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Loss Recovery for Mobile Healthcare Applications

R. Gunasundari, R. Arthi, R. Geetha

Abstract


The current healthcare systems, structured for reacting to crisis and managing illness are facing new challenges. Traditionally, healthcare services are provided by using wired networks such as telephones, and DSL or cable-modem-based broadband access systems to transmit biomedical data between a hospital and the point of care. However, these fixed systems have limitations in providing services to patients in remote localities and when the patients are mobile. Therefore, mobile healthcare services with applications in emergency healthcare have become popular to provide prompt and effective patient care. Wireless transmission of medical signals has high error rates. The bio –medical signals, where every second of data could mean abnormal patterns, cannot tolerate such losses. One of the solutions to overcome such losses Compressed Sensing (CS), an emerging signal processing technique that is used to recover the losses that occurs in the healthcare networks. CS enables the reconstruction of the signals if any data are omitted at the sender or dropped by the communication channel. Computer simulation results demonstrate that the proposed scheme may serve as an efficient procedure to recover the data loss that occurs in mobile health care applications.

Keywords


Mobile Healthcare Applications, Loss Recovery, Compressed Sensing (Cs), Matching Pursuit (Mp)

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References


N.K.G. Samaraweera “Non-congestion packet loss detection for TCP error recovery using wireless links”, IEE Proceedings Communication, Vol.146, No.4,pp.222-230,August 1999.

FeiHu, YangXiao, QiHao, “Congestion-Aware, Loss-Resilient Bio-Monitoring Sensor Networking for Mobile Health Applications”, IEEE Journal on Selected Areas in Communications, Vol. 27, No. 4, pp. 450-465, May, 2009.

V. Shnayder, B. Chen, K. Lorincz, T. R. F. Fulford-Hones, and M. Welsch. “Sensor networks for medical care”, Harvard University Technical Report 2005.

C. Chigan and V. Oberoi, “ Providing QoS in ubiquitous telemedicine networks”, Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW‟06), Pisa, Italy March 2006.

D. J. Vergados, D. D. Vergados, and I. Maglogiannis,” Applying wireless diffserv for QoS provisioning in mobile emergency telemedicine”, Proceedings of IEEE Global Telecommunications Conference , Karlovassi Samos, Greece, pp. 1–5, November, 2006.

G. Dimic, N. D. Sidiropoulos, and R. Zhang, "Medium access control physical cross-layer design," IEEE Signal Processing Magazine., pp. 40-50, September, 2004.

A. Shiozaki, "Adaptive type-II hybrid broadcast ARQ system," IEEE Transactions on Communication Networks, vol. 44, no. 4, pp. 420-422, April, 1996.

D. Donoho. “Compressed sensing”, IEEE Transactions on Biomedical Engineering, vol. 52, pp. 1289–1306, April 2006.

E. Candes, J. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements”, Communications on Pure and Applied Mathematics, vol. 59, pp. 1207–1223, August 2006.

IEEE Signal Processing Magazine [Sensing, Sampling, and Compression], vol. 25. IEEE Signal Processing Magazine, March 2008.

P. K. Baheti , H. Garudadri, “Heart rate and blood pressure estimation from compressively sensed photoplethysmograph”, Proceedings of the Fourth International Conference on Body Area Networks , Los Angeles, California, August, 2009.

P. K. Baheti , H. Garudadri,” An ultra low power pulse oximeter sensor based on compressed sensing”, Proceedings of 6th International Workshop on Wearable and Implantable Body Sensor Networks, Berkeley, CA, USA, June 2009.

Harinath Garudadri , Pawan K. Baheti, ” Packet Loss Mitigation for Biomedical Signals in Healthcare Telemetry”, Proceedings of 31st Annual International Conference of the IEEE EMBS, Minnesota, USA, September 2-6, 2009.

Candès, E. J., Romberg, J., Tao, T., “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information”, IEEE Transactions on Information Theory, vol. 52, no.6, pp. 489–509, June, 2006.

Donoho, D. L., Huo, X., “Uncertainty principles and ideal atomic decomposition”, IEEE Transactions on Information Theory, vol. 47, no.5, , 2845–2862, September,2001.

Feuer,A., Nemirovski, A.,” On sparse representation in pairs of bases”, IEEE Transmation on Information Theory, vol.49, no.3, pp.1579–1581, March,2003..

Candès, E. J., Tao, T., “Decoding by linear programming”, IEEE Transactions on Information Theory, vol. 51,pp. 4203–4215,2005.


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