Skip to main content
Log in

A study on algorithm to identify the abnormal status of a patient using acceleration algorithm

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

The system discussed in this paper targets high-risk patients and the elderly living alone requiring ongoing status checking. For services that quickly identify abnormal symptoms that occurred to the subject and send them to medical staff, changes in the patient’s condition are detected by using acceleration (tangent) algorithm. We conducted a study sensing sudden changes based on the value of the location information and temperature/pulse/heartbeat/blood pressure values measured in personal health devices (PHDs), a biological information measuring device attached to the patient. PHDs based on ZigBee, and smartphone will replace the role of the sensor gateway. ZigBee sensor nodes were connected to PHDs, which measure the bio-signals of patients, to form a wireless network.

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

Access this article

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

Instant access to the full article PDF.

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
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. “World Population Ageing: 1950–2050”, UN. http://www.un.org

  2. Zhao M, Konishi Y, Noguchi H (2013) Retiring for better health? Evidence from health investment behaviors in Japan. Faculty of Political Science and Economics, Waseda University, Diss

    Google Scholar 

  3. Park GS (2012) Research on utilizing U-health to provide telehealthcare to the elderly in rural areas. Glob e-Bus Assoc 13:463–486

    Google Scholar 

  4. Lee BM, Lim HC, Kang UK (2011) Mutual authentication protocol based on the effective divided session for the secure transmission of medical information in u-Health. J Korea Contents Assoc 11:142–151

    Google Scholar 

  5. Kim JS (2011) Elderly people’s medical cost rise and effective management method. Korea Institute for Health and Social Affairs, issue and focus, vol 114, pp 1–8

  6. Song TM (2011) u-Health service effect analysis—centering on metabolic syndrome management service. Korea Institute for Health and Social Affairs, issue and focus, vol 79, pp 1–8

  7. Kim YH, Lim IK, Lee JG, Lee JP, Namgung H, Lee JK (2013) Study on medical emergency alert algorithm based on acceleration algorithm. Information Science and Applications (ICISA), 2013 International Conference on, pp 77–80

  8. Virone G, Noury N, Demongeot J (2002) A system for automatic measurement of circadian activity deviations in telemedicine. IEEE Trans Biomed Eng 49(12):1463–1469

    Article  Google Scholar 

  9. Kim TH, Kim SH, Lee MS, Shin DK, Shin DI (2010) Intelligent home system with context awareness based on physiological signal. Conference of Korean Society for Internet Information, pp 127–132

  10. Wood A (2006) ALARM-NET: Wireless Sensor Networks for assisted-living and residential monitoring. Technical report CS-2006-13, WSN Research Group, University of Virginia

  11. UVA AlarmNet: http://www.cs.virginia.edu/wsn/medical/alarmnet

  12. Ko JW, Chung KY, Han JS (2013) Model transformation verification using similarity and graph comparison algorithm. Multimed Tools Appl. doi:10.1007/s11042-013-1581-y

    Google Scholar 

  13. Han JS, Chung KY, Kim GJ (2013) Policy on literature content based on software as service. Multimed Tools Appl. doi:10.1007/s11042-013-1664-9

    Google Scholar 

  14. Chan M, Esteve D, Escriba C, Campo E (2008) A review of smart homes-present state and future challenges. Comput Method Programs Biomed 91:55–81

    Article  Google Scholar 

  15. Lee SW, Mase K (2002) Activity and location recognition using wearable sensors. IEEE Pervasive Comput 1:24–32

    Google Scholar 

  16. Led S et al (2013) Analysis and description of HOLTIN service provision for AECG monitoring in complex indoor environments. Sensors 13(4):4947–4960

    Google Scholar 

  17. Baek SJ, Han JS, Chung KY (2013) Dynamic reconfiguration based on goal-scenario by adaptation strategy. Wireless Pers Commun. doi:10.1007/s11277-013-1239-0

    Google Scholar 

  18. Kim SH, Chung KY (2013) Medical information service system based on human 3D anatomical model. Multimed Tools Appl. doi:10.1007/s11042-013-1584-8

    Google Scholar 

  19. Virone G, Alwan M, Dalal S, Kell SW, Turner B, Stankovic JA, Felder R (2008) Behavioral Patterns of Older Adults in Assisted Living. IEEE Trans Inf Technol Biomed 12(3):387–398

    Article  Google Scholar 

  20. Virone G (2009) Assessing everyday life behavioral rhythms for the older generation. Pervasive Mobile Comput 5:606–622

    Google Scholar 

  21. Lee SW (2011) Senior living pattern detection and Error Detection Technology Trends. Korean Inst Inf Sci Eng 29(1):83–92

    Google Scholar 

  22. Kang SK, Chung KY, Lee JH (2013) Development of head detection and tracking systems for visual surveillance. Pers Ubiquit Comput. doi:10.1007/s00779-013-0668-9

    Google Scholar 

  23. Oh SY, Chung KY (2013) Target speech feature extraction using non-parametric correlation coefficient. Cluster Computing. doi:10.1007/s10586-013-0284-5

    Google Scholar 

  24. Kim GH, Kim YG, Chung KY (2013) Towards virtualized and automated software performance test architecture. Multimed Tools Appl. doi:10.1007/s11042-013-1536-3

    Google Scholar 

Download references

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0013029).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jae-Kwang Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, YH., Lim, IK. & Lee, JK. A study on algorithm to identify the abnormal status of a patient using acceleration algorithm. Pers Ubiquit Comput 18, 1337–1350 (2014). https://doi.org/10.1007/s00779-013-0736-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-013-0736-1

Keywords

Navigation