ABSTRACT
In the present growing era advancement of computer processing power, data communication capabilities, low power micro electronics devices and micro sensors increases the popularity of wireless sensor network in real life. Body area sensor network is a group of sensor nodes inside and outside the human body for continuous monitoring of health conditions, behavior and activities. Context awareness in pervasive health care is a proactive approach which is different from a conventional event-driven model (for example: visiting doctor when sick) and here we are continuously monitoring a patient health conditions through the use of Body area sensor network. This paper presents a layered architecture of Wide Area Wireless Sensor Body Area Network (WA-WSBAN) along with data fusion techniques, standards and sensor network hardware requirement for context awareness.
A BodyMedia sensor dataset collected from 9 different sensor nodes has been used to classify the user activities with reference to different sensor readings. The context information derived from the proposed Wide Area Wireless Sensor Body Area Network (WA-WSBAN) architecture may be used in pervasive healthcare monitoring to detect various events and accurate episodes and unusual patterns and activities obtained from the study can be marked for later review. In this research work patient activity and gender classification has been done by using one to all and multi kernel based support vector data classification. The similar practices may be utilized for the study of various observations in real time health care applications and proactive measures may be initiated based on results obtained from data classification.
- Liolios et. al., 2010. An Overview of Body Sensor Networks in Enabling Pervasive Healthcare and Assistive Environments. PETRA '10, June 23--25, 2010, Samos, Greece. Copyright © 2010 ACM ISBN 978-1-4503-0071-1/10/06. Google ScholarDigital Library
- Andrew D. Jurik, Alfred C. Weaver, 2009. Body Sensors: Wireless Access to Physiological Data. IEEE Software, vol. 26, no. 1, pp. 71--73, Jan./Feb. 2009, doi:10.1109/MS.2009.5. Google ScholarDigital Library
- DeVaul, R. W., Sung, M., Gips, J., and Pentland, S., MIThril 2003. Applications and Architecture. In Proceedings of ISWC 2003, (White Plains, U.S.A., 2003). Google ScholarDigital Library
- Georgios Kambourakis et. al. 2007. Securing Medical Sensor Environments: The CodeBlue Framework Case. Second International Conference on Availability, Reliability and Security (ARES'07) 0-7695-2775-2/07. Google ScholarDigital Library
- Harvard University CodeBlue project: Wireless Sensor Networks for Medical Care. Available: http://www.eecs.harvard.edu/~mdw/proj/codeblue/Google Scholar
- G. Virone, A. Wood, L. Selavo, Q. Cao, L. Fang, T. Doan, Z. He, R. Stoleru, S. Lin, and J. A. Stankovic, 2006. An Advanced Wireless Sensor Network for Health Monitoring at Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare (D2H2), Arlington, VA, April 2--4, 2006.Google Scholar
- Ali Peiravi. 2010. Reliability of Wireless Body Area Networks used for Ambulatory Monitoring and Health Care, Life Science Journal, Volume (7), 2010--6.Google Scholar
- Brown L, et. al. 2009. Body area network for monitoring autonomic nervous system responses. This paper appears in: Print ISBN: 978-963-9799-42-4, Digital Object Identifier: 10.4108/ICST.PERVASIVEHEALTH2009.5973Google Scholar
- Wac, K., Bults, R., et al. 2004. Mobile Health Care over 3G Networks: The MobiHealth Pilot System and Service. Global Mobile Congress, Shanghai, China.Google Scholar
- Soo-Min Woo, Hye-Jin Lee, Bub-Joo Kang, Sang-Woo Ban. 2010. ECG signal monitoring using one-class support vector machine, Proceedings of the 9th WSEAS international conference on Applications of electrical engineering, p.132--137, March 23--25, 2010, Penang, Malaysia Google ScholarDigital Library
- Shyamal Patel, Konrad Lorincz, Richard Hughes et al. 2009. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors., 864--73. In IEEE Transactions on Information Technology in Biomedicine 13 (6). Google ScholarDigital Library
- M. EIHelw, et. al. 2009. An Integrated Multi-Sensing Framework for Pervasive Healthcare Monitoring Digital Object Identifier: 10.4108/ICST. PERVASIVEHEALTH2009.6038 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05191197Google Scholar
- Korel, B. T.; Koo, S. G. M.; 2007. Addressing Context Awareness Techniques in Body Sensor Networks This paper appears in: Advanced Information Networking and Applications Workshops, 2007, ISBN: 978-0-7695-2847-2 Digital Object Identifier: 10.1109/AINAW.2007.69 Google ScholarDigital Library
- Anthony et. al., 2010. SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results IEEE{2010} http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5352277Google Scholar
- Guang-Zhong Yang et al., 2006. Body Sensor networks" ISBN-10: 1-84628-272-1, ISBN-13: 978-1-84628-272-0, © Springer-Verlag London Limited 2006.Google Scholar
- Hairong Dong, David Evans, 2007. Data-Fusion Techniques and Its Application. Fuzzy Systems and Knowledge Discovery, Fourth International Conference on, vol. 2, pp. 442--445, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Vol.2, 2007. Google ScholarDigital Library
- Jitendra R. Raol. 2006. Multi Sensor Data Fusion with Matlab. CRC Press International Standard Book Number: 978-1-4398-0003-4 (Hardback). Google ScholarDigital Library
- Wu, W. H. Batalin, M. A. Au, L. K.; Bui, A. A. T. Kaiser, W. J. 2007. Context-aware Sensing of Physiological Signals. 29th Annual International Conference of the IEEE, vol., no., pp.5271--5275, 22--26 Aug. 2007 doi: 10.1109/IEMBS.2007.4353531Google Scholar
- Benny P. L. Lo; Guang-Zhong Yang; 2006. Body Sensor Networks: Infrastructure for Life Science Sensing Research. Life Science Systems and Applications Workshop, IEEE/NLM, vol., no., pp.1--2, July 2006.Google Scholar
- Sunseri, M. The SenseWear armband as a Sleep Detection Device. http://www.bodymedia.com/Learn-More/Whitepapers/The-SenseWear-armband-as-a-Sleep-Detection-Device.Google Scholar
- Craig B. Liden et. al. Characterization and Implications of the Sensors Incorporated into the SenseWearÔ Armband for Energy Expenditure and Activity Detection. www.bodymedia.com/site/docs/papers/Sensors.pdfGoogle Scholar
- Jin-Shyan Lee; Yu-Wei Su; Chung-Chou Shen. 2007. A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi. 33rd Annual Conference of the IEEE, vol., no., pp.46--51, 5--8 Nov. 2007 doi: 10.1109/IECON.2007.4460126Google Scholar
- Mukherji, R.; Egyhazy, C.; Johnson, M. 2002 Architecture for a large healthcare information system. IT Professional, vol.4, no.6, pp. 19--27, Nov/Dec 2002 doi: 10.1109/MITP.2002.1114843 Google ScholarDigital Library
- Joonyoung Jung et al. 2006.Wireless Body Area Network in a Ubiquitous Healthcare System for Physiological Signal Monitoring and Health Consulting International Journal of Advanced Science and Technology http://www.sersc.org/journals/IJSIP/vol1_no1/papers/06.pdfGoogle Scholar
- John Shawe-Taylor & Nello Cristianini 2000. Support Vector Machines and other kernel-based learning methods. Cambridge University Press. Google ScholarDigital Library
- V. Vapnik. 1998 Statistical Learning Theory, Wiley.Google Scholar
- C. W. Hsu and C.-J. Lin 2002. A comparison of methods for multi-class support vector machines, IEEE Transactions on Neural Networks, 13:415--425. Google ScholarDigital Library
- J. Weston and C. Watkins, MultiClass Support Vector Machines.Google Scholar
- In M. Verleysen, 1999 Proceedings of ESANN99, Brussels. D. Facto Press.Google Scholar
- Yi Liu; Zheng, Y. F. 2005. One-against-all multi-class SVM classification using reliability measures. IEEE International Joint Conference on, vol.2, no., pp. 849--854 vol. 2, 31 July--4 Aug. 2005Google ScholarCross Ref
- Lingyun Zou; Zhengzhi Wang. 2007 Microarray Gene Expression Cancer Diagnosis Using Multiclass Support Vector Machines. The 1st International Conference on, vol., no., pp.260--263, 6--8 July 2007 doi: 10.1109/ICBBE.2007.70Google Scholar
- Yoonkyung Lee, Yi Lin, & Grace Wahba. 2004. Multicategory Support Vector Machines: Theory and Application to the Classi. cation of Microarray Data and Satellite Radiance Data © 2004 American Statistical Association Journal of the American Statistical Association March 2004, Vol. 99, No. 465.Google Scholar
- Physiological Data Modeling Contest http://www.cs.utexas.edu/~sherstov/pdmc/Google Scholar
- Varshney, Upkar 2005. Pervasive Healthcare: Applications, Challenges And Wireless Solutions. Communications of the Association for Information Systems: Vol. 16, Article 3. Available at: http://aisel.aisnet.org/cais/vol16/iss1/3Google Scholar
Index Terms
- SVM based context awareness using body area sensor network for pervasive healthcare monitoring
Recommendations
Node Placement Strategy in Wireless Sensor Network
The performance and quality of services in wireless sensor networks WSNs depend on coverage and connectivity. Node placement is a fundamental issue closely related to the coverage and connectivity in sensor networks. Node placement influences the target ...
Application specific study, analysis and classification of body area wireless sensor network applications
The evolution of wearable computing and advances in wearable sensor devices has motivated various applications of Body Area Sensor Networks (BASN). In the last few years body areas sensor networks have emerged as a major type of wireless sensor networks (...
System architecture of a wireless body area sensor network for ubiquitous health monitoring
Recent technological advances in sensors, low-power microelectronics and miniaturization, and wireless networking enabled the design and proliferation of wireless sensor networks capable of autonomously monitoring and controlling environments. One of ...
Comments