Advertisement

Gait-Based Authentication for Smart Locks Using Accelerometers in Two Devices

  • Kazuki Watanabe
  • Makoto Nagatomo
  • Kentaro Aburada
  • Naonobu Okazaki
  • Mirang ParkEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)

Abstract

Smart locks can be opened and closed electronically. Fingerprint or face authentication is inconvenient for smart locks because it requires the user to stop for several seconds in front of the door and remove certain accessories (e.g., gloves, sunglasses). This study proposes a user authentication method based on gait features. Conventional gait-based authentication methods have low identification accuracy. The proposed gait-based authentication method uses accelerometers in a smartphone and a wearable device (i.e., smartwatch). We extracted 31 features from the acquired acceleration data and calculated identification accuracy for various machine-learning algorithms. The highest accuracy was 95.3%, obtained using random forest. We found that the maximum interval, minimum interval, and minimum value had the highest contributions to identification accuracy, and variance, median, and standard deviation had the lowest contributions.

Notes

Acknowledgements

This work was supported by JSPS KAKENHI Grant Numbers JP17H01736, JP17K00139.

References

  1. 1.
    Ministry of Internal Affairs and Communications: 2018 White Paper on Information and Communication in Japan (2018). http://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h29/pdf/n3300000.pdf
  2. 2.
    Gartner: Gartner Says Worldwide Wearable Device Sales to Grow 26 Percent in 2019, 07 June 2019. https://www.gartner.com/en/newsroom/press-releases/2018-11-29-gartner-says-worldwide-wearable-device-sales-to-grow-
  3. 3.
    Qrio: Qrio Smart Lock, 07 May 2019. https://qrio.me/smartlock
  4. 4.
    August: August Smart Lock, 07 May 2019. https://august.com
  5. 5.
    Kwikset: Door Locks Door Hardware Smart Locks & Smart key Technology, 16 April 2019. https://www.kwikset.com
  6. 6.
    Hou, R., Watanabe, Y.: A Study on authentication at the time of the walk of using the acceleration sensor of smartphone. In: Computer Security Symposium 2013, pp. 21–23 (2013). (in Japanese)Google Scholar
  7. 7.
    Konno, S., Nakamura, Y., Shiraishi, Y., Takahashi, O.: Improvement of gait-based authentication by using multiple wearable sensors. IPSJ J. 57(1), 109–122 (2016). (in Japanese)Google Scholar
  8. 8.
    Iwamoto, T., Sugimori, D., Matsumoto, M.: A study of identification of pedestrian by using 3-axis accelerometer. IPSJ J. 55(2), 734–749 (2014). (in Japanese)Google Scholar
  9. 9.
    Mondal, S., Nandy, A., Chakraborty, P., et al.: Gait based personal identification system using rotation sensor. J. Emerg. Trends Comput. Inf. Sci. 3(3), 395–402 (2012)Google Scholar
  10. 10.
    Scikit-learn: scikit-learn machine learning in Python Scikit-learn 0.19.1 documentation, 07 May 2019. http://scikit-learn.org/stable/index.html

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kazuki Watanabe
    • 1
  • Makoto Nagatomo
    • 1
  • Kentaro Aburada
    • 2
  • Naonobu Okazaki
    • 2
  • Mirang Park
    • 1
    Email author
  1. 1.Kanagawa Institute of TechnologyAtsugiJapan
  2. 2.University of MiyazakiMiyazakiJapan

Personalised recommendations