Skip to main content
SpringerLink
Log in

Fall Detection and Protection System Based on Characteristic Areas Algorithm

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13013))

Included in the following conference series:

Abstract

Hip fracture caused by falls and its complications is one of the greatest threats to disability and death of the elderly. To reduce physical damage from falls in the elderly, the current solution to achieve effective protection is detecting fall trends and turning on protective devices. However, the existing products have the problems of low accuracy and poor real-time. In this paper, a high accuracy and high real-time human fall detection and protection system based on characteristic areas algorithm is designed, which can detect the trend of falls within 400 ms after the human body begins to fall and is filled with the airbag in the 400 ms later, realizing effective protection of the human hip. The system got 95.33% accuracy, with an average airbag opening time of 70 ms.

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

Access this chapter

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
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

Institutional subscriptions

References

  1. Jiang, J., Long, J., Ling, W., et al.: Incidence of fall-related injury among old people in mainland China. Arch. Gerontol. Geriatr. 61(2), 131–139 (2015)

    Article  Google Scholar 

  2. Hu, L.: Fall detection algorithms based on wearable device: a review. J. Zhejiang Univ. (Eng. Sci.) 52(9), 1717–1728 (2018)

    Google Scholar 

  3. Ahn, S., Shin, I., Kim, Y.: Pre-impact fall detection using an inertial sensor unit. J. Foot Ankle Res. 7(1), A124 (2014)

    Article  Google Scholar 

  4. Lee, J.K., Robinovitch, S.N., Park, E.J.: Inertial sensing-based pre-impact detection of falls involving near-fall scenarios. IEEE Trans. Neural Syst. Rehabil. Eng. 23(2), 258–266 (2015)

    Article  Google Scholar 

  5. Otanasap, N.: Pre-impact fall detection based on wearable device using dynamic threshold model. In: 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pp. 362–365. IEEE Computer Society (2016)

    Google Scholar 

  6. Aziz, O., Russell, C.M., Park, E.J., et al.: The effect of window size and lead time on pre-impact fall detection accuracy using support vector machine analysis of waist mounted inertial sensor data. In: Proceedings of International Conference on Engineering in Medicine and Biology Society, pp. 30–33. IEEE, Chicago (2014)

    Google Scholar 

  7. Diep, N.N., Pham, C., Phuong, T.M.: A classifier-based approach to real-time fall detection using low-cost wearable sensors. In: Proceedings of the Fifth Symposium on Information and Communication Technology, pp. 14–20. IEEE, Hanoi (2014)

    Google Scholar 

  8. Er, J.K., Ang, W.T.: Evaluation of single HMM as a pre-impact fall detector based on different input signals. In: 2018 IEEE Region Ten Symposium (Tensymp), Sydney, Australia, pp. 207–212 (2018)

    Google Scholar 

  9. Jian, H., Chen, H.: A portable fall detection and alerting system based on k-NN algorithm and remote medicine. Communications 12(4), 23–31 (2015)

    MathSciNet  Google Scholar 

  10. Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: X-factor HMMs for detecting falls in the absence of fall-specific training data. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 1–9. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13105-4_1

    Chapter  Google Scholar 

  11. Nukala, B.T., Shibuya, N., Rodriguez, A.I., et al.: A real-time robust fall detection system using a wireless gait analysis sensor and an artificial neural network. In: Proceedings of International Conference on Healthcare Innovation, pp. 219–222. IEEE, Seattle (2014)

    Google Scholar 

  12. Fakhrulddin, A.H., Fei, X., Li, H.: Convolutional neural networks (CNN) based human fall detection on body sensor networks (BSN) sensor data. In: 4th International Conference on Systems and Informatics (ICSAI), Hangzhou, pp. 1461–1465 (2017)

    Google Scholar 

  13. Münzner, S., Schmidt, P., Reiss, A., et al.: CNN-based sensor fusion techniques for multimodal human activity recognition. In: Proceedings of the 2017 ACM International Symposium on Wearable Computers, pp. 158–165 (2017)

    Google Scholar 

  14. Shi, G., Chan, C.S., Luo, Y., et al.: Development of a human airbag system for falling protection using MEMS motion sensing technology. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4405–4410. IEEE (2006)

    Google Scholar 

  15. Toshiyo, T., Masaki, S., Takumi, Y.: A wearable airbag to prevent fall injuries. IEEE Trans. Inf. Technol. Biomed. 13(6), 910–914 (2009)

    Article  Google Scholar 

  16. Toshiyo, T., Takumi, Y., Masaki, S.: A preliminary study to demonstrate the use of an air bag device to prevent fall-related injuries. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France, pp. 23–26. IEEE (2007)

    Google Scholar 

  17. Yilun, L., Guangyi, S., Josh, L., et al.: Towards a human airbag system using µIMU with SVM training for falling-motion recognition. In: IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO), Hong Kong. IEEE (2009)

    Google Scholar 

  18. Zeng, M., Nguyen, L.T., Yu, B., et al.: Convolutional neural networks for human activity recognition using mobile sensors. In: Sixth International Conference on Mobile Computing, Applications and Services (MobiCASE 2014), pp.197–205. IEEE (2014)

    Google Scholar 

  19. Zhong, Z., Chen, F., Zhai, Q., et al.: A real-time pre-impact fall detection and protection system. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1039–1044. IEEE (2018)

    Google Scholar 

  20. Guangyi, S., Cheung-Shing, C., Guanglie, Z.: Towards a mobile airbag system using MEMS Sensors and embedded intelligence. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), Sanya, China, pp. 634–639. IEEE (2007)

    Google Scholar 

  21. Xu, T., Se, H., Liu, J.: A fusion fall detection algorithm combining threshold-based method and convolutional neural network. Microprocess. Microsyst. 82, 103828 (2021)

    Article  Google Scholar 

  22. Hassan, M.M., et al.: A smartphone-enabled fall detection framework for elderly people in connected home healthcare. IEEE Netw. 33(6), 58–63 (2019)

    Article  Google Scholar 

Download references

Acknowledgment

This research was funded by the National Key R&D Program of China (2018YFB1307002) and Beijing Municipal Science and Technology Project (Z191100004419008).

Author information

Authors and Affiliations

  1. Institute of Robotics, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China

    Jun Du & Jingyi Shi

  2. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China

    Xiaodong Wei, Ying Xu & Diansheng Chen

Authors
  1. Jun Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingyi Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaodong Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diansheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diansheng Chen .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Xin-Jun Liu

  2. Tsinghua University, Beijing, China

    Zhenguo Nie

  3. Beihang University, Beijing, China

    Jingjun Yu

  4. Tsinghua University, Beijing, China

    Fugui Xie

  5. Shandong University, Shandong, China

    Rui Song

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Du, J., Shi, J., Wei, X., Xu, Y., Chen, D. (2021). Fall Detection and Protection System Based on Characteristic Areas Algorithm. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13013. Springer, Cham. https://doi.org/10.1007/978-3-030-89095-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89095-7_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89094-0

  • Online ISBN: 978-3-030-89095-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation