Skip to main content

Advertisement

SpringerLink
Log in

Parallel Neural Network–Convolutional Neural Networks for Wearable Motorcycle Airbag System

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

Recently, motorcycle accidents have increased as the number of motorcycle drivers has increased. Although the head and neck are the body parts most frequently injured when a motorcycle accident occurs, there is a lack of research on the protection afforded to the neck by the safety equipment used by motorcycle drivers. This study presents an airbag system that uses artificial intelligence to prevent injury to the neck of a motorcycle driver. It uses a six-axis sensor, the MPU6050 sensor, which measures acceleration and angular velocity in real time as the user moves. The angles are obtained by using the measured acceleration and angular velocity, and the accident situation is judged by AI, which analyzes the acceleration and angle data. Because data is needed for AI to learn, data by type were collected through experiments. In this study, we compare the judgement performance of a parallel neural networks–convolutional neural network and a parallel neural network.

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

Access this article

Log in via an institution

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
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Jo SH, Woo J, Jeong JH, Byun GS (2019) Safety air bag system for motorcycle using parallel neural networks. J Electric Eng Technol 14(5):2191–2203

    Article  Google Scholar 

  2. Woo J, Jo SH, Jeong JH, Kim M, Byun GS (2020) A study on wearable airbag system applied with convolutional neural networks for safety of motorcycle. J Electric Eng Technol 15(2):883–897

    Article  Google Scholar 

  3. Quick Service Two Wheeled Vehicle Delivery Safety Guide 2017. KOSHA. https://www.kosha.or.kr/kosha/data/business/transportBook.do?medSeq=37934&codeSeq=1150180&medForm=101&menuId=-1150180101&mode=view. Accessed 10 Apr 2017

  4. Kiguchi K, Matsuo R (2017) Accident prediction based on motion data for perception-assist with a power-assist robot. In: 2017 IEEE symposium series on computational intelligence (SSCI), pp 1–5

  5. Kawaguchi S, Takemura H, Mizoguchi H, Kusunoki F, Egusa R, Funaoi H et al (2017) Accuracy evaluation of hand motion measurement using 3D range image sensor. In: 2017 eleventh international conference on sensing technology (ICST), pp 1–4

  6. Stone EE, Skubic M (2014) Fall detection in homes of older adults using the Microsoft Kinect. IEEE J Biomed Health Inform 19(1):290–301

    Article  Google Scholar 

  7. Ryu JT (2013) The development of fall detection system using 3-axis acceleration sensor and tilt sensor. J Korea Ind Inform Syst Res 18(4):19–24

    Google Scholar 

  8. Kim SH, Park J, Kim DW, Kim NG (2011) The study of realtime fall detection system with accelerometer and tilt sensor. J Korean Soc Precision Eng 28(11):1330–1338

    Google Scholar 

  9. Kim HY, Min JK (2011) Implementation of a motion capture system using 3-axis accelerometer. J KIISE Comput Pract Lett (in Korean) 17(6):383–388

    Google Scholar 

  10. Jun C, Park JH, Zhao X, Karimi H, Cao J (2019) Quantized nonstationary filtering of network-based markov switching RSNSs: a multiple hierarchical structure strategy. IEEE Trans Autom Control. https://doi.org/10.1109/TAC.2019.2958824

    Article  Google Scholar 

  11. Cheng J, Park JH, Cao J, Qi W (2020) A hidden mode observation approach to finite-time SOFC of Markovian switching systems with quantization. Nonlinear Dyn. https://doi.org/10.1007/s11071-020-05501-0

    Article  MATH  Google Scholar 

  12. Lee SM, Jo HR, Yoon SM (2016) Machine learning analysis for human behavior recognition based on 3-axis acceleration sensor. J Korean Inst Commun Sci 33(11):65–70

    Article  Google Scholar 

  13. Lee H, Lee S (2014) Real-time activity and posture recognition with combined acceleration sensor data from smartphone and wearable device. J KIISE Softw Appl 41(8):586–597

    Google Scholar 

  14. Kau LJ, Chen CS (2014) A smart phone-based pocket fall accident detection, positioning, and rescue system. IEEE J Biomed Health Inform 19(1):44–56

    Article  Google Scholar 

  15. Yoo BH, Heo G (2017) Detection of rotations in jump rope using complementary filter. J Korea Inst Inform Commun Eng 21(1):8–16

    Article  Google Scholar 

  16. Zhang Q, Li HQ, Ning YK, Liang D, Zhao GR (2014) Design and realization of a wearable hip-airbag system for fall protection. Appl Mech Mater 461:667–674 (Trans Tech Publications Ltd)

    Article  Google Scholar 

  17. Sugomori Y (2017) Detailed deep learning-time series data processing by TensorFlow. Keras, Wikibook, chap. 3, part 3.1.2 Deep learning and neural networks

  18. Sacko (2017) Deep learning that literary students can also understand(1)—perceptron. Sacko Tistory. https://sacko.tistory.com/10. Accessed 27 Sep 2017

  19. Sacko (2017) Deep learning that literary students can also understand(2)—Neural Network. Sacko Tistory. https://sacko.tistory.com/17?category=632408. Accessed 18 Oct 2017

  20. Saint Binary (2018) Role and type of activation function. Saint Binary Tistory. https://saintbinary.tistory.com/8. Accessed 5 Sept 2018

  21. Ahn NH (2016) Neural Network. Machine Learning Blog. https://nmhkahn.github.io/NN. Accessed 30 Jan 2016

  22. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  23. Park HS (2018) Machine learning #4. Tensor ≈ Blog. https://tensorflow.blog/%ED%95%B4%EC%BB%A4%EC%97%90%EA%B2%8C-%EC%A0%84%ED%95%B4%EB%93%A4%EC%9D%80-%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D-4/. Accessed Mar 2018

  24. Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113

    Article  Google Scholar 

  25. LeCun Y, Bengio Y (1995) Convolutional networks for images, speech, and time series. Handb Brain Theory Neural Netw 3361(10):1995

    Google Scholar 

  26. Sun L, Jia K, Yeung DY, Shi BE (2015) Human action recognition using factorized spatio-temporal convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 4597–4605

  27. Avci D (2016) An automatic diagnosis system for hepatitis diseases based on genetic wavelet kernel extreme learning machine. J Electric Eng Technol 11(4):993–1002

    Article  Google Scholar 

  28. Sacko (2017) Deep learning that literary students can also understand(3)—error back propagation, gradient descent. Sacko Tistory. https://sacko.tistory.com/19?category=632408. Accessed 25 Oct 2017

  29. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in neural information processing systems, pp 6626–6637

  30. Kim BS (2016) Gradient descent optimization algorithms theorem. Beomsu Kim’s Blog. https://shuuki4.github.io/deep%20learning/2016/05/20/Gradient-Descent-Algorithm-Overview.html. Accessed 20 May 2016

  31. Tieleman T, Hinton G (2012) Lecture 6.5-rmsprop, coursera: neural networks for machine learning. University of Toronto, Technical report

  32. Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: International conference on machine learning, pp 1139–1147

  33. Clevert DA, Unterthiner T, Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289

Download references

Acknowledgements

This work was supported by a Research Grant of Pukyong National University (2019).

Author information

Authors and Affiliations

  1. Department of Control and Instrumentation Engineering, Pukyong National University, Busan, Republic of Korea

    So-Hyeon Jo, Joo Woo & Gi-Sig Byun

  2. Light Rail Transit Research Team, Korea Railroad Research Institute, Uiwang-si, Gyeonggi-do, Republic of Korea

    Jae-Hoon Jeong

  3. Reliability Research Team, KD Navien Co., Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea

    Dong-Heon Lee

  4. TYTS CO., Ltd., Busan, Republic of Korea

    Tae-Kyung Sung

Authors
  1. Jae-Hoon Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. So-Hyeon Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joo Woo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dong-Heon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tae-Kyung Sung
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gi-Sig Byun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gi-Sig Byun.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jeong, JH., Jo, SH., Woo, J. et al. Parallel Neural Network–Convolutional Neural Networks for Wearable Motorcycle Airbag System. J. Electr. Eng. Technol. 15, 2721–2734 (2020). https://doi.org/10.1007/s42835-020-00507-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-020-00507-5

Keywords

Search

Navigation