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Standardizing a Shoe Insole Based on ISO/IEEE 11073 Personal Health Device (X73-PHD) Standards

  • Hawazin BadawiEmail author
  • Fedwa Laamarti
  • Faisal Arafsha
  • Abdulmotaleb El Saddik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 918)

Abstract

Personal healthcare systems play fundamental role in shaping the future of healthcare. With the explosion of Digital Twin [1] including wearable technology, many healthcare systems depend on personal health devices (PHD) to perform their functions. It is thus important to standardize the utilized PHDs. In this paper, we propose the X73-PHD standard for a smart shoe insole as this promising device is not yet standardized. We provide a background on shoe insoles and their importance in gait analysis. The main contribution of this paper is providing the detailed design of X73-PHD standard for the shoe insole (SI) through the three main parts of X73-PHD standards, which are: domain information model (DIM), service model (SM), and communication model (CM). Besides,we explain the hardware architecture and the functional model of the SI used in this work. Finally, the standard implementation for the SI is presented.

Keywords

Shoe insole Standardization Personal health device X73-PHD 

References

  1. 1.
    El Saddik, A.: Digital twins: the convergence of multimedia technologies. IEEE Multimedia 25(2), 87–92 (2018)CrossRefGoogle Scholar
  2. 2.
    Committee of the IEEE Engineering in Medicine, S. and Society, B. 1107. IEEE Std 11073-20601TM-2014. Standard for Health informatics—Personal health device communication—Application profile—Optimized Exchange Protocol (1107)Google Scholar
  3. 3.
  4. 4.
    GPS SmartSole. http://gpssmartsole.com/gpssmartsole/. Accessed 8 Nov 2018
  5. 5.
    DIGITSOLE. https://www.digitsole.com/. Accessed 8 Nov 2018
  6. 6.
    FEETME. http://www.feetme.fr/en/index.php. Accessed 8 Nov 2018
  7. 7.
    Muro-de-la-Herran, A., García-Zapirain, B., Méndez-Zorrilla, A.: Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14(2), 3362–3394 (2014)CrossRefGoogle Scholar
  8. 8.
    Hausdorff, J.M., Ladin, Z., Wei, J.Y.: Footswitch system for measurement of the temporal parameters of gait. J. Biomech. 28(3), 347–351 (1995)CrossRefGoogle Scholar
  9. 9.
    Fraser, S.A., Li, K.Z., DeMont, R.G., Penhune, V.B.: Effects of balance status and age on muscle activation while walking under divided attention. J. Gerontol. B, Psychol. Sci. Soc. Sci. 62(3), P171–P178 (2007)CrossRefGoogle Scholar
  10. 10.
    Cutlip, R.G., Mancinelli, C., Huber, F., DiPasquale, J.: Evaluation of an instrumented walkway for measurement of the kinematic parameters of gait. Gait Posture 12(2), 134–138 (2000)CrossRefGoogle Scholar
  11. 11.
    van Uden, C.J., Besser, M.P.: Test-retest reliability of temporal and spatial gait characteristics measured with an instrumented walkway system (GAITRite®). BMC Musculoskelet. Disord. 5(1), 13 (2004)CrossRefGoogle Scholar
  12. 12.
    Arafsha, F., Laamarti, F., El Saddik, A.: Development of a wireless CPS for gait parameters measurement and analysis. In: International Instrumentation and Measurement Technology Conference (I2MTC), May 2018Google Scholar
  13. 13.
    Howell, A.M., Kobayashi, T., Hayes, H.A., Foreman, K.B., Bamberg, S.J.M.: Kinetic gait analysis using a low-cost insole. IEEE Trans. Biomed. Eng. 60(12), 3284–3290 (2013)CrossRefGoogle Scholar
  14. 14.
    Hafidh, B., Al Osman, H., El Saddik, A.: SmartInsole: a foot-based activity and gait measurement device. In: 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–4 (2013)Google Scholar
  15. 15.
    Crea, S., Donati, M., De Rossi, S., Oddo, C., Vitiello, N.: A wireless flexible sensorized insole for gait analysis. Sensors 14(1), 1073–1093 (2014)CrossRefGoogle Scholar
  16. 16.
    Tan, A.M., Fuss, F.K., Weizman, Y., Woudstra, Y., Troynikov, O.: Design of low cost smart insole for real time measurement of plantar pressure. Procedia Technol. 20, 117–122 (2015)CrossRefGoogle Scholar
  17. 17.
    Jagos, H., Pils, K., Haller, M., Wassermann, C., Chhatwal, C., Rafolt, D., Rattay, F.: Mobile gait analysis via eSHOEs instrumented shoe insoles: a pilot study for validation against the gold standard GAITRite. J. Med. Eng. Technol. 41(5), 375–386 (2017)CrossRefGoogle Scholar
  18. 18.
    Arafsha, F., Hanna, C., Aboualmagd, A., Fraser, S., El Saddik, A.: Instrumented Wireless SmartInsole system for mobile gait analysis: a validation pilot study with Tekscan Strideway. J. Sens. Actuator Netw. 7(3), 36 (2018)CrossRefGoogle Scholar
  19. 19.
    Badawi, H.F., Dong, H., El Saddik, A.: Mobile cloud-based physical activity advisory system using biofeedback sensors. Future Gener. Comput. Syst. 66, 59–70 (2017)CrossRefGoogle Scholar
  20. 20.
    Park, K., Lim, S.: A multipurpose smart activity monitoring system for personalized health services. Inf. Sci. 314, 240–254 (2015)CrossRefGoogle Scholar
  21. 21.
    DesClouds, P., Laamarti, F., Durand-Bush, N., El Saddik, A.: Developing and testing an application to assess the impact of smartphone usage on well-being and performance outcomes of student-athletes. In: International Conference on Information Theoretic Security, 2018, pp. 883–896. Springer, Cham (2018)Google Scholar
  22. 22.
    Badawi, H., El Saddik, A.: Towards a context-aware biofeedback activity recommendation mobile application for healthy lifestyle. Procedia Comput. Sci. 21, 382–389 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hawazin Badawi
    • 1
    Email author
  • Fedwa Laamarti
    • 1
  • Faisal Arafsha
    • 1
  • Abdulmotaleb El Saddik
    • 1
  1. 1.Multimedia Communications Research Laboratory (MCRLab)University of OttawaOttawaCanada

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