Skip to main content
SpringerLink
Log in
Book cover

International Conference on Advanced Information Networking and Applications

AINA 2022: Advanced Information Networking and Applications pp 199–208Cite as

Smart Insole Monitoring System for Fall Detection and Bad Plantar Pressure

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 451))

Abstract

The present work proposes a portable electronic system for the feedback and monitoring of plantar pressure for elderly people in real-time. It is composed of a smart insole, a mobile application, and a cloud server to store data. The smart insole is made up of four resistive pressure sensors for footwear, gyroscopes (GY521) for measuring foot orientation, a Bluetooth HC05 module for transmitting collected data in real time to a laptop, smartphone, and an ATMEGA328P microcontroller. The mobile application was created to provide the patient with instant visual feedback of his actions while wearing the shoes. This technique is useful for monitoring the physical health of elderly persons on a regular basis without interfering with their normal activities.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Ping, W., Jin-Gang, W., Xiao-Bo, S., Wei, H.: The research of telemedicine system based on embedded computer. In: Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS 2005), Shanghai, China, pp. 114–117. IEEE, January 2006

    Google Scholar 

  2. Gani, A., Gribok, A.V., Lu, Y., Ward, W.K., Vigersky, R.A., Reifman, J.: Universal glucose models for predicting subcutaneous glucose concentration in humans. IEEE Trans. Inf. Technol. Biomed. 14(1), 157–165 (2010)

    Article  Google Scholar 

  3. Sonou, A., et al.: Absolute cardiovascular risk of women using hormonal contraception in Porto-Novo. Cardiovasc. J. Afr., 2 (2018)

    Google Scholar 

  4. Bowling, F.L., Rashid, S.T., Boulton, A.J.: Preventing and treating foot complications associated with diabetes mellitus. Nat. Rev. Endocrinol. 11(10), 606–616 (2015)

    Article  Google Scholar 

  5. Coates, J., Chipperfield, A., Clough, G.: Wearable multimodal skin sensing for the diabetic foot (2016)

    Google Scholar 

  6. World Health Organization: Hearts: technical package for cardiovascular disease management in primary health care (2016)

    Google Scholar 

  7. Cortesi, P.A., et al.: Trends in cardiovascular diseases burden and vascular risk factors in Italy: the Global Burden of Disease study 1990–2017. Eur. J. Prev. Cardiol. 28(4), 385–396 (2021)

    Google Scholar 

  8. Dos Santos, L.R., et al.: Cardiovascular diseases in obesity: what is the role of magnesium? Biol. Trace Elem. Res., 1–8 (2021)

    Google Scholar 

  9. Leszek, J., et al.: The links between cardiovascular diseases and Alzheimer’s disease. Curr. Neuropharmacol. 19(2), 152–169 (2021)

    Google Scholar 

  10. Sterling, D.A., O’Connor, J.A., Bonadies, J.: Geriatric falls: injury severity is high and disproportionate to mechanism. J. Trauma Injury Infect. Crit. Care 50(1), 116–9 (2001)

    Article  Google Scholar 

  11. Hegde, N., Bries, M., Swibas, T., Melanson, E., Sazonov, E.: Automatic recognition of activities of daily living utilizing insole-based and wrist-worn wearable sensors. IEEE J. Biomed. Health Inform. 22(4), 979–988 (2018)

    Article  Google Scholar 

  12. Chen, J.L., et al.: Plantar pressure-based insole gait monitoring techniques for diseases monitoring and analysis: a review. Adv. Mater. Technol., 2100566 (2021)

    Google Scholar 

  13. Saidani, S., Haddad, R., Bouallegue, R., Shubair, R.: A new proposal of a smart insole for the monitoring of elderly patients. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021. LNNS, vol. 226, pp. 273–284. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75075-6_22

  14. Sunarya, U., et al.: Feature analysis of smart shoe sensors for classification of gait patterns. Sensors 20(21), 6253 (2020)

    Google Scholar 

  15. Ivanov, K., et al.: Identity recognition by walking outdoors using multimodal sensor insoles. IEEE Access 8, 150797–150807 (2020)

    Google Scholar 

  16. Gioacchini, L., Poli, A., Cecchi, S., Spinsante, S.: Sensors characterization for a calibration-free connected smart insole for healthy ageing. In: Goleva, R., Garcia, N.R..C., Pires, I.M. (eds.) HealthyIoT 2020. LNICST, vol. 360, pp. 35–54. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69963-5_3

  17. Ren, D., Aubert-Kato, N., Anzai, E., Ohta, Y., Tripette, J.: Random forest algorithms for recognizing daily life activities using plantar pressure information: a smart-shoe study. PeerJ 8, e10170 (2020)

    Google Scholar 

  18. Sundarsingh, E.F., Saraswathi, V., Sankareshwari, S., Sona, S.: Fall detection smart-shoe enabled with wireless IoT device. Circuit World (2020)

    Google Scholar 

  19. Li, X., Nie, L., Xu, H., Wang, X.: Collaborative fall detection using smart phone and Kinect. Mob. Netw. Appl. 23(4), 775–788 (2018)

    Article  Google Scholar 

  20. Shahzad, A., Kim, K.: FallDroid: an automate smart-phone-based fall detection system using multiple kernel learning. IEEE Trans. Ind. Inform. 15(1), 35–44 (2019)

    Google Scholar 

  21. Mariani, B., Hoskovec, C., Rochat, S., Bula, C., Penders, J., Aminian, K.: 3D gait assessment in young and elderly subjects using foot-worn inertial sensors. J. Biomech. 43(15), 2999–3006 (2010)

    Google Scholar 

  22. Saidani, S., Haddad, R., Bouallegue, R.: A prototype design of a smart shoe insole system for real-time monitoring of patients. In: 2020 6th IEEE Congress on Information Science and Technology (CiSt), pp. 116–121. IEEE, June 2021

    Google Scholar 

  23. Hynes, A., Kirkland, M. C., Ploughman, M., Czarnuch, S.: Comparing the gait analysis of a Kinect system to the Zeno Walkway: preliminary results. Developing (2019)

    Google Scholar 

  24. Yu, J., Gao, W., Jiang, W.: Foot pronation detection based on plantar pressure measurement. In: Journal of Physics: Conference Series, vol. 1646, no. 1, p. 012041. IOP Publishing, September 2020

    Google Scholar 

  25. Saboor, A., et al.: Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review. IEEE Access 8, 167830–167864 (2020)

    Article  Google Scholar 

  26. Xi, X., Jiang, W., Lü, Z., Miran, S.M., Luo, Z.Z.: Daily activity monitoring and fall detection based on surface electromyography and plantar pressure. Complexity (2020)

    Google Scholar 

Download references

Acknowledgment

We are grateful for the support of the Department of Electrical and Computer Engineering at the New York University of Abu Dhabi (NYU).

Author information

Authors and Affiliations

  1. Innov’Com Laboratory/Sup’Com, National Engineering School of Tunis, University Tunis El Manar, Tunis, Tunisia

    Salma Saidani

  2. Laval University, Quebec, Canada

    Rim Haddad

  3. Innov’Com Laboratory, High School of communication of Tunis University of Carthage, Ariana, Tunisia

    Ridha Bouallegue

  4. New York University (NYU) Abu Dhabi, Abu Dhabi, United Arab Emirates

    Raed Shubair

Authors
  1. Salma Saidani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rim Haddad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ridha Bouallegue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raed Shubair
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Salma Saidani .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Technology Sydney, Sydney, NSW, Australia

    Farookh Hussain

  3. Faculty of Bussiness Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saidani, S., Haddad, R., Bouallegue, R., Shubair, R. (2022). Smart Insole Monitoring System for Fall Detection and Bad Plantar Pressure. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_20

Download citation

Publish with us

Policies and ethics

Search

Navigation