Skip to main content
SpringerLink
Log in

Real-Time Multi-target Activity Recognition Based on FMCW Radar

  • Conference paper
  • First Online:
Intelligent Equipment, Robots, and Vehicles (LSMS 2021, ICSEE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1469))

  • 1488 Accesses

Abstract

Since multi-target activity recognition solutions based on wearable devices require that sensors be worn at all times, as well as vision-based multi-target activity recognition solutions are susceptible to weather or lighting conditions and are prone to revealing personal privacy, so this paper proposes a real-time multi-target activity recognition solution based on FMCW (Frequency Modulated Continuous Wave) radar to realize non-contact real-time multi-target activity recognition. Firstly, the point cloud data collected by FMCW radar are preprocessed. Secondly, each target information is obtained by multi-target tracking algorithm, and the activity key points and activity feature are extracted. Thirdly, according to the activity characteristic quantity, the data is classified by the classifier to realize the indoor real-time multi-target activity recognition. The test results show that the real-time activity recognition accuracy of this solution for falling, sitting and walking can reach 100%, 87.2% and 100%, respectively.

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 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
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. Park, S., Aggarwal, J.: Recognition of two-person interactions using a hierarchical Bayesian network. In: First ACM SIGMM International Workshop on Video Surveillance, pp. 65–76 (2003)

    Google Scholar 

  2. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 221–231 (2012)

    Article  Google Scholar 

  3. Li, T., Fan, L., Zhao, M., Liu, Y., Katabi, D.: Making the invisible visible: Action recognition through walls and occlusions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 872–881 (2019)

    Google Scholar 

  4. Banerjee, D., et al.: Application of spiking neural networks for action recognition from radar data. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–10. IEEE (2020)

    Google Scholar 

  5. Park, J., Cho, S.H.: IR-UWB radar sensor for human gesture recognition by using machine learning. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1246–1249. IEEE (2016)

    Google Scholar 

  6. Vandersmissen, B., et al.: Indoor person identification using a low-power FMCW radar. IEEE Trans. Geosci. Remote Sens. 56, 3941–3952 (2018)

    Google Scholar 

  7. Chuma, E.L., Roger, L.L.B., de Oliveira, G.G., Iano, Y., Pajuelo, D.: Internet of Things (IoT) privacy–protected, fall-detection system for the elderly using the radar sensors and deep learning. In: 2020 IEEE International Smart Cities Conference (ISC2), pp. 1–4. IEEE (2020)

    Google Scholar 

  8. Li, H., et al.: Multisensor data fusion for human activities classification and fall detection. In: 2017 IEEE SENSORS, pp. 1–3. IEEE (2017)

    Google Scholar 

  9. Luo, D., Du, S., Ikenaga, T.: Multi-task and multi-level detection neural network based real-time 3D pose estimation. In: 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1427–1434. IEEE (2019)

    Google Scholar 

  10. Ugolotti, R., Sassi, F., Mordonini, M., Cagnoni, S.: Multi-sensor system for detection and classification of human activities. J. Ambient Intell. Humanized Comput. 4, 27–41 (2013)

    Google Scholar 

  11. Will, C., Vaishnav, P., Chakraborty, A., Santra, A.: Human target detection, tracking, and classification using 24-GHz FMCW radar. J. IEEE Sens. J. 19, 7283–7299 (2019)

    Article  Google Scholar 

  12. Ren, A., Wang, Y., Yang, X., Zhou, M.: A dynamic continuous hand gesture detection and recognition method with FMCW radar. In: 2020 IEEE/CIC International Conference on Communications in China (ICCC), pp. 1208–1213. IEEE (2020)

    Google Scholar 

  13. Chen, M.-C., Wu, H.-Y.: Design of a fall detection system. In: 2011 International Conference on Electric Information and Control Engineering, pp. 3148–3151. IEEE (2011)

    Google Scholar 

  14. Livshitz, M.: Tracking radar targets with multiple reflection points. J. TI Internal Document (2017)

    Google Scholar 

  15. Baird, Z., Rajan, S., Bolic, M.: Classification of human posture from radar returns using ultra-wideband radar. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3268–3271. IEEE (2018)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Provincial Natural Science Foundation of Zhejiang (Y21F010057).

Author information

Authors and Affiliations

  1. Research Center for Sino-German Autonomous Driving, College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, 310018, China

    He Pan, Kang Liu, Lei Shen & Aihong Tan

Authors
  1. He Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aihong Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aihong Tan .

Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Qinglong Han

  2. Queen's University Belfast, Belfast, UK

    Sean McLoone

  3. Shanghai University, Shanghai, China

    Chen Peng

  4. Qingdao University of Science and Technology, Qingdao, China

    Baolin Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pan, H., Liu, K., Shen, L., Tan, A. (2021). Real-Time Multi-target Activity Recognition Based on FMCW Radar. In: Han, Q., McLoone, S., Peng, C., Zhang, B. (eds) Intelligent Equipment, Robots, and Vehicles. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1469. Springer, Singapore. https://doi.org/10.1007/978-981-16-7213-2_43

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-7213-2_43

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7212-5

  • Online ISBN: 978-981-16-7213-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation