant
throughout. Participants frequently recounted falls with a
sense of humour and told us we were being overcautious.
Twoavoidedusinganysupportdespitesevereinstability,even
whendemonstratinganactivityassociatedwithpreviousfall-
events(whichappearedtheonlyphysicallydemandingaspect
of the study, though every participant was willing to do it).
Participants also seemed comfortable with how we applied
sensors. The wearables did not appear to hinder or distract
them but some participants appeared unstable during (or
fatiguedby)therepeatedsittingtostandingactionsnecessary
fortheapplicationofwearables.Ourgreatestconcernabout
thewearablesandtheKinectwasthatouruncertaintyabout
Figure 5: Examples of furniture obscuring the camera and chal-
whethertheywererecordingdivertedourattentionfromthe
lengingbalance.Monitoringtransferintochairsandmanoeuvring
participants.
throughgapsbetweenfurniturepieceswouldbeinformativeasthese
activitiesfrequentlychallengebalance.Theobscuredcameraview
3.3. Video Analysis: Observed Risk of Falling. We reviewed
highlights the importance of wearable devices as part of a sensor
246 minutes of participant activity. Although occasionally
array.
they moved out of camera view or something/someone
obscuredourview(seeFigure5),wecounted227occasions
whenaparticipantappearedatimminentriskoffalling(see
Table2). markedly to use every available support on the stairs (see
All participants used support (mostly furniture) to pre- Figure6).
serve their balance; particularly when turning or rising, Participantsappearedparticularlyunsteadyduringturns
participants paused and either repositioned their hands or and on steps, if they started to walk immediately on rising,
feetorabortedtheaction.Theyflexedorrotatedtheirtrunks and if they did not use support when standing or sitting
BioMedResearchInternational 7
sensorsandcontinuedtoengageasvisitorswhilethesensors
recorded and we noted what the participants were doing.
We experienced the reality of living with a high risk of
falling, rather than simply “observed” it and support the
assertionthatasubjectiveperspectiveinresearchisvaluable
and increases “the knowledge yield” [21]: we gained more
insightthanwecouldhavethroughobserving,evaluating,or
questioningtheparticipantsanywhereotherthanathome.As
in previous studies [15] we had some technical issues (with
equipment failure and obscured sensors) and participants
disclosedsomeconcernsaboutsurveillancebuteveryinsight
gainedatthisstagewillinformaprogrammeofresearchthat
isnowbasedonexperienceratherthansupposition.
Figure6:Relianceonbanistersandrails.Participantsutilisedevery
available support when tackling the stairs. In the absence of a Some people might find researchers repeatedly observ-
banisteronbothsidesofthestaircase,oneparticipantkeptahand ing them at home difficult to accommodate to and overly
onthestairlifttrackandoneplacedbothhandsontheoneavailable intrusivebutourparticipantsallowedustorecordwherever
rail. (and whatever) we wanted: none dropped out during the
study.Participantsmayhavefeltthatsittingthewholetime
wewereobservingthemwasnotwhatwewantedtosee:itis
down. Balance was often lost backwards, but, in walking, widelyacceptedthatbeingpartofastudycancausepeopleto
participants tended to stumble forwards or sideways when change their behaviour (“the observer effect”). Rather than
their feet did not clear the floor or crossed, or they tripped a limitation, “staging a performance” can be a strength in
or froze. Unsteady transfers were characterised by swaying ethnography, wherein the findings are not the raw data but
backwards(sothatthetoesliftedonstanding)orbyactually theinterpretationofdataincontext[22].Inthecurrentstudy,
falling backwards into the chair (either on rising, or so participantsmayhavesavedactivitiestodemonstratewhile
violentlyduringsittingthatbothfeetliftedoffthefloor:twice we were recording but we still found them to be sedentary
aparticipantnearlymissedthechair). (andthussurmisethattheywereevenmoresedentarywhen
Participantsunsteadywalkingorinstandingmostlytook we were absent). Furthermore, our focus on fall risk meant
recovery steps, grabbed something, or sat down quickly to that we were observing unintentional balance loss, rather
restoretheirbalance.Whenparticipantswereunsteadytrans- thananything“staged.”
ferring,thechairbrokethepotentialfall,thoughonfiveocca-
sions(threeduringtransfers)anotherpersonassisted/caught 4.1. The Challenges of Using Sensors. The residential envi-
theparticipant. ronmentposesmanymorechallengestomovementresearch
than does the laboratory: working in a real home brought
theoretical challenges into focus. The needs of residents
3.4. Reflections on the Combined Data. The participant’s
andresearcherscanseemcontradictory,when,forexample,
histories,behaviour,andthoughts,alongsidetheresearchers’
environmental features that assisted the residents obscured
observations,suggestedthatmonitoringfiveactivitiescould
the camera’s view. Some people at risk of falling rely on
identifybalanceprotection,loss,andrecovery:
“furniturecreeping”tonegotiatesafelyaroutearoundtheir
(1)Chairtransfers. homes,soitwouldbeinappropriateforresearcherstomanip-
ulate “obstructions” (like carefully placed chairs). However,
(2)Walking(throughopenspacesandaroundfurniture).
environmentalfeatures(likedoorwaysandgapsbetweenfur-
(3)Turning(instandingandwalking). niture pieces) also challenge residents. We thereforesuggest
thatwhenresidentsarelargelysedentary,perhapsrestricting
(4)Steppingonto,off,oroverobstacles/steps.
theiropportunitiestofall,camerasshouldfocusonthefew
(5)Performingtasksinstanding(e.g.,conversing,cook- challengestheystillhavetotacklehabitually.Inthecurrent
ing). study,theneedtochangedirection(orlevel)oftenputpeople
atimminentriskoffalling.
3.5. Missing Data. We attempted to record sensor data on When exploring the potential of in-home sensors to
eighteen visits (as we reduced data collection to reduce the impact health and healthcare in future, we must consider
burden on one participant). We collected video data every whetherweneedtrackparticipants100%ofthetimethrough-
time, Kinect data 17 times (94%) as a connection between out100%oftheirhomes.Couldweanswercarefullydefined
sensor and lap top failed once, and wearable data 12 times questionswithafewappropriatesensorsoperatingatrelevant
(67%)afterfourequipmentfailuresandtwooperatorerrors. momentsin high yield locations? Our findings suggest that
awearabledevicecoupledwithcamerasinthesittingroom
4.Discussion and hall could meet the requirements and be acceptable to
residents.Forclinicianstoadoptsuchtechnology,however,it
Followingtheprinciplesofethnographicresearch,wesought wouldneedtobemoreuser-friendlyandlessdistractingthan
tokeepthesituationasnaturalaspossiblebeforeintroducing theiterationsweutilised:welostdatabeforewemodifiedthe
8 BioMedResearchInternational
user-interfacesonthewearablesandKinect.Asacombined balanceprotection,loss,orrestoration,thoughasinglesensor
array however, extended human observation coupled with hasadvantagesineaseofapplication.
sensors could be an effective way of understanding how Without intruding in parts of the home people might
peopleatriskoffallingnegotiateoravoidhigh-risklocations prefer to keep private, sensors could monitor the risks of
and activities at home. We suggest clinicians working with falling, and the associated risks of inactivity and isolation.
peopleathighriskoffallingtakeadetailedfallhistory[10] With reasonable reservations, our participants accepted the
and then follow up with a period of in-home monitoring, technology. They appeared at greatest risk of falling trans-
targeting the areas of most concern on an individual basis. ferring, crossing spaces without handholds, manoeuvring
Even having sensors in the home for one week might yield aroundobstacles,turningthroughdoorways,andnegotiating
richer data than is obtainable through self-report. In the steps.Thesechallengesareamenabletorecordingandmost,
current study, we asked participants to identify an activity if not all, arise along the short route between favourite
that they felt carried a particularly high fall risk for them chair and stair, negating the need for pervasive sensors
andweobserved thisactivityrepeatedly(attheendofdata throughoutthehome,withnopossibilityof“escape.”Having
collection when we were familiar with the participant). In identifiedkeyactivitiesduringwhichexperiencedobservers
a research context, this approach is an alternative to basing noted “instability,” our next step will be to examine the
sensorplacementonacompletefallhistory. data recorded by the Kinect and wearable sensors that the
Without witnessing an event or having video to review, participantsallowedustotrial.Researchersneedtoestablish
clinicians (such as physiotherapists) can only glean what that sensors can identify instability during simple isolated
happenedbefore,during,andaftertheeventfromsomeone’s actions and then to validate the sensor-based identification
recollection.Continuouslyrecordingfrommultiplecameras offallriskduringcomplexfree-livingactivities.
within the home is impractical but there is the potential to
keeparecordofincidentsforlaterscrutiny.Itmaybepossible, 4.3. Wider Applications. Our research focusses on moni-
for example, to record for one minute and continuously toring fall risk among people with balance and mobility
delete that recording unless there is something to report. disorders that restrict their function and participation in
This would revolutionise the data on which clinicians base society.Butourrecommendationforfocused(nonpervasive)
decisions. There is growing interest in using sensors to log monitoring applies to anyone whose health limits their
simple gait parameters (e.g., in people walking 20m trials activity towards a single favoured location, with everything
in a laboratory [23]) but the current study suggests that they need close to hand. Though we studied only people
cliniciansmanagingfallriskneedtoknowmorethansome- with Parkinson’s in the current project, the indications of
one’s stride length and velocity. Sensors could, for example, fall risk that we have suggested sensors could identify and
revealfluctuationsinperformanceunderdifferentconditions monitoraregeneric(withtheprobableexceptionoffreezing).
and the availability and success of saving reactions when Although our participants were sedentary, they were still
required. mobile: we believe that focussing sensors on a defined,
frequently occupied, daytime location in the home should
4.2.ThePotentialofSensorstoSurpassHumanObserversin beasinformativeabouthowandwhenlesssedentary(even
MonitoringFallRisk. Peoplerecogniseanindividualisatrisk activeorimpulsive)individualsmove.However,unobtrusive
offallinginmanyways,fromhowtheylookormovetowhat sensors capable of identifying instability could have even
theysay.However,anindividualmayfeelthattheyaregoing wider applications. For example, alcohol intoxication may
to fall whilst giving no obvious indications to an observer; alterbehaviourandfallrisk.Itmightbepossibletomonitor
sensors may surpass humans in being sensitive enough to remotelyindicatorsofinstability,ifitisintrusivetomonitor
detect very subtle deviations in motor behaviour. Further anindividualorimpossibletomonitoracrowd.
research is necessary but evidence suggests that triaxial
accelerometerswornonthepelvismaydistinguishnearfalls 5.Conclusions
from other gait patterns observed in healthy subjects on a
treadmillinagaitlaboratory[1]. Immersedintherealityoflivingwiththeexperience,fearand
Extended in-home observation has multiple advantages riskoffalling,wegainedinsightswewouldnothavegained
over the one-off “home visits” used in clinical practice. anyotherway.Participantsenhancedtheirsafetyusingbanis-
With no agenda, the observer sees how the resident uses tersandgrabrailsbutmorefrequentlyadditionalsupportin
their space, how they pace activities,and how they manage theformofstrategicallyplacedfurniture.Researcherscannot
tasks when their attention is on a goal, not on the task removesuch“obstacles”butmusttaketheircuefromthem:
itself. Ticking off a checklist of theoretical challenges and thesearewherepeoplearelikelytoappearunstable.Because
hazardswithinasinglesessionislikelytobeunrepresentative: physical obstructions, including other people, frequently
“assessing”someonedescendingthestepsintotheirkitchen obscured the cameras’ views, comprehensive monitoring
isunlikelytoreflecthowtheydoitwhenhurryingtowards wouldnecessitatemultiplecamerasplusatleastonewearable
a saucepan that is boiling over. Over an extended period, a device.However,theextenttowhichparticipantsrestricted
humanobserverwouldbecostlyandintrusive;sensorswould their activity suggests that, by identifying an individual’s
be more realistic, and monitoring by sensors alone would high-use locations and focussing only on them, researchers
removeanyneedto“perform”forahumanobserver.Amixed and clinicians could leave the remainder of people’s homes
arrayofsensorsislikelytooutdoanysingletypeinidentifying sensor-free.
BioMedResearchInternational 9
We most frequently noted a high risk of falling when General Practitioner,” BMC Family Practice, vol. 11, article 8,
people transferred between sitting and standing, walked, 2010.
turned, negotiated steps, and tackled tasks in standing. We [5] E.A.Lingard,E.A.Wright,andC.B.Sledge,“Pitfallsofusing
noted that someone was protecting, losing, or restoring patientrecalltoderivepreoperativestatusinoutcomestudiesof
theirbalancelargelythroughvisualclues,sowebelievethat totalkneearthroplasty,”TheJournalofBoneandJointSurgery—
appropriatelypositionedsensorswouldalsobeabletodetect AmericanVolume,vol.83,no.8,pp.1149–1156,2001.
indicationsofinstability.Whethersensorscanequal,oreven [6] J. Ward and R. Sanson-Fisher, “Accuracy of patient recall of
surpass, human observers requires further research. People opportunistic smoking cessation advice in general practice,”
may be less likely to alter their behaviour for a sensor than TobaccoControl,vol.5,no.2,pp.110–113,1996.
for a human observer, and sensors might be able to detect [7] S.R.Cummings,M.C.Nevitt,andS.Kidd,“Forgettingfalls:
changesinstabilitytoosubtleforhumanobserverstonotice thelimitedaccuracyofrecalloffallsintheelderly,”Journalof
theAmericanGeriatricsSociety,vol.36,no.7,pp.613–616,1988.
in real time or from video. There is considerable scope for
sensors to usefully monitor changing fall risk (rather than [8] NICE,TheAssessmentandPreventionofFallsinOlderPeople,
Clinical Guideline 161, National Institute for Health and Care
merelydetectfalls)unobtrusively.Wesuggestthatresearchers
Excellence,2013,http://guidance.nice.org.uk/CG161.
explore the value of monitoring a habitual route (such as
[9] C. Todd and D. Skelton, What Are the Main Risk Factors for
betweenchairandstair,orforpeoplelivinginhomeswithout
Falls amongst Older People and What Are the Most Effective
stairs,thedaytimeroutebetweenchairandtoiletdoor)when
Interventions to Prevent These Falls? World Health Organiza-
evaluatingfallriskovertime.
tion,Geneva,Switzerland,2004.
[10] E.StackandA.Ashburn,“Falleventsdescribedbypeoplewith
EthicalApproval
Parkinson’sdisease:implicationsforclinicalinterviewingand
theresearchagenda,”PhysiotherapyResearchInternational,vol.
The Ethics Committee of the Faculty of Health Sciences,
4,no.3,pp.190–200,1999.
UniversityofSouthampton,grantedapprovaltoconductthis
[11] R.D.Clark,S.R.Lord,andI.W.Webster,“Clinicalparameters
study in May 2014 (Observing Frequent Fallers at Home:
associatedwithfallsinanelderlypopulation,”Gerontology,vol.
EthicsID9766). 39,no.2,pp.117–123,1993.
[12] F. Bagala`, C. Becker, A. Cappello et al., “Evaluation of accel-
ConflictofInterests erometer-based fall detection algorithms on real-world falls,”
PLoSONE,vol.7,no.5,ArticleIDe37062,2012.
The authors declare that there is no conflict of interests
[13] A. K. Bourke, S. Prescher, F. Koehler et al., “Embedded fall
regardingthepublicationofthispaper. andactivitymonitoringforawearableambientassistedliving
solution for older adults,” in Proceedings of the IEEE Annual
International Conference of the Engineering in Medicine and
Acknowledgments
BiologySociety(EMBC’12),vol.2012,pp.248–251,SanDiego,
Calif,USA,September2012.
This work was performed under the SPHERE (Sensor Plat-
[14] S.N.Robinovitch,F.Feldman,Y.Yangetal.,“Videocaptureof
formforHealthcareinaResidentialEnvironment)interdis-
thecircumstancesoffallsinelderlypeopleresidinginlong-term
ciplinary research collaboration funded by the Engineering
care:anobservationalstudy,”TheLancet,vol.381,no.9860,pp.
andPhysicalSciencesResearchCouncil(EPSRC)UK:Grant
47–54,2013.
EP/KO31910/1. The authors thank the participants for wel-
[15] F. Feldwieser, M. Gietzelt, M. Goevercin et al., “Multimodal
coming them into their homes; the branches of Parkinson’s
sensor-basedfalldetectionwithinthedomesticenvironmentof
UKwhoallowedthemtoinvitetheirmemberstotakepartin
elderlypeople,”ZeitschriftfurGerontologieundGeriatrie,vol.
thestudy;andDr.MassimoCamplani(UniversityofBristol)
47,no.8,pp.661–665,2014.
forhiscontributiontotechnologydevelopment.
[16] A. Ashburn, E. Stack, R. M. Pickering, and C. D. Ward, “A
community-dwellingsampleofpeoplewithParkinson’sdisease:
References characteristicsoffallersandnon-fallers,”AgeandAgeing,vol.
30,no.1,pp.47–52,2001.
[1] A.Weiss,I.Shimkin,N.Giladi,andJ.M.Hausdorff,“Automated [17] B.Lindholm,P.Hagell,O.Hansson,andM.H.Nilsson,“Pre-
detectionofnearfalls:algorithmdevelopmentandpreliminary dictionoffallsand/ornearfallsinpeoplewithmildParkinson’s
results,”BMCResearchNotes,vol.3,article62,2010. disease,”PLoSONE,vol.10,no.1,ArticleIDe0117018,2015.
[2] P. A. Garcia, J. M. D. Dias, S. L. A. Silva, and R. C. Dias, [18] C.G.Canning,“RehabilitationinParkinson’sdisease—thechal-
“Prospectivemonitoringandself-reportofpreviousfallsamong lenge to provide early and ongoing, evidence-based, patient-
olderwomenathighriskoffallsandfractures:astudyofcom- centredcare,”ArquivosdeNeuro-Psiquiatria,vol.71,no.12,2013.
parisonandagreement,”BrazilianJournalofPhysicalTherapy, [19] C.BaileyandV.Buckley,“Recruitingandretainingolderper-
vol.19,no.3,pp.218–226,2015. sonswithinahome-basedpilotstudyusingmovementsensors,”
[3] D.Kunkel,R.M.Pickering,andA.M.Ashburn,“Comparison HealthandSocialCareintheCommunity,vol.19,no.1,pp.98–
ofretrospectiveinterviewsandprospectivediariestofacilitate 105,2011.
fallreportsamongpeoplewithstroke,”AgeandAgeing,vol.40, [20] M.M.HoehnandM.D.Yahr,“Parkinsonism;onset,progres-
no.2,ArticleIDafq177,pp.277–280,2011. sionandmortality,”Neurology,vol.17,no.5,pp.427–442,1967.
[4] A. O. Booth and C. A. Nowson, “Patient recall of receiving [21] M.PfadenhauerandT.Grenz,“Uncoveringtheessence:thewhy
lifestyle advice for overweight and hypertension from their and how of supplementing observation with participation in