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;

• 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)

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.

3. Objectives

4. System architecture

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)

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)

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.

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

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

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

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

8. Geo Fencing

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

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

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

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.

9. Human Activity Recognition

New features proposal

Stage 1

Stage 2

9. Human Activity Recognition

New features proposal

Stage 3

Stage 4

10. Activity Data Acquisition

6 individuals10 hours of activity

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

12. Work Plan

Application for Continuous Health Monitoring using

Machine-to-Machine Communications

February 2012

João Prudêncio

Supervisors: Ana Aguiar, Daniel Lucani