Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Zhangjie, F., Sun, X., Qi, L., Zhou, L., and Shu, J., Achieving Efficient Cloud Search Services: Multi-keyword Ranked Search over Encrypted Cloud Data Supporting Parallel Computing. IEICE Trans. Comm. E98-B(1):190–200, 2015.

    Article  Google Scholar 

  2. Ren, Y., Shen, J., Wang, J., Han, J., and Lee, S., Mutual Verifiable Provable Data Auditing in Public Cloud Storage. Journal of Internet Technology 16(2):317–323, 2015.

    Google Scholar 

  3. Zhihua Xia, Xinhui Wang, Xingming Sun, and Qian Wang, A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data. IEEE Trans. Parallel Distrib. Syst., 2015. doi:10.1109/TPDS.2015.2401003.

  4. Zhou, Y., and Zhu, J., Design and implementation of Zig-bee based wireless sensor network for remote SpO2 monitor. In: Proc. of 2010 2nd International Conference on Future Computer and Communication (ICFCC), pp. 278–281, 2010.

  5. Adochiei, F., Rotariu, C., Ciobotariu, R., and Costin, H., wireless low-power pulse oximetry system for patient telemonitoring. In: Proc Of 2011 7th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 1–4, 2011.

  6. Naraharisetti, K. V. P., and Bawa,M., Comparison of different signal processing methods for reducing artifacts from photoplethysmograph signal. In: Proc Of 2011 IEEE International Conference on Electro/Information Technology (EIT), pp. 1–8, 2011.

  7. Otero, A., Felix, P., Presedo, J., and Zamarron, C., Evaluation of an alternative definition for the apnea-hypopnea index. In: Proc Of 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4654–4657, 2010.

  8. Nobuyuki, A., Yasuhiro, N., Taiki, T., Miyae, Y., Kiyoko, M., and Terumasa, H., Trial of Measurement of Sleep Apnea Syndrome with SoundMonitoring and SpO2 at home. In: Proc. Of 11th International Conference on e-Health Networking, Applications and Services, pp. 66–69, 2010.

  9. Ranjan Singh, R., and Banerjee, R., Multi-parametric analysis of sensory data collected from automotive drivers for building a safety-critical wearable computing system. In: Proc Of 2010 2nd International Conference on Computer Engineering and Technology (ICCET), pp. 355–360, 2010.

  10. Ghasemzadeh, H., Loseu, V., and Jafari, R., Structural Action Recognition in Body Sensor Networks: Distributed Classification Based on String Matching. IEEE Trans. Inf. Technol. Biomed. 14(2):425–435, 2010.

    Article  PubMed  Google Scholar 

  11. Hong, Y.-J., Kim, I.-J., Chul Ahn, S., and Kim, H.-G.: Activity Recognition Using Wearable Sensors for Elder Care, 2008.

  12. Cao, H., Leung, V., Chow, C., and Chan, H., Enabling Technologies for Wireless Body Area Networks: A Survey and Outlook. IEEE Commun. Mag. 47(12):84–93, 2009.

    Article  Google Scholar 

  13. Chung, W.-Y., Lee, Y.-D., and Jung, S.-J., A Wireless Sensor Network Compatible Wearable U-healthcare Monitoring System Using Integrated ECG, Accelerometer and SpO2, pp. 1529–1532, 2008.

  14. Lee, H., Lim, S., and Huh, J., Design and Implementation of Baby-care Service based on Context-awareness for Digital Home. In: The 7th IEEE International Conference on Advanced Communication Technology, Vol. 11, pp. 908–911, 2005.

  15. Huo, H., Xu, Y., Yan, H., Mubeen, S., and Zhang, H., An Elderly Health Care System Using Wireless Sensor Networks at Home. In: Sensor Third International Conference on Technologies and Applications, pp. 158–163, 2009.

  16. Tang, Y., Wang, S., Chen, Y., and Chen, Z., PPCare: A Personal and Pervasive Health Care System for the Elderly. In: 9th International Conference on Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC), 2012.

  17. Hossain, M. A., and Ahmed, D. T., Virtual Caregiver: An Ambient-Aware Elderly Monitoring System. IEEE Trans. Inf. Technol. Biomed. 16(6):1024–1031, 2012.

    Article  PubMed  Google Scholar 

  18. Bourke, A. K., OBrien, J. V., and Lyons, G. M., Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & Posture 26:194–199, 2007.

    Article  CAS  Google Scholar 

  19. Dai, J., Bai, X., Yang, Z., Shen, Z., and Xuan, D., PerFallD: A pervasive fall detection system using mobile phones. In: IEEE International Conference on Pervasive Computing and Communications Workshops(PERCOM Workshops), pp. 292–297, 2010.

  20. Sposaro, F., and Tyson, G., iFall: An Android Application for Fall Monitoring and Response. In: 31st Annual International Conference of the IEEE EMBS, pp. 6119–6122, 2009.

  21. Nyan, M. N., Tay, F. E. H., and Tan, A.W. Y., Seah Distinguishing Fall Activities from Normal Activities by Angular Rate Characteristics and High-Speed Camera Characterization. Med. Eng. Phys. 28(8):842–849, 2006.

    Article  CAS  PubMed  Google Scholar 

  22. Lai, C.-F., Chang, S.-Y., Chao, H.-C., and Huang, Y.-M., Detection of Cognitive Injured Body Region UsingMultiple Triaxial Accelerometers for Elderly Falling. IEEE Sensors J. 11(3):763–770, 2011.

    Article  Google Scholar 

  23. Chang, S.-Y., C-F Lai, H.-C., Chao, J., and Park, Y.-M., Huang, An Environmental-Adaptive Fall detection System on Mobile Device. J. Med. Syst. 35(5):1299–1312, 2011.

    Article  PubMed  Google Scholar 

  24. Lai, C.-F., Huang, Y.-M., Park, J., and Chao, H.-C., Adaptive Body Posture Analysis Using Collaborative Multi-Sensors for Elderly Falling Detection. IEEE Intell. Syst. 24(6):20–30, 2010.

  25. Degen, T., Jaeckel, H., Rufer, M., and Wyss, S., SPEEDY: a fall detector in a wrist watch. In: Proc. Seventh IEEE Int. Symp. on Wearable Computers (ISWC’03), New-York, pp. 184–187, (2003)

  26. Chen, D., Zhang, Y., Feng, W., and X. Li: A wireless realtime fall detecting system based on barometer and accelerometer (2012).

  27. Krunz, M., Muqattash, A., and Lee, S.-J., Transmission power control in wireless ad hoc networks: challenges, solutions and open issues. Network 18(5):8–14, 2004.

    Google Scholar 

  28. Correia Luiz, H. A., Macedo Daniel, F., dos Santos Aldri, L., Loureiro Antonio, A. F., Marcos, S., and Nogueira, J., Transmission power control techniques for wireless sensor networks. Comput. Netw. 4765–4779, 2007.

  29. Lin, S., Zhang, J., Zhou, G., Gu, L., He, T., and Stankovic John, A., ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks. In: Proceedings of the 4th international conference on Embedded networked sensor systems, pp. 223–236, 2006.

  30. Egwar, A. A., and Rai, I. A., Power Aware Differentiated Routing (PADR) in Wireless Sensor Networks. In: Proceedings of 18th Internatonal Conference on Computer Communications and Networks, Vol. 3-6, pp. 1–6, 2009.

  31. Egwar, A. A.: Power Aware Differentiated Routing(PADR) in Wireless Sensor Networks, Master thesis in Data Communication and Software Engineering of Makerere University, 2009.

  32. Wood, A., Stankovic, J. A., Virone, G., Selavo, L., He, Z., Cao, Q., Doan, T., Wu, Y., Fang, L., and Stoleru, R., Context-Aware Wireless Sensor Networks for Assisted Living and Residential Monitoring. IEEE Netw. 22(4):26–33, 2008.

  33. Nishihara, K., Ishizaka, K., and Sakai, J., Power Saving in Mobile Devices Using Context-Aware Resource Control. In: First International Conference on Networking and Computing, pp. 220–226, 2010.

Download references

Acknowledgments

The authors would like to thank the National Science Council of the Republic of China, Taiwan for supporting this research under Contract NSC 101-2628-E-194-003-MY3, 101-2221-E-197-008-MY3 and 102-2219-E-194-002. This study is also conducted under the Institute for Information Industry which is subsidized by the Ministry of Economy Affairs of the Republic of China.

Author information

Authors and Affiliations

  1. Department of Engineering Science, National Cheng Kung University, Tainan, 701, Taiwan, Republic of China

    Shih-Yeh Chen & Ming-Shi Wang

  2. School of Computer & Software, 210044, Nanjing, China

    Chin-Feng Lai

  3. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, 621, Taiwan, Republic of China

    Chin-Feng Lai & Ren-Hung Hwang

  4. Smart Network System Institute, Institute for Information Industry, Taipei, 105, Taiwan, Republic of China

    Ying-Hsun Lai

Authors
  1. Shih-Yeh Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chin-Feng Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ren-Hung Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying-Hsun Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming-Shi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chin-Feng Lai.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, SY., Lai, CF., Hwang, RH. et al. An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services. J Med Syst 39, 194 (2015). https://doi.org/10.1007/s10916-015-0343-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0343-y

Keywords

Search

Navigation