Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a significant opportunity for improving the quality of health care services. Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Our approach focused towards handling all kinds of vital signals like ECG, EMG, SpO2 etc. which previous work was not supporting. While predictions the framework considers all parameters like patient history, domain expert’s rules and continuously monitored realtime signals. Implementation and results of applying clustering algorithms (Graph theoretic, K-means) on patient’s historical health data for forming the health rule base are discussed here. The framework has been designed to perform the analysis on the instantaneous and stream (continuous) data over a sliding time window. The comparative analysis on vital signals made from various clustering algorithms adds extra dimension to global risk alerts and help doctors to diagnose more accurately

1. H.-C. Wu, C.-H. Lin, K.-C. Wang, S.-C. Wang, C.-H. Chen, S.-T. Young and T.-S. Kuo, “A mobile system for real-time patient-monitoring with integrated physiological signal processing,” in Proc. 1st Joint BMES/Eng. Med. Biol. Soc. Conf., Atlanta, GA, 1999, p. 712.
2. Y.-H. Lin, I.-C. Jan, P. Chow-InKo,Y.-Y. Chen, J.- M.Wong, and G.-J. Jan, “A wireless PDA-based physiological monitoring system for patient transport,” IEEE Trans. Inf. Technol. Biomed., vol. 8, no. 4, pp. 439– 447, Dec. 2004.
3. R.-G. Lee, K.-C. Chen, C.-C. Hsiao and C.-L. Tseng, “A mobile care system with alert mechanism,” IEEE Trans. Inf. Technol. Biomed., vol. 11, no. 5, pp. 507–517, Sep. 2007.
4. N. Saranummi, “Information technology in biomedicine,” IEEE Trans.Biomed. Eng., vol. 49, no. 12, pp. 1385–1386, Dec. 2002.
5. K. Lorincz, D. J. Malan, T. R. F. Fulford-Jones, A. Nawoj, A. Clavel, V. Shnayder, G. Mainland, M. Welsh, and S. Moulton, “Sensor networks for emergency response: Challenges and opportunities,” IEEE Pervasive Comput., vol. 3, no. 4, pp. 16–23, Oct. 2004.
6. B. G. Cellar, N. H. Lovell and J. Basilakis, .Using information technology to improve the management of chronic disease. Med. J. of Australia, 2003; 179 (5): 242- 246.
7. J. Han and M. Kam ber, Data Mining: Concepts and Techniques (MorganKaufmann Series in Data Management Systems). San Mateo, CA: Morgan Kaufmann, 2000.
8. Selim Aksoy and Robert M. Haralick, “Graph Theoretic Clustering for Image Grouping and Retrieval”, Intelligent Systems Laboratory, Department of Electrical Engineering,University of Washington Seattle, WA 98195- 2500
9. Daniele Apiletti, Elena Baralis, Member, IEEE, Giulia Bruno, and Tania Cerquitelli, “Real-Time Analysis of Physiological Data to Support Medical Applications”, IEEE transactions on information technology in biomedicine, vol. 13, NO. 3, MAY 2009, pp 313-321
10. Hanady Abdulsalam, David B. Skillicorn, Patrick Martin, “Classification Using Streaming Random Forests”, IEEE transactions on knowledge and data engineering, vol. 23, no. 1., pp 22-36
11. Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani Thuraisingham, “ Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints ”, IEEE transactions on knowledge and data engineering, accepted for publication in march 2010
12. V.Kavitha ,M.Punithavalli, “Clustering Time Series Data Stream – A Literature Survey”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 1, April 2010, pp 289-294
13. Brian Foo and Mihaela van der Schaar, “A Distributed Approach for Optimizing Cascaded Classifier topologies in Real-Time Stream Mining Systems”, IEEE transactions on Image processing, VOL. 19, NO. 11, November 2010 , pp 3035-3048
14. Shaoning Pang, Seiichi Ozawa, Nikola Kasabov, “Incremental Linear Discriminant Analysis for Classification of Data Streams”, IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 35, NO. 5., pp 905-914
15. L.Cohen, et al., "Incremental Classification of Nonstationary Data Streams," Porto, Portugal, 2005, pp. 117-124.
16. Rajanish Dass, Varun Kumar, “Kaal – a Real Time Stream Mining Algorithm”,In Proc. IEEE 43rd Hawaii International Conference on System Sciences – 2010
17. The MIMIC database on PhysioBank (2007, Oct.) [Online]. Available: http://www.physionet.org/physiobank/database/mimicdb.

Real time data stream mining K-means Graph Theoretic Vital signal processing in WMRHM