Skip to main content
SpringerLink
Log in

Challenges in Real-Time Vital Signs Monitoring for Persons During Exercises

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

Although there have been a variety of wearable Information and Communication Technology (ICT) devices around us, which are easily connectable to smart phones, unfortunately, very few people are practicing everyday healthcare using them. There must be some causes in it. This paper examines educational and literate causes in the impracticality of everyday healthcare using wearable ICT devices, which may be inherent to Japan, and emphasizes the importance of real-time vital signs monitoring for schoolchildren in classroom learning and physical training, namely, persons during exercises. Then, the paper points out technical problems in its realization in terms of vital sensing and wireless networking, and introduces some solutions which we have been making up to the present. And finally, the paper shows some challenges of the future towards realization of real-time vital signs monitoring for schoolchildren during physical training, with the possibility of wireless multi-hop networking taking the mobility and location of vital sensor nodes into consideration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. http://www.worldrugby.org/rwc2019

  2. https://tokyo2020.jp/en/

  3. http://www.wmg2021.jp/en/

  4. http://www.mhlw.go.jp/english/index.html

  5. J. O. Prochaska and W. F. Velicer, The Transtheoretical Model of Health Behavior Change, Am. J. Health Promot., Vol. 12, No. 1, pp. 38–48, 1997.

    Article  Google Scholar 

  6. METI of Japan, “Correspondence to aging society - Application of personal data to the field of medicine and health care (in Japanese),” Proc. 1st Strategic Meeting on the Application of Health Care Data, Japan Association of Applied IT Healthcare, Oct. 2015.

  7. http://www.healthliteracy.jp/

  8. http://www.mext.go.jp/a_menu/shotou/new-cs/youryou/eiyaku/1261037.htm

  9. http://www.mext.go.jp/a_menu/shotou/new-cs/youryou/eiyaku/1298356.htm

  10. http://news-de-smile.com/post-257-257

  11. http://www.asahi.com/articles/ASH7B5FCHH7BPTIL01N.html

  12. OECD, “A Teachers’ Guide to TALIS (Teaching and Learning International Survey),”OECD, 2013.

  13. W. L. Kenny, J. H. Wilmore and D. L. Costill, Physiology of Sport and Exercise, Human Kinetics, 1999.

  14. M. Karvonen, K. Kentala and O. Mustala, The effects of training on heart rate: a longitudinal study, Ann. Med. Exp. Biol. Fenn, Vol. 35, No. 3, pp. 307–315, 1957.

    Google Scholar 

  15. S. Hara, et al., Real-time vital monitoring for persons during exercises—Solutions and Challenges–, IEICE Trans. Commun, Vol. E99–B, No. 3, pp. 556–564, Mar. 2016.

  16. K. Nemoto, et al., Effects of high-intensity interval walking training on physical fitness and blood pressure in middle-aged and older people, Mayo Clinic Proceedings, Vol. 82, No. 7, pp. 803–811, July 2007.

  17. http://www.mext.go.jp/b_menu/houdou/27/08/1360707.htm, 2015.

  18. K. Yamaji, Science of Heart Rate for Exercise Prescription (in Japanese), Taishukan, 1994.

  19. www.wi-fi.org/

  20. www.zigbee.org/

  21. www.bluetooth.com

  22. http://www.memsic.com/

  23. https://www.wi-sun.org/

  24. http://www.wi-fi.org/discover-wi-fi/wi-fi-halow

  25. http://www.3gpp.org/

  26. G. Wu, et al., M2M: From mobile to embedded internet, IEEE Commun. Mag., Vol. 49, No. 4, pp. 36–43, Apr. 2011.

  27. http://www.ecomott.co.jp/cloudlogger-terminal.html

  28. C. E. Perkins, Ad Hoc Networking, Upper Saddle River, NJ, USA: Addison-Wesley, 2001.

  29. 802.15.6-2012 - IEEE Standard for Local and metropolitan area networks-Part 15.6: Wireless Body Area Networks, IEEE, 2012.

  30. S. Abbas, Y. Ranga and K. Esselle, “Reconfigurable antenna options for 2.45/5 GHz wireless body area networks in healthcare applications, ff IEEE EMBC 2015, pp. 5465–5468, Milan, Italy, 25–29 Aug. 2015.

  31. H. Su and X. Zhang, “Battery-dynamics driven TDMA MAC protocols for wireless body-area monitoring networks in healthcare applications, ff IEEE, J. Sel. Areas Commun., Vol. 27, No. 4, pp. 424–434, Apr. 2009.

  32. S. Manfredi, “Congestion control for differentiated healthcare service delivery in emerging heterogeneous wireless body area networks, ff IEEE Trans, Wireless Commun., Vol. 21, No. 2, pp. 81–90, Feb. 2014.

  33. A. Awad, A. Mohamed and A. El-Sherif, “Energy efficient cross-layer design for wireless body area monitoring networks in healthcare applications, ff Proc. IEEE PIMRC 2013, pp. 1484–1489, London, UK, 8–11 Sep. 2013.

  34. D. Hoang, et al., “Thermal energy harvesting from human warmth for wireless body area network in medical healthcare system, ff Proc. IEEE PEDS 2009, pp. 1277–1282, Taipei, Taiwan, 2–5 Nov. 2009.

  35. S. Lim, et al., “Security issues on wireless body area network for remote healthcare monitoring, ff IEEE SUTC 2010, pp. 327–332, Newport Beach, CA, USA, 7–9 June 2010.

  36. J. Wan, et al., “Cloud-enabled wireless body area networks for pervasive healthcare, ff, IEEE Network, Vol. 27, No. 5, pp. 56–61, May 2013.

  37. O. Salem, et al., “Online anomaly detection in wireless body area networks for reliable healthcare monitoring, ff IEEE, J. Biomed. Health Inform., Vol. 18, No. 5, pp. 1541–1551, May 2014.

  38. M. Chen, et al., Body area networks: A survey, Mobile Networks and Applications, Vol. 16, No. 2, pp. 171–193, Apr. 2011.

  39. T. Shimazaki and S. Hara, “Cancellation of motion artifact induced by exercise for PPG-based heart rate sensing,” Proc. IEEE EMBC 2014, pp. 3216–3219, Chicago, USA, 26–30 Aug. 2014.

  40. T. Shimazaki and S. Hara, “Design of PPG-based heart rate sensor enabling motion artifact cancellation,” Proc. SASIMI 2015, http://sasimi.jp/new/sasimi2015/files/archive/pdf/p283_R3-14, Yilan, Taiwan, 16–17 Mar. 2015.

  41. T. Shimazaki and S. Hara, “Breathing motion artifact cancellation in PPG-based heart rate sensing,” Proc. ISMICT 2015, pp. 200–203, Kamakura, Japan, 24–26 Mar. 2015.

  42. T. Shimazaki, et al., “Motion artifact cancellation and outlier rejection for a clip-type PPG-based heart rate sensor,” Proc. IEEE EMBC 2015, pp. 2026–2029, Milan, Italy, 25–29 Aug. 2015.

  43. M. Asano, et al., “Development of an exercise meter using triaxial acceleration data,” Proc. IEEE EMBC 2006, pp. 3731–3734, New York, USA, 31 Aug.–3 Sep. 2006.

  44. M. Miyatake, et al., and S. Hara, “VO2 estimation using 6-axis motion sensing data,” Proc. ISMICT 2015, in CD-ROM, Worcester, USA, 20–23 Mar. 2016.

  45. S. Hara, et al., “A Real-time vital sensing system for persons during exercises,” Proc. IEEE ICCE 2016-Berlin, in CD-ROM, Berlin, Germany, 5–7 Sep. 2016.

  46. S. Hara et al., “Development of a real-time vital data collection system from players during a football game,” Proc. IEEE HealthCom 2013, pp. 409–414, Lisbon, Portugal, 9–12 Oct. 2013.

  47. https://www.polar.com/ja/b2b_products/team_sports/team_pro

  48. http://preview.thenewsmarket.com/Previews/ADID/DocumentAssets/246461

  49. L. Cheng and S. Hailes, “Analysis of wireless inertial sensing for athlete coaching support,” Proc. IEEE GLOBECOM 2008, pp. 1–5, New Orleans, USA, 30 Nov.–4 Dec. 2008.

  50. N. Uyop, et al., “LED indicator for heart rate monitoring system in sport application,” Proc. CSPA2011, pp. 64–66, Penang, Malaysia, 4–6 Mar. 2011.

  51. A. Dhamdhere, et al., “Experiments with wireless sensor networks for real-time athlete monitoring,” Proc. IEEE LCN 2010, pp. 938–945, Colorado, USA, 11–14 Oct. 2010.

  52. V. Sivaraman, et al., “Experimental study of mobility in the soccer field with application to real-time athlete monitoring,” Proc. IEEE WiMob 2008, pp. 337–345, Avignon, France, 12–14 Oct. 2010.

  53. G. Miguel, et al., “A wireless sensor network for soccer team monitoring,” Proc. DCOSS 2011, pp. 1–6, Barcelona, Spain, 27–29 June 2011.

  54. Association of Radio Industries and Businesses, ARIB STD-T108, version 1.0, 2011.

  55. 802.15.4-2015—IEEE Standard for Low-Rate Wireless Networks, IEEE 2015.

  56. C. Perkins, E. Royer and S. Das, “Ad hoc On-Demand Distance Vector (AODV) Routing, ff RFC 3561, 2003.

  57. http://www.digi.com/lp/xbee.

  58. Kohei Tezuka, “Vital data collection methods in real-time assistance for outdoor physical training,” master thesis, Osaka City University, Graduate School of Engineering, Mar. 2014.

  59. S. Hara, et al., “Challenges in wireless networking for real-time vital sensing from persons in exercises,” Proc. ISMICT 2016, in CD-ROM, Worcester, USA, 20–23 Mar. 2016.

  60. S. Hara et al., “Performance evaluation of packet forwarding methods in real-time vital data collection for players during a football game,” Proc. ISMICT 2014, pp. 1–5, Florence, Italy, 2–4 April 2014.

  61. M. Abolhasan, T. Wysocki and E. Dutkiewicz, “ A review of routing protocols for mobile ad hoc networks, ff, Ad Hoc Networks, Vol. 2, No. 1, pp. 1–22, Jan. 2004.

  62. T. Clausen and P. Jacquet, “Optimized Link State Routing Protocol (OLSR), ff RFC 3626, 2003.

  63. T. Winter, et al., “ RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks, ff RFC 6550, 2012.

  64. S. Hara et al., “Elements of a real-time vital signs monitoring system for players during a football game,” Proc. IEEE HealthCom 2014, pp. 460–465, Natal, Brazil, 15–18 Oct. 2014.

  65. S. Hara, T. Kawabata and H. Nakamura, “Real-time sensing, transmission and analysis for vital signs of persons during exercises,” Proc. IEEE EMBC 2015, Milan, Italy, 25–29 Aug. 2015.

  66. Z. Zhang, Z. Pi and B. Liu, TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise, IEEE Trans. Biomed. Eng., Vol. 62, No. 2, pp. 522–531, Feb. 2015.

  67. T. Nagata, et al., “VO2 estimation using 6-axis motion sensor with sports activity classification,” Proc. IEEE EMBC 2016, in the web, Orlando, USA, 16–20 Aug. 2016.

  68. N. Nakamura, et al., “Applying neural network to VO2 estimation using 6-axis motion sensing data,” Proc. IEEE EMBC 2016, in the web, Orlando, USA, 16–20 Aug. 2016.

  69. A. Gagge, J. Stolwijk and Y. Nishi, An effective temperature scale based on a simple model of human physiological regulatory response, ASHRAE Trans., Vol. 77, No. 1, pp. 247–262, 1977.

    Google Scholar 

  70. H. Yomo, D. Nakamura and S. Hara, “Human group sensing and networking: scenario development and feasibility study,” Proc. ISMICT 2015, pp. 144–147, Kamakura, Japan, 24–26 Mar. 2015.

  71. S. Zhang, R. Benenson and B. Schiele, “Filtered channel features for pedestrian detection,” Proc. IEEE CVPR 2015, Boston, USA, pp. 1751–1760, 7–12 June 2015.

  72. S. Zhang, C. Bauckhage and A. Cremers, “Informed Haar-like features improve pedestrian detection,” Proc. IEEE CVPR 2014, Columbus, USA, pp. 947–954, 23–28 June 2015.

  73. R. Miyamoto and T. Oki, “Soccer player detection with only color features selected using informed Haar-like features,” Advanced Concepts for Intelligent Vision Systems, Oct. 2016 (to be published).

  74. H. Hiromoto, H. Sugano and R. Miyamoto, Partially parallel architecture for AdaBoost-based detection with Haar-like features, IEEE Trans. Circuits Syst. Video Technol., Vol. 19, No. 1, pp. 41–52, Jan. 2009.

  75. M. Hiromoto and R. Miyamoto, “Hardware architecture for high-accuracy real-time pedestrian detection with CoHOG features,” Proc. IEEE ICCVW 2009, pp. 894–899, Kyoto, Japan, 27 Sep.–4 Oct. 2009.

  76. R. Miyamoto and H. Sugano, “Parallel implementation strategy for CoHOG-based pedestrian detection using a multi-core processor,” IEICE Trans. Fundamentals, Vol. E94-A, No. 11, pp. 2315–2322, Nov. 2011.

  77. T. Oki and R. Miyamoto, “Scene text localization using a two-class detector constructed by filtered channel features,” Proc. ITC-CSCC 2016, pp. 459–462, Okinawa, Japan, 10–13 July 2016.

  78. H. Sugano and R. Miyamoto, “Parallel implementation of pedestrian tracking using multiple cues on GPGPU,” Proc. IEEE ICCVW 2009, pp. 900–906, Kyoto, Japan, 27 Sep.–4 Oct. 2009.

  79. R. Aoki and R. Miyamoto, “Personal identification based on feature extraction using motions of a reduced set of joints,” Proc, IEEE CoDIT, in CD-ROM, Saint Paul’s Bay, Malta, Vol. 6–8, Apr. 2016.

Download references

Acknowledgements

This work was supported by the Research and Development of Innovative Network Technologies to Create the Future of National Institute of Information and Communications Technology (NICT) of Japan.

Author information

Authors and Affiliations

  1. Graduate School of Engineering, Osaka City University, Osaka, Japan

    Shinsuke Hara

  2. Graduate School of Engineering Science, Kansai University, Suita, Japan

    Hiroyuki Yomo

  3. School of Science and Technology, Meiji University, Kawasaki, Japan

    Ryusuke Miyamoto

  4. Oki Electric Industry Co., Ltd., Osaka, Japan

    Yasutaka Kawamoto

  5. Synthesis Corporation, New York, NY, USA

    Hiroyuki Okuhata

  6. Graduate School of Health and Well-Being, Kansai University, Suita, Japan

    Takashi Kawabata

  7. Aihara Second Hospital, Osaka, Japan

    Hajime Nakamura

Authors
  1. Shinsuke Hara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiroyuki Yomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryusuke Miyamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yasutaka Kawamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hiroyuki Okuhata
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Takashi Kawabata
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hajime Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shinsuke Hara.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hara, S., Yomo, H., Miyamoto, R. et al. Challenges in Real-Time Vital Signs Monitoring for Persons During Exercises. Int J Wireless Inf Networks 24, 91–108 (2017). https://doi.org/10.1007/s10776-017-0339-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-017-0339-2

Keywords

Search

Navigation