Machine to Machine communications CS 441 SIUC Seonguk Heo Presentation.
Application for Continuous Health Monitoring using Machine-to-Machine Communications February 2012
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Transcript of Application for Continuous Health Monitoring using Machine-to-Machine Communications February 2012
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Application for Continuous Health Monitoring using
Machine-to-Machine Communications
February 2012
João Prudêncio
Supervisors: Ana Aguiar, Daniel Lucani
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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)
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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.
1 Shin, Donghoon. 2011. M-healthcare revolution: an e-commerce perspective. Paper presented at First ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering, May 23-25.
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3. Objectives
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4. System architecture
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5. Example of applications 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)
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5. Example of applications 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)
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6. Machine-to-Machines Communications• 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 Rongxing Lu; Xu Li; Xiaohui Liang; Xuemin Shen; Xiaodong Lin; , "GRS: The green, reliability, and security of emerging machine to machine communications," Communications Magazine, IEEE , vol.49, no.4, pp.28-35, April 20112 Geng Wu; 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, April 20113 ETSI(The European Telecommunications Standards Institute). 2011. Draft ETSI TR 102 732 V0.4.1. Machine to Machine Communications (M2M): Use cases of M2M applications for eHealth. France: The European Telecommunications Standards Institute.
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6. Machine-to-Machines Communications
Shao-Yu Lien; Kwang-Cheng Chen; Yonghua Lin; , "Toward ubiquitous massive accesses in 3GPP machine-to-machine communications," Communications Magazine, IEEE , vol.49, no.4, pp.66-74, April 2011
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7. Heart abnormalities
• Bradycardia: heart rate less than 60 bps;
• Tachycardia: heart rate greater that 100 bps;
• QRS complexes: QRS interval greater than 120 miliseconds and heart rate greater than 100 bps;
• Supraventricular tachycardia with narrow QRS complexes: QRS interval less than 120 miliseconds and heart rate greater than 100 bps.
Liszka, K.J.; Mackin, M.A.; Lichter, M.J.; York, D.W.; Dilip Pillai; Rosenbaum, D.S.; , "Keeping a beat on the heart," Pervasive Computing, IEEE , vol.3, no.4, pp. 42- 49, Oct.-Dec. 2004Yonglin Ren; Pazzi, R.W.N.; Boukerche, A.; , "Monitoring patients via a secure and mobile healthcare system," Wireless Communications, IEEE , vol.17, no.1, pp.59-65, February 2010
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8. Geo Fencing
• 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.; , "Developing smartphone applications for people with Alzheimer's disease," Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on , vol., no., pp.1-5, 3-5 Nov. 20102 Bilgic, Hasan Tahsin; Alkar, Ali Ziya; , "A secure tracking system for GPS-enabled mobile phones," Information Technology and Multimedia (ICIM), 2011 International Conference on , vol., no., pp.1-5, 14-16 Nov. 20113 Alotaibi, F.D.; Abdennour, A.; Ali, A.A.; , "A Real-Time Intelligent Wireless Mobile Station Location Estimator with Application to TETRA Network," Mobile Computing, IEEE Transactions on , vol.8, no.11, pp.1495-1509, Nov. 2009
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8. Geo Fencing
• Ray casting algorithm;• Simple polygons not self-
interconnected 1.
If F = 1 then it’s an internal point If F = 0 then it’s an external point
P: P3P4, P4P5, P5P6 and P6P7.F(P) = 1+(-1)+1+(-1)
f(ei ) has the value of: -1, if ei crossed up to down; 1, if ei crossed down to up; 0, if ei not crossed .
Wu Jian; Cai Zongyan; , "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
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8. Geo Fencing
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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. 2008Khan, 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
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9. Human Activity Recognition
Autoregressive Modeling
• Linear prediction methods: predicts the output based on previous inputs1 2;
• Finite impulse response (FIR) filter;
• Methods: The least squares; Yule-Walker ; Burg’s 3 4.
1 C.Jennings M.Kulahci Montgomery,C.Douglas. Introduction toTime Series Analysis and Forecasting.John Wiley and Sons.Inc.,first edition,20082 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. 20103 H.Schoonewelle M.J.L.De Hoon,T.H.J.J.Van Der Hagenand H.Van Dam. Why Yule-Walker should not be used for autoregressive modelling.4 K. Roth, I. Kauppinen,P.A.A.Esquef,and V.Valimaki. Frequency warped Burg’s method for AR-modeling.
Y(t) original signal a(i) unknown coefficientsP the order of the modelE(t) residual error
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9. Human Activity Recognition
Signal Magnitude Area (SMA)
• Analyze the magnitude of the variations of the signal;
• Distinguish between static and dynamic activities 1 2.
Where x(i), y(i), z(i) : acceleration in the x,y,z axis at the time i
1 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. 20082 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
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9. Human Activity Recognition
1 Karantonis, D.M.; Narayanan, M.R.; Mathie, M.; Lovell, N.H.; Celler, B.G.; , "Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring," Information Technology in Biomedicine, IEEE Transactions on , vol.10, no.1, pp.156-167, Jan. 20062 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.; Van Lummel, R.C.; , "Detection of static and dynamic activities using uniaxial accelerometers,"Rehabilitation Engineering, IEEE Transactions on , vol.4, no.4, pp.375-385, Dec 1996
Tilt Angle
• Angle between the vector of gravity and the z axis 1 2;
• Distinguish between static activities: sitting and lying 3.
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9. Human Activity Recognition
New features proposal
Stage 1
Stage 2
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9. Human Activity Recognition
New features proposal
Stage 3
Stage 4
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10. Activity Data Acquisition
6 individuals10 hours of activity
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11. Technologies• Machine-to-Machine Communications• The Extensible Messaging and Presence
Protocol (XMPP)• MyContext: Context Framework developed by PT
Inovação
• Android SDK• Web technologies: PHP, HTML, CSS, Javascript
• R
• Java
• Neuroph: Java neural network framework
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12. Work Plan
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Application for Continuous Health Monitoring using
Machine-to-Machine Communications
February 2012
João Prudêncio
Supervisors: Ana Aguiar, Daniel Lucani