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Disruption-Tolerant Wireless Sensor Networking for Biomedical Monitoring in Outdoor Conditions

Monitoring the Cardiac Activity of Marathon Runners using DTN Techniques

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Abstract

Off-the-shelf wireless sensing devices open up interesting perspectives for biomedical monitoring. Yet because of their limited processing and transmission capacities most applications considered to date imply either indoor real-time data streaming, or ambulatory data recording. In this paper we investigate the possibility of using disruption-tolerant wireless sensors to monitor the biomedical parameters of athletes during outdoor sports events. We focus on a scenario we believe to be a most challenging one: the ECG monitoring of runners during a marathon race, using off-the shelf sensing devices and a limited number of base stations deployed along the marathon route. Field experiments conducted during intra-campus sports events show that such a scenario is indeed viable, although special attention must be paid to supporting episodic, low-rate transmissions between sensors carried by runners and roadside base stations.

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References

  1. Chen M, Gonzalez S, Vasilakos A, Cao H, Leung VCM (2011) Body area networks: a survey. J Mob Netw Appl 16(2):171–193. Springer, ACM

    Article  Google Scholar 

  2. Alemdar H, Ersoy C (2010) Wireless sensor networks for healthcare: a survey. Comput Netw 54(15):2688–2710

    Article  Google Scholar 

  3. Konstantas D, Herzog R (2003) Continuous monitoring of vital constants for mobile users: the MobiHealth approach. In: 25th annual international conference of the IEEE EMBS, pp 3728–3731

  4. Benferhat D, Guidec F, Quinton P (2012) Disruption-tolerant wireless sensor networking for biomedical monitoring in outdoor conditions. In: 7th International conference on body area networks (BODYNETS’12). ACM Digital Library, Oslo, pp 1–7 September 2012

  5. Benferhat D, Guidec F, Quinton P (2012) Cardiac monitoring of marathon runners using disruption-tolerant wireless sensors. In: 6th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI’12). LNCS, Vitoria-Gasteiz, Spain, 1–8 December 2012

  6. Fall K (2004) Messaging in Difficult Environments. Technical report, Intel Research Berkeley

  7. Babovic Z, Crnjin A, Racocevic G, Stankovic M, Peric Z, Cirkovic I, Damjanovic I, Milutinovic V (2009) ProSense research activities in Belgrad. In: 9th international conference on telecommunication in modern satellite, cable, and broadcasting services (TELSIKS’09). IEEE, pp 291–294

  8. Konstantas D, Jones V, Herzog R (2002) MobiHealth innovative 2.5-3G mobile services and applications for healthcare. In: Proceedings of the 11th Information Society Technologies (IST) mobile and wireless telecom, pp 43–52

  9. Tsiknakis M, Katehakis DG, Orphanoudakis SC (2004) A health information infrastructure enabling secure access to the life-long multimedia electronic health record. In: CARS, pp 289–294

  10. Klompmaker F, Workowski A, Thronicke W, Ostermair F, Willemsen D, Hoffmann JD (2012) User centered design of an interactive mobile assistance and supervision system for rehabilitation purposes. In: 3rd international conference on wireless mobile communication and healthcare (MobiHealth’12), Springer, Paris, 1–10 November 2012

  11. Fall K (2003) A delay-tolerant network architecture for challenged internets. In: Proceedings of ACM SIGCOMM03

  12. Voyiatzis AG (2012) A survey of delay- and disruption-tolerant networking applications. J Internet Eng 5(1):331–344

    Google Scholar 

  13. Pisztor B, Musolesi M, Mascolo C (2007) Opportunistic mobile sensor data collection with SCAR. In: Proceedings of IEEE international conference on mobile adhoc and sensor systems (MASS07), IEEE Press, pp 1–22

  14. Jain S, Shah R, Brunette W, Borriello G, Roy S (2006) Exploiting mobility for energy efficient data collection in wireless sensor networks. MONET 11(3):327–339

    Google Scholar 

  15. Wang Y, Wu H, Lin F, Tzeng NF (2008) Cross-Layer protocol design and optimization for delay/fault-tolerant mobile sensor networks (DFT-MSN’s). J Sel Areas Commun 26(5):809–819

    Article  Google Scholar 

  16. Nayebi A, Sarbazi-Azad H, Karlsson G (2009) Routing, data gathering, and neighbor discovery in delay-tolerant wireless sensor networks. In: 23rd IEEE international symposium on parallel and distributed processing, IPDPS 2009, Rome, Italy, 23-29, 2009, IEEE CS pp 1–6

  17. Syed-Abdul S, Scholl J, Lee P, Jian WS, Liou DM, Li YC (2012) Study on the potential for delay tolerant networks by health workers in low resource settings. Comput Methods Prog Biomed 107(3):557–564

    Article  Google Scholar 

  18. Burns A, Greene B, McGrath M, O’Shea T, Kuris B, Ayer S, Stroiescu F, Cionca V (2010) SHIMMER: a wireless sensor platform for noninvasive biomedical research. IEEE Sensors J 10(9):1527–1534

    Article  Google Scholar 

  19. Levis P,Madden S, Polastre J, Szewczyk R,Woo A, Gay D, Hill J, WelshM, Brewer E, Culler D (2005) TinyOS: an operating system for sensor networks. Ambient Intelligence, Springer, pp 115–148

  20. Singh BN, Tiwari AK (2006) Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process 16(3):275–287

    Article  Google Scholar 

  21. Hossein M, Nadia K, Pierre V (2011) Real-time compressed sensing-based electrocardiogram compression on energyconstrained wireless body sensors. In: IEEE international symposium on circuits and systems (ISCAS2011), IEEE CS pp 1–4

  22. Madzarov G, Dordevic D (2010) Heartbeat tracking application for mobile devices - arrhythmia recognition module. In: 32nd international conference on information technology interfaces (ITI2010), IEEE CS, pp 585–590

  23. Boichat N, Khaled N, Rincn FJ, Atienza D (2009) Wavelet-based ECG delineation on a wearable embedded sensor platform. In: 6th international workshop on wearable and implantable body sensor networks (BSN09), IEEE CS, pp 256–261

  24. Farshchi S, Nuyujukian PH, Pesterev A, Mody I, Judy JW (2005) A TinyOS-based wireless neural sensing, archiving, and hosting system. In: 2nd international IEEE EMBS conference on neural engineering, IEEE CS, pp 671–674

  25. IEEE802.15.4-2006 Part 15.4 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs). IEEE Standard

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Acknowledgments

The authors would like to thank Alan Srey who re-designed and implemented part of the Android application during a summer internship, and participated in some of the experiments whose results are presented in this paper.

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Correspondence to Frédéric Guidec.

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Guidec, F., Benferhat, D. & Quinton, P. Disruption-Tolerant Wireless Sensor Networking for Biomedical Monitoring in Outdoor Conditions. Mobile Netw Appl 19, 684–697 (2014). https://doi.org/10.1007/s11036-013-0491-6

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  • DOI: https://doi.org/10.1007/s11036-013-0491-6

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