Skip to main content
SpringerLink
Log in

Human Fall Detection System over IMU Sensors Using Triaxial Accelerometer

  • Conference paper
  • First Online:
Book cover Computational Intelligence: Theories, Applications and Future Directions - Volume I

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 798))

Abstract

A sudden increase in the number of deaths over the past few years by slipping and falling, especially in case of patients in hospitals and aged people at homes, is a serious concern and calls for the need of an autonomous system for detection of fall and alerting caretaker in case of emergency. We propose an algorithm which, first, derives features from an input stream of data sensed and uses it in learning of our system and further, provides it with the capability of classifying a sequence into either fall or activity of daily living sequence implemented using support vector machine. We propose a space and time efficient system, minimizing its cost by using only 3-axial accelerometer as sensor. Choice of type and number of features along with their operational complexity is a crucial factor for our system. Performance analysis is done by first training our system and then testing its accuracy in classifying test sequences using machine learning algorithm.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Ahmed, H., Tahir, M.: Improving the accuracy of human body orientation estimation with wearable imu sensors. IEEE Trans. Instrum. Measur. 66(3), 535–542 (2017)

    Article  Google Scholar 

  2. Aziz, O., Musngi, M., Park, E.J., Mori, G., Robinovitch, S.N.: A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med. Bio. Eng. Comput. 55(1), 45–55 (2017)

    Article  Google Scholar 

  3. Bourke, A., Obrien, J., Lyons, G.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2), 194–199 (2007)

    Article  Google Scholar 

  4. Bourke, A.K., Lyons, G.M.: A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Med. Eng. Phys. 30(1), 84–90 (2008)

    Article  Google Scholar 

  5. Cornacchia, M., Ozcan, K., Zheng, Y., Velipasalar, S.: A survey on activity detection and classification using wearable sensors. IEEE Sens. J. 17(2), 386–403 (2017)

    Article  Google Scholar 

  6. Dai, J., Bai, X., Yang, Z., Shen, Z., Xuan, D.: PerFallD: a pervasive fall detection system using mobile phones. In: 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 292–297 . IEEE (2010)

    Google Scholar 

  7. Hakim, A., Huq, M.S., Shanta, S., Ibrahim, B.: Smartphone based data mining for fall detection: analysis and design. Procedia Comput. Sci. 105, 46–51 (2017)

    Article  Google Scholar 

  8. Hwang, J.Y., Kang, J., Jang, Y.W., Kim, H.C.: Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEMBS’04, vol. 1, pp. 2204–2207. IEEE (2004)

    Google Scholar 

  9. Kangas, M., Konttila, A., Winblad, I., Jamsa, T.: Determination of simple thresholds for accelerometry-based parameters for fall detection. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, pp. 1367–1370. IEEE (2007)

    Google Scholar 

  10. Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden Markov models. Appl. Soft Comput. 55, 168–177 (2017)

    Article  Google Scholar 

  11. Lindemann, U., Hock, A., Stuber, M., Keck, W., Becker, C.: Evaluation of a fall detector based on accelerometers: a pilot study. Med. Biol. Eng. Comput. 43(5), 548–551 (2005)

    Article  Google Scholar 

  12. Mubashir, M., Shao, L., Seed, L.: A survey on fall detection: principles and approaches. Neurocomputing 100, 144–152 (2013)

    Article  Google Scholar 

  13. Noury, N., Fleury, A., Rumeau, P., Bourke, A., Laighin, G., Rialle, V., Lundy, J.: Fall detection-principles and methods. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, EMBS, pp. 1663–1666. IEEE (2007)

    Google Scholar 

  14. Panhwar, M., Shah, S.M.S., Shah, S.M.Z.S., Shah, S.M.A., Chowdhry, B.S.: Smart phone based fall detection using auto regression modeling in a non-restrictive setting. Indian J. Sci. Technol. 10(5), (2017)

    Article  Google Scholar 

  15. Shastry, M.C., Asgari, M., Wan, E.A., Leitschuh, J., Preiser, N., Folsom, J., Condon, J., Cameron, M., Jacobs, P.G.: Context-aware fall detection using inertial sensors and time-of-flight transceivers. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 570–573. IEEE (2016)

    Google Scholar 

  16. Tolkiehn, M., Atallah, L., Lo, B., Yang, G.Z.: Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 369–372. IEEE (2011)

    Google Scholar 

  17. Vavoulas, G., Pediaditis, M., Spanakis, E.G., Tsiknakis, M.: The mobifall dataset: An initial evaluation of fall detection algorithms using smartphones. In: 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE), pp.1–4. IEEE (2013)

    Google Scholar 

  18. Vavoulas, G., Pediaditis, M., Chatzaki, C., Spanakis, E.G., Tsiknakis, M.: The MobiFall dataset: fall detection and classification with a smartphone. In: Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, IGI Global, pp. 1218–1231 (2017)

    Google Scholar 

  19. Vogelhuber, T., Fleming, G., Moore, L., Haggerty, M., Hanlon, P., Bartone, C.G.: Prize-winning ohio university students present their work on an antenna for body area networks [education corner]. IEEE Antennas Propag. Mag. 59(1), 116–126 (2017)

    Article  Google Scholar 

  20. Wang, Y., Wu, K., Ni, L.M.: Wifall: device-free fall detection by wireless networks. IEEE Trans. Mob. Comput. 16(2)(2016)

    Google Scholar 

  21. Zhao, K., Jia, K., Liu, P.: Fall detection algorithm based on human posture recognition. In: Advances in Intelligent Information Hiding and Multimedia Signal Processing: Proceeding of the Twelfth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 21–23 Nov 2016, Kaohsiung, Taiwan, vol. 2, pp.119–126. Springer (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Punjab Engineering College, Chandigarh, India

    Shubham Ranakoti, Shagneet Arora, Shweta Chaudhary, Shakun Beetan, Amtoj Singh Sandhu, Padmavati Khandnor & Poonam Saini

Authors
  1. Shubham Ranakoti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shagneet Arora
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shweta Chaudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shakun Beetan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amtoj Singh Sandhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Padmavati Khandnor
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Poonam Saini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shubham Ranakoti .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ranakoti, S. et al. (2019). Human Fall Detection System over IMU Sensors Using Triaxial Accelerometer. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_39

Download citation

Publish with us

Policies and ethics

Search

Navigation