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Application for Continuous Health Monitoring using Machine-to-Machine Communications February 2012

Application for Continuous Health Monitoring using Machine-to-Machine Communications February 2012. João Prudêncio. Supervisors: Ana Aguiar, Daniel Lucani. 1. Context. Aging population 1 ; 48% of the US population suffer from at least one chronic ailment 2 ;

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Application for Continuous Health Monitoring using Machine-to-Machine Communications February 2012

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  1. Application for Continuous Health Monitoring using Machine-to-Machine CommunicationsFebruary 2012 JoãoPrudêncio Supervisors: Ana Aguiar, Daniel Lucani

  2. 1. Context • Aging population 1; • 48% of the US population suffer from at least one chronic ailment 2; • Health care crisis, spending reached 15.5% of GDP by year of 2010 3. Mobile-healthcare 1 World Health Organization. 2004. Active ageing: Towards age-friendly primary health care. WHO Library Cataloguing-in-Publication Data. http://whqlibdoc.who.int/publications/2004/9241592184.pdf (accessed November 22, 2011). 2 D.B. Kendall, K.Tremain, J. Lemieux, and S.R. Levine. 2003. Heatlhy Aging v. Chronic Illness Preparing Medicare for the New Health Care Challenge. Quoted in Shieh, Y.Y.; Tsai, F.Y.; Arash; Wang, M.D.; Lin. 2007. Mobile Healthcare: Opportunities and Challenges. Paper presented at International Conference on the Management of Mobile Business, July 9-11, in Toronto, Canada 3 Centers for Medicare and Medicaid Services (CMS). 2011. National Health Expenditures 2000-2010. http://www.cms.gov/ (accessed November 27, 2001)

  3. 2. Problem • How to monitor the patients in near real time?; • Achieve energy efficiency, security and reliability; • Interoperability 1; • Lack of open solutions for mobile healthcare. 1Shin, Donghoon. 2011. M-healthcare revolution: an e-commerce perspective. Paper presented at First ACIS/JNU International Conference onComputers, Networks, Systems and Industrial Engineering, May 23-25.

  4. 3. Objectives

  5. 4. Systemarchitecture

  6. 5.Exampleofapplications MOTOACTV Motorola. 2011. Motorola brings personalized media and mobile experiences together to meet the exploding consumer demand for video and interactive services. http://www.motorola.com/Consumers/US-EN/Consumer-Product-and-Services/MOTOACTV/MOTOACTV/MOTOACTV-US-EN  (accessed January 20, 2012)

  7. 5.Exampleofapplications • Endomodo Endomondo. 2007. Endomondo is a sports community based on free real-time GPS tracking of running, cycling, etc.http://www.endomondo.com (accessed January 20, 2012)

  8. 6. Machine-to-MachinesCommunications • Communication among Machines without human intervention 1 ; • The most promising solution for the intelligent pervasive applications 1 2; • Standardization is the wise step to enable interoperability and integration of the worldwide systems; • Use cases, service requirements and capabilities of a M2M architecture in an healthcare scenario is currently being developed by ETSI 3. 1 RongxingLu; Xu Li; XiaohuiLiang; XueminShen; XiaodongLin; , "GRS: Thegreen, reliability, andsecurityofemergingmachine to machinecommunications," Communications Magazine, IEEE , vol.49, no.4, pp.28-35, April2011 2GengWu; Talwar, S.; Johnsson, K.; Himayat, N.; Johnson, K.D.; , "M2M: From mobile to embedded internet," Communications Magazine, IEEE , vol.49, no.4, pp.36-43, April2011 3ETSI(TheEuropeanTelecommunications Standards Institute). 2011. Draft ETSI TR 102 732 V0.4.1. Machine to Machine Communications (M2M): Use cases of M2M applications for eHealth. France: TheEuropeanTelecommunications Standards Institute.

  9. 6. Machine-to-MachinesCommunications Shao-YuLien; Kwang-ChengChen; YonghuaLin; , "Towardubiquitousmassiveaccessesin 3GPP machine-to-machinecommunications," Communications Magazine, IEEE , vol.49, no.4, pp.66-74, April 2011

  10. 7. Heartabnormalities • Bradycardia: heart rate lessthan 60 bps; • Tachycardia:heart rate greaterthat 100 bps; • QRS complexes: QRS intervalgreaterthan 120 milisecondsandheart rate greaterthan 100 bps; • Supraventriculartachycardiawithnarrow QRS complexes: QRS intervallessthan 120 milisecondsandheart rate greaterthan 100 bps. Liszka, K.J.; Mackin, M.A.; Lichter, M.J.; York, D.W.; DilipPillai; Rosenbaum, D.S.; , "Keeping a beatontheheart," PervasiveComputing, IEEE , vol.3, no.4, pp. 42- 49, Oct.-Dec. 2004 YonglinRen; Pazzi, R.W.N.; Boukerche, A.; , "Monitoringpatients via a secureand mobile healthcaresystem," WirelessCommunications, IEEE , vol.17, no.1, pp.59-65, February 2010

  11. 8.GeoFencing • Perimeter in a geographic area; • When the user exits the virtual fence an alarm is generated 1 2; • Useful for patients with dementia 3. 1 Armstrong, N.; Nugent, C.D.; Moore, G.; Finlay, D.D.; , "Developingsmartphoneapplications for peoplewithAlzheimer'sdisease," InformationTechnologyandApplicationsinBiomedicine (ITAB), 2010 10th IEEE InternationalConferenceon , vol., no., pp.1-5, 3-5 Nov. 2010 2Bilgic, HasanTahsin; Alkar, Ali Ziya; , "A securetrackingsystem for GPS-enabled mobile phones," InformationTechnologyandMultimedia (ICIM), 2011 InternationalConferenceon , vol., no., pp.1-5, 14-16 Nov. 2011 3 Alotaibi, F.D.; Abdennour, A.; Ali, A.A.; , "A Real-TimeIntelligentWireless Mobile StationLocationEstimatorwithApplication to TETRA Network," Mobile Computing, IEEE Transactionson , vol.8, no.11, pp.1495-1509, Nov. 2009

  12. 8.GeoFencing • Ray casting algorithm; • Simplepolygonsnotself-interconnected1. • f(ei ) hasthevalueof: • -1, ifeicrossedup to down; • 1, ifeicrosseddown to up; • 0, ifeinotcrossed . P: P3P4, P4P5, P5P6 and P6P7.F(P) = 1+(-1)+1+(-1) If F = 1 thenit’saninternalpointIf F = 0 thenit’sanexternalpoint Wu Jian; CaiZongyan; , "A method for the decision of a point whether in or not in polygon and self-intersected polygon," Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on , vol.1, no., pp.16-18, 26-28 July 2011

  13. 8.GeoFencing

  14. 9.Human Activity Recognition Khan, A. M.; Lee, Y. K.; Kim, T.-S.; , "Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets," Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE , vol., no., pp.5172-5175, 20-25 Aug. 2008 Khan, A.M.; Young-Koo Lee; Lee, S.Y.; Tae-Seong Kim; , "A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer," Information Technology in Biomedicine, IEEE Transactions on , vol.14, no.5, pp.1166-1172, Sept. 2010

  15. 9.Human Activity Recognition AutoregressiveModeling • Linear predictionmethods: predictsthe output basedonprevious inputs1 2; • Finiteimpulse response (FIR) filter; • Methods: Theleastsquares; Yule-Walker ; Burg’s3 4. Y(t) original signal a(i) unknowncoefficients P theorderofthemodel E(t) residual error 1C.JenningsM.KulahciMontgomery,C.Douglas. IntroductiontoTime Series AnalysisandForecasting.JohnWileyandSons.Inc.,first edition,2008 2 Khan, A.M.; Young-KooLee; Lee, S.Y.; Tae-SeongKim; , "A TriaxialAccelerometer-BasedPhysical-ActivityRecognition via Augmented-SignalFeaturesand a HierarchicalRecognizer," InformationTechnologyinBiomedicine, IEEE Transactionson , vol.14, no.5, pp.1166-1172, Sept. 2010 3H.SchoonewelleM.J.L.DeHoon,T.H.J.J.Van Der HagenandH.VanDam. WhyYule-Walkershouldnotbeused for autoregressivemodelling. 4 K. Roth, I. Kauppinen,P.A.A.Esquef,andV.Valimaki. FrequencywarpedBurg’smethod for AR-modeling.

  16. 9.Human Activity Recognition • Signal Magnitude Area (SMA) • Analyzethe magnitude ofthevariationsofthesignal; • Distinguishbetweenstaticanddynamicactivities1 2. Where x(i), y(i), z(i) : accelerationinthex,y,zaxisatthetime i 1 Khan, A. M.; Lee, Y. K.; Kim, T.-S.; , "Accelerometersignal-basedhumanactivityrecognitionusingaugmentedautoregressivemodelcoefficientsand artificial neural nets," EngineeringinMedicineandBiologySociety, 2008. EMBS 2008. 30th AnnualInternationalConferenceofthe IEEE , vol., no., pp.5172-5175, 20-25 Aug. 2008 2 Khan, A.M.; Young-KooLee; Lee, S.Y.; Tae-SeongKim; , "A TriaxialAccelerometer-BasedPhysical-ActivityRecognition via Augmented-SignalFeaturesand a HierarchicalRecognizer," InformationTechnologyinBiomedicine, IEEE Transactionson , vol.14, no.5, pp.1166-1172, Sept. 2010

  17. 9.Human Activity Recognition TiltAngle • Anglebetweenthe vector ofgravityandthe z axis1 2; • Distinguishbetweenstaticactivities: sittingandlying3. 1 Karantonis, D.M.; Narayanan, M.R.; Mathie, M.; Lovell, N.H.; Celler, B.G.; , "Implementationof a real-timehumanmovementclassifierusing a triaxialaccelerometer for ambulatorymonitoring," InformationTechnologyinBiomedicine, IEEE Transactionson , vol.10, no.1, pp.156-167, Jan. 2006 2 Do-Un Jeong; Se-Jin Kim; Wan-Young Chung; , "Classification of Posture and Movement Using a 3-axis Accelerometer," Convergence Information Technology, 2007. International Conference on , vol., no., pp.837-844, 21-23 Nov. 2007 3 Veltink, P.H.; Bussmann, HansB.J.; de Vries, W.; Martens, WimL.J.; VanLummel, R.C.; , "Detectionofstaticanddynamicactivitiesusinguniaxialaccelerometers,"RehabilitationEngineering, IEEE Transactionson , vol.4, no.4, pp.375-385, Dec 1996

  18. 9.Human Activity Recognition Newfeaturesproposal Stage 1 Stage 2

  19. 9.Human Activity Recognition Newfeaturesproposal Stage 3 Stage 4

  20. 10.Activity Data Acquisition 6 individuals 10 hoursofactivity

  21. 11. Technologies • Machine-to-Machine Communications • The Extensible Messaging and Presence Protocol (XMPP) • MyContext: Context Framework developedby PT Inovação • Android SDK • Web technologies: PHP, HTML, CSS, Javascript • R • Java • Neuroph: Java neural networkframework

  22. 12. Work Plan

  23. Application for Continuous Health Monitoring using Machine-to-Machine CommunicationsFebruary 2012 JoãoPrudêncio Supervisors: Ana Aguiar, Daniel Lucani

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