Ambient Assisted Living and Ambient Intelligence for Health

147 Pages·2017·20.47 MB·English

Mobile Information Systems
Ambient Assisted Living and
Ambient Intelligence for Health
Lead Guest Editor: Pino Caballero-Gil
Guest Editors: Lilia Georgieva, Ljiljana Brankovic, and Mike Burmester
Ambient Assisted Living
and Ambient Intelligence for Health
Mobile Information Systems
Ambient Assisted Living
and Ambient Intelligence for Health
Lead Guest Editor: Pino Caballero-Gil
Guest Editors: Lilia Georgieva, Ljiljana Brankovic,
and Mike Burmester
Copyright©2018Hindawi.Allrightsreserved.
Thisisaspecialissuepublishedin“MobileInformationSystems.”AllarticlesareopenaccessarticlesdistributedundertheCreativeCom-
monsAttributionLicense,whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkis
properlycited.
Editorial Board
MariC.AguayoTorres,Spain AlmudenaDíaz-Zayas,Spain FrancoMazzenga,Italy
RamonAguero,Spain FilippoGandino,Italy EduardoMena,Spain
MarkosAnastassopoulos,UK JorgeGarciaDuque,Spain MassimoMerro,Italy
MarcoAnisetti,Italy L.J.GarcíaVillalba,Spain JoseF.Monserrat,Spain
ClaudioAgostinoArdagna,Italy MicheleGaretto,Italy RaulMontoliu,Spain
JoseM.Barcelo-Ordinas,Spain RomeoGiuliano,Italy MarioMuñoz-Organero,Spain
AlessandroBazzi,Italy ProsantaGope,Singapore FrancescoPalmieri,Italy
LucaBedogni,Italy JavierGozalvez,Spain JoséJ.Pazos-Arias,Spain
PaoloBellavista,Italy FrancescoGringoli,Italy VicentPla,Spain
NicolaBicocchi,Italy CarlosA.Gutierrez,Mexico DanieleRiboni,Italy
PeterBrida,Slovakia RaviJhawar,Luxembourg PedroM.Ruiz,Spain
CarlosT.Calafate,Spain PeterJung,Germany MicheleRuta,Italy
MaríaCalderon,Spain AdrianKliks,Poland StefaniaSardellitti,Italy
JuanC.Cano,Spain DikLunLee,HongKong FilippoSciarrone,Italy
SalvatoreCarta,Italy DingLi,USA FlorianoScioscia,Italy
Yuh-ShyanChen,Taiwan JurajMachaj,Slovakia MichaelVassilakopoulos,Greece
WenchiCheng,China SergioMascetti,Italy LaurenceT.Yang,Canada
MassimoCondoluci,Sweden ElioMasciari,Italy JinglanZhang,Australia
AntoniodelaOliva,Spain MaristellaMatera,Italy
Contents
AmbientAssistedLivingandAmbientIntelligenceforHealth
PinoCaballero-Gil ,LiliaGeorgieva,LjiljanaBrankovic,andMikeBurmester
Editorial(2pages),ArticleID7560465,Volume2018(2018)
DeepLearningversusProfessionalHealthcareEquipment:AFine-GrainedBreathingRateMonitoring
Model
BangLiu ,XiliDai ,HaigangGong,ZihaoGuo,NianboLiu ,XiaominWang,andMingLiu
ResearchArticle(9pages),ArticleID5214067,Volume2018(2018)
ImprovingLearningTasksforMentallyHandicappedPeopleUsingAmIEnvironmentsBasedon
Cyber-PhysicalSystems
DiegoMartín ,BorjaBordel ,RamónAlcarria ,andYoneCastro
ResearchArticle(12pages),ArticleID8198379,Volume2018(2018)
AFuzzyLogic-BasedPersonalizedMethodtoClassifyPerceivedExertioninWorkplacesUsinga
WearableHeartRateSensor
PabloPancardo ,J.A.Hernández-Nolasco ,andFranciscoAcosta-Escalante
ResearchArticle(17pages),ArticleID4216172,Volume2018(2018)
GaitAnalysisUsingComputerVisionBasedonCloudPlatformandMobileDevice
MarioNieto-Hidalgo ,FranciscoJavierFerrández-Pastor,RafaelJ.Valdivieso-Sarabia ,
JerónimoMora-Pascual,andJuanManuelGarcía-Chamizo
ResearchArticle(10pages),ArticleID7381264,Volume2018(2018)
GaitSpeedMeasurementforElderlyPatientswithRiskofFrailty
XavierFerre,ElenaVillalba-Mora,Maria-AngelesCaballero-Mora,AlbertoSanchez,WilliamsAguilera,
NuriaGarcia-Grossocordon,LauraNuñez-Jimenez,LeocadioRodríguez-Mañas,QinLiu,
andFranciscodelPozo-Guerrero
ResearchArticle(11pages),ArticleID1310345,Volume2017(2018)
MassageTherapyoftheBackUsingaReal-TimeHaptic-EnhancedTelerehabilitationSystem
CristinaRamírez-Fernández,VictoriaMeza-Kubo,EloísaGarcía-Canseco,AlbertoL.Morán,OliverPabloff,
DavidBonilla,andNirvanaGreen
ResearchArticle(10pages),ArticleID5253613,Volume2017(2018)
SemanticandVirtualReality-EnhancedConfigurationofDomesticEnvironments:TheSmartHome
Simulator
DanieleSpoladore,SaraArlati,andMarcoSacco
ResearchArticle(15pages),ArticleID3185481,Volume2017(2018)
HelpingElderlyUsersReportPainLevels:AStudyofUserExperiencewithMobileandWearable
Interfaces
IyubanitRodríguez,GabrielaCajamarca,ValeriaHerskovic, CarolinaFuentes,andMauricioCampos
ResearchArticle(12pages),ArticleID9302328,Volume2017(2018)
DAFIESKU:ASystemforAcquiringMobilePhysiologicalData
MaiderSimón,EzekielSarasua,BorjaGamecho, EdurneLarraza-Mendiluze,andNestorGaray-Vitoria
ResearchArticle(17pages),ArticleID7261958,Volume2017(2018)
ExploitingAwarenessfortheDevelopmentofCollaborativeRehabilitationSystems
MiguelA.Teruel,ElenaNavarro,andPascualGonzález
ResearchArticle(15pages),ArticleID4714328,Volume2017(2018)
FuzzyIntelligentSystemforPatientswithPreeclampsiainWearableDevices
MacarenaEspinilla,JavierMedina,Ángel-LuisGarcía-Fernández, SixtoCampaña,andJorgeLondoño
ResearchArticle(10pages),ArticleID7838464,Volume2017(2018)
Hindawi
Mobile Information Systems
Volume 2018, Article ID 7560465, 2 pages
https://doi.org/10.1155/2018/7560465
Editorial
Ambient Assisted Living and Ambient Intelligence for Health
Pino Caballero-Gil ,1 Lilia Georgieva,2 Ljiljana Brankovic,3 and Mike Burmester4
1Departmentof ComputerEngineeringand Systems,UniversityofLaLaguna,Tenerife, Spain
2Departmentof ComputerScience,Heriot-WattUniversity,Edinburgh, UK
3SchoolofElectrical EngineeringandComputer Science,UniversityofNewcastle,Australia
4CenterforSecurity&AssuranceinIT,FloridaStateUniversity,USA
CorrespondenceshouldbeaddressedtoPinoCaballero-Gil;[email protected]
Received 14 January 2018; Accepted 15 January 2018; Published 20 June 2018
Copyright©2018PinoCaballero-Giletal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properlycited.
Ambient assisted living is an emerging trend in which ar- replaying massage sessions according to a patient’s therapy
tificialintelligenceenablestheuseofnewproducts,services, program.
and processes that help to provide safe, high-quality, and ThepaperbyD.Spoladoreetal.presentsasmarthome
independentlivesforthefrailandelderly.Duetounderlying simulator, using semantic and virtual reality-enhanced con-
health issues, aspects of everyday living can become phys- figurationofdomesticenvironments,andtakingintoaccount
ically and mentally challenging for them. Technology can both the preferences of the end-users, the configurations of
supportdailyinteractionandbeintegratedinthehealthcare smart appliances, and relevant technologies, including de-
ofseniorcitizens,whicharebothvitaltoensuretheirhealth ployment and data-sharing issues.
and happiness. ThepaperbyI.Rodr´ıguezetal.addressesissuessurrounding
Artificial intelligence has enabled significant advancements usingmobileandwearabledevicesforself-reportingofchronic
in ensuring such support, while preserving independence. Ad- pain and pain management in older adults.
vancements include development of information and com- The paper by M. Simo´n et al. introduces a system for
municationtechnologiesusedinversatileways,includingfor gatheringphysiologicaldata,whichisvaluablefortheanalysisof
prediction,prevention,rehabilitation,andsupport.However, personal characteristics, such as behaviour, health conditions,
technologythatenablesambientassistedlivingcomeswithits and preferences.
own challenges. It needs to be easy to use, while suitably The paper by M. A. Teruel et al. discusses physical and
designed, and adaptable to changing needs and individual cognitive rehabilitation and shows that thedevelopment of
preferences. collaborative rehabilitation systems is one of the best alter-
ThepaperbyX.Ferreetal.addressestheuseofultrasonic natives to mitigate isolation.
sensor-based gait speed measurement device controlled via The article by M. Espinilla et al. introduces a fuzzy in-
amobileinterface,whichpermitspatientstoself-assessphysical telligent system for patients with preeclampsia in wearable
performance. This allows for timely detection of functional devices.Thesystemusesadecisionanalysistoolfortheearly
declineandfrailty,whichcan,ifundetected,ultimatelyprogress detection of the condition in women at risk.
to disability. Gait analysis, using computer vision based on cloud
The paper by C. Ram´ırez-Ferna´ndez et al. presents the platform and mobile device, is the topic of the paper by
usabilityevaluationofahaptic-enhancedtelerehabilitation M.Nieto-Hidalgoetal.Sincedeteriorationofcognitiveand
systemformassagetherapyoftheback.Thesystemincludes motorfunctionislinkedtogaitpatterns,gaitanalysiscanbe
features that allow for administering online therapy pro- a powerful tool to assess frailty and senility syndromes.
grams,providingself-adjustableandsafetytreatmentofback The paper by B. Liu et al. discusses the relevance of
massages using a virtual environment, and saving and monitoring breathing and establishing accurate breathing
2 MobileInformationSystems
rateusingadeeplearning-basedfine-grainedbreathingrate
monitoring algorithm, which works on smartphone and
achieves professional-level accuracy.
A fuzzy logic-based personalized method to classify
perceivedexertioninworkplacesusingawearableheartrate
sensoristhetopicofthepaperbyP.Pancardoetal.Wearable
heart rate sensors represent an effective way to capture
perceivedexertion,ergonomicmethodsaregeneric,andthey
donotconsiderthediffusenatureoftherangesthatclassify
the efforts. The proposed method is personalized, and it
assessesperceivedexertionandusesfuzzylogicasanoption
tomanageimprecisionanduncertaintyininvolvedvariables.
The paper by D. Mart´ın et al. discusses approaches to
improving learning tasks for mentally handicapped people
usingambientintelligencetechniquesbasedoncyber-physical
systems.Thepapershowsthatthatsuchsolutionsarefeasible
and allow for learning of complex tasks in some cases.
Pino Caballero-Gil
Lilia Georgieva
Ljiljana Brankovic
Mike Burmester
Hindawi
Mobile Information Systems
Volume 2018, Article ID 5214067, 9 pages
https://doi.org/10.1155/2018/5214067
Research Article
Deep Learning versus Professional Healthcare Equipment:
A Fine-Grained Breathing Rate Monitoring Model
Bang Liu , Xili Dai , Haigang Gong, Zihao Guo, Nianbo Liu ,
Xiaomin Wang, and Ming Liu
Big DataResearch Center,Departmentof ComputerScience andEngineering,
Universityof ElectronicScienceandTechnologyofChina,Chengdu,China
CorrespondenceshouldbeaddressedtoXiliDai;[email protected]
Received 28 July 2017; Revised 13 November 2017; Accepted 28 November 2017; Published 1 March 2018
AcademicEditor:PinoCaballero-Gil
Copyright©2018BangLiuetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,which
permitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
InmHealthfield,accuratebreathingratemonitoringtechniquehasbenefitedabroadarrayofhealthcare-relatedapplications.
Manyapproachestrytousesmartphoneorwearabledevicewithfine-grainedmonitoringalgorithmtoaccomplishthetask,which
can only be done by professional medical equipment before. However, such schemes usually result in bad performance in
comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained
breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bi-
directionalrecurrentneuralnetwork(RNN)stackedwithconvolutionallayersandspeededupbybatchnormalization.Moreover,
wecollect16.17GBbreathingsoundrecordingdataof248hoursfrom109andanother10volunteerstotrainandtestourmodel,
respectively. Theresultsshowareasonably good accuracyofbreathingrate monitoring.
1.Introduction fatigue, depression, cardiovascular disease, and anxiety [5],
breathingrate monitoringiscriticaltodetect earlysignsof
The emergence of mHealth draws much attention both in several diseases such as diabetes and heart disease [6]. The
industry and academy [1]. Google, Microsoft, and Apple breathing rate monitoring can also be applied to the sleep
conduct a series of work on mHealth from hardware to apnea diagnosis and treatment, treatment for asthma [7],
software.GoogleisthefirstonetogetinvolvedinmHealth. and sleep stage detection [8]. Thus, fine-grained breathing
InApril2012,GooglereleasedGoogleGlass[2]andapplied rate monitoring is important to facilitate these healthcare-
ittohealthcareinJuly2013[3].Pristinedeclaredtodevelop related applications.
medical application for Google Glass. After that, Google Traditionally, one’s breathing rate can be captured by
accomplishedtheacquisitionofabiotechcompanyLiftLabs, professionalmedicalequipmentasmonitoringmachinesin
whichinventedanelectronicspoontohelpParkinsonpatients hospitals. In most cases, such machines are too expensive,
havefood.In2015,GoogleXannouncedthatitwasworking toocomplex,andtooheavyfordailyuseforordinarypeople.
on wearable suits which can exam cancer cell of users. In A possible solution is to achieve accurate sleep monitoring
addition,MicrosoftBand,AppleWatch,Fitbit,Jawbone,and viasmartphoneorotherdeviceswithrecognitionalgorithm
more smart wearable devices bloom up everywhere. [9],whichismoreandmorepopularincurrenthealthcare-
There exists a broad array of healthcare-related appli- relatedapplications.Forexample,Renetal.[10]exploitthe
cations on sleep monitoring by smart wearable devices [4]. readily available smartphone earphone placed close to the
Theyoftenaimatfine-grainedbreathingratemonitoringas user to reliably capture the human breathing sound. It
a kind of nonobtrusive sleep monitoring for the under- cannotworkiftheearphoneisapartfromtheuser.Liuetal.
standing of users’ sleep quality. Since inadequate and ir- [11]tracksthevitalsignsofboththebreathingrateandthe
regular sleep can lead to serious health problems such as heartrateduringsleep,byusingoff-the-shelfWiFiwithout