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Walking pattern analysis using deep learning for energy harvesting smart shoes with IoT

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Abstract

Wearable Health Devices (WHDs) benefit people to monitor their health status and have become a necessity in today’s world. The smart shoe is the type of WHD, that provides comfort, convenience, and fitness tracking. Hence smart shoes can be considered as one of the most useful innovations in the field of wearable devices. In this paper, we propose a unique system, in which the smart shoes are capable of energy harvesting when the user is walking, running, dancing, or carrying out any other similar activities. This generated power can be used to charge portable devices (like mobile) and to light up the LED torch. It also has Wi-Fi-that allows it to get connected to smartphones or any device on a cloud. The recorded data were used to determine the walking pattern of the user (gait analysis) using deep learning. The overall classification accuracy obtained with proposed smart shoes could reach up to 96.2%. This gait analysis can be further used for detecting any injury or disorder that the shoe user is suffering from. One more unique feature of the proposed smart shoe is its capability of adjusting the size by using inflatable technology as per the user’s comfort.

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References

  1. Shenck NS, Paradiso JA (2001) Energy scavenging with shoe-mounted piezoelectrics. IEEE Micro 21(3):30

    Article  Google Scholar 

  2. Kong K, Bae J, Tomizuka M (2008) Detection of abnormalities in a human gait using smart shoes. In: Sensors and smart structures technologies for civil, mechanical, and aerospace systems 2008, vol 6932. International Society for Optics and Photonics, p 69322G

  3. Jung PG, Oh S, Lim G, Kong K (2014) A mobile motion capture system based on inertial sensors and smart shoes. ASME J Dyn Syst Meas Control 136(1):011002. https://doi.org/10.1115/1.4025207

    Article  Google Scholar 

  4. Eskofier BM, Lee SI, Baron M, Simon A, Martindale CF, Gaßner H, Klucken J (2017) An overview of smart shoes in the internet of health things: gait and mobility assessment in health promotion and disease monitoring. Appl Sci 7(10):986

    Article  Google Scholar 

  5. Zhang Z, Zhu Z, Bazor B, Lee S, Ding Z, Pan T (2019) FeetBeat: a flexible iontronic sensing wearable detects pedal pulses and muscular activities. IEEE Trans Biomed Eng 66(11):3072

    Article  Google Scholar 

  6. Xia H, Charlton J, Hunt M, Shull P (2019) Preliminary test of a smart shoe for training foot progression angle during walking. Osteoarthritis Cartilage 27:S64

    Article  Google Scholar 

  7. Zerin I (2015) LPcomS: towards a low power wireless smart-shoe system for gait analysis in people with disabilities. Master’s Theses. Paper 335. http://epublications.marquette.edu/theses_open/335

  8. Wood TL (1994) Smart shoes. US Patent 5,373,651

  9. Oshin O, Sobowale S, Oni O, Atayero A (2017) Development of a power-harnessing smart shoe system with outdoor navigation. In: Proceedings of the world congress on engineering and computer science, vol 1

  10. Koli PS, Chavan NS, Gaikar SG (2019) Construction of smart shoe system for visually impaired people. Int J Res Analyt Rev 6(1):2349–5138

    Google Scholar 

  11. Seo S, Jang SW (2015) Design and Implementation of a smart shoes module based on Arduino. J Korea Inst Inf Commun Eng 19(11):2697

    Google Scholar 

  12. Larkin R (2016) Meet the new “talking shoes” by google. https://www.esoftload.info/meet-the-new-talking-shoes-by-google

  13. Liszewski A (2015) Reebok pumps are back and aren’t as hideous or expensive any more. https://gizmodo.com/reebok-pumps-are-back-and-arent-as-hideous-or-expensive-1689855875

  14. Velloso E, Schmidt D, Alexander J, Gellersen H, Bulling A (2015) The feet in human–computer interaction: a survey of foot-based interaction. ACM Comput Surv (CSUR) 48(2):1

    Article  Google Scholar 

  15. Gatto A, Frontoni E (2014) Energy harvesting system for smart shoes. In: 2014 IEEE/ASME 10th international conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE, pp 1–6

  16. Gupta A, Sharma A (2015) Piezoelectric energy harvesting via shoe sole. Int J New Technol Res 1(6):10–13

    Google Scholar 

  17. Frontoni E, Mancini A, Zingaretti P, Gatto P (2013) Energy harvesting for smart shoes: a real life application. In: ASME 2013 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers Digital Collection

  18. Shull PB, Xia H (2018) Energy harvesting modeling and prediction during walking gait for a sliding shoe. In: 2018 IEEE 23rd international conference on Digital Signal Processing (DSP). IEEE, pp 1–4

  19. Lee SS, Choi ST, Choi SI (2019) Classification of gait type based on deep learning using various sensors with smart insole. Sensors 19(8):1757

    Article  Google Scholar 

  20. Mehendale N (2020) Facial emotion recognition using convolutional neural networks (FERC). SN Appl Sci 2(3):1

    Article  Google Scholar 

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Authors and Affiliations

  1. K. J. Somaiya College of Engineering, Mumbai, 400077, Maharashtra, India

    Neel Shah, Laxit Kamdar, Drashti Gokalgandhi & Ninad Mehendale

Authors
  1. Neel Shah
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  2. Laxit Kamdar
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  3. Drashti Gokalgandhi
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  4. Ninad Mehendale
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Corresponding author

Correspondence to Ninad Mehendale.

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Conflict of interest

Authors N. Mehendale, N. Shah, and L. Kamdar declares that he has no conflict of interest also author D. Gokalgandhi declares that she has no conflict of interest aswell.

Human and animal rights

This article does not contain any studies with animals performed by any of the authors. And also this article contain studies with human participants wearing the smart shoes. All the necessary permissions were obtained from Institute Ethical committee and concerned authorities.

Informed consent

Informed consent was obtained from all the human participation who participated in wearing these smart shoes.

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Shah, N., Kamdar, L., Gokalgandhi, D. et al. Walking pattern analysis using deep learning for energy harvesting smart shoes with IoT. Neural Comput & Applic 33, 11617–11625 (2021). https://doi.org/10.1007/s00521-021-05864-4

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  • DOI: https://doi.org/10.1007/s00521-021-05864-4

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