Skip to main content
SpringerLink
Log in

A Survey of Computer Vision-Based Fall Detection and Technology Perspectives

  • Conference paper
  • First Online:
Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1682))

  • 432 Accesses

Abstract

With the increase in the number of elderly people living alone, the use of computer vision technology for real-time fall detection is of great importance. In this paper, we review fall detection based on computer vision from four perspectives: background significance, current research status, relevant influencing factors, and future research outlook. We summarized our approach by classifying the three types of input image data in fall detection systems: RGB (Red, Green, Blue), Depth, and IR (Infrared Radiation), outlining research in both target tracking and bone detection for basic image processing tasks, as well as methods for processing video data. We analyzed the possible effects of multiple factors on fall detection regarding camera selection, the individual object recognized, and the recognition environment, and collected the solutions. Based on the current problems and trends in vision-based fall detection, we present an outlook on future research and propose four new ideas including functional extensions using the easy fusion feature of Mask R-CNN (Mask Region with Convolutional Neural Network), the use of YOLO (You Only Look Once) family to improve the speed of target detection, using variants of LSTM (Long Short-Term Memory) such as GRU (Gate Recurrent Unit) to achieve more efficient detection, and using Transformer methods that have been migrated from natural language processing to computer vision for detection.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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. WHO global report on falls prevention in older age (2007)

    Google Scholar 

  2. Gutierrez, J., Rodriguez, V., Martin, S.: Comprehensive review of vision-based fall detection systems. Sens.-Basel 21(3) (2021)

    Google Scholar 

  3. Chen, Z.J., Wang, Y.: Infrared-ultrasonic sensor fusion for support vector machine-based fall detection. J. Intell. Mater. Syst. Struct. 29(9), 2027–2039 (2018)

    Article  Google Scholar 

  4. Msaad, S., Cormier, G., Carrault, G.: Detecting falls and estimation of daily habits with depth images using machine learning algorithms. In: 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Montreal, Canada, pp. 2163–2166. IEEE (2020)

    Google Scholar 

  5. Yodpijit, N., Sittiwanchai, T., Jongprasithporn, M.: The development of artificial neural networks (ANN) for falls detection. In: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), pp. 547–50. IEEE (2017)

    Google Scholar 

  6. Ramanujam, E., Padmavathi, S.: A vision-based posture monitoring system for the elderly using intelligent fall detection technique. In: Mahmood, Z. (eds.) Guide to Ambient Intelligence in the IoT Environment. Computer Communications and Networks. Springer, Cham, pp. 249–69 (2019). https://doi.org/10.1007/978-3-030-04173-1_11

  7. Auvinet ER, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Multiple cameras fall data set. Technical Report Number 1350. University of Montreal: Montreal, QC, Canada (2011)

    Google Scholar 

  8. Charfi, I.: Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification. J. Electr. Imag. 22(4) (2013)

    Google Scholar 

  9. Kepski, M., Kwolek, B.: Embedded system for fall detection using body-worn accelerometer and depth sensor. In: International Workshop Intelligent Data, pp. 755–759 (2015)

    Google Scholar 

  10. Martinez-Villasenor, L., Ponce, H., Brieva, J., Moya-Albor, E., Nunez-Martinez, J., Penafort-Asturiano, C.: UP-fall detection dataset: a multimodal approach. Sensors (Basel) 19(9) (2019)

    Google Scholar 

  11. Ma, X.: Depth-based human fall detection via shape features and improved extreme learning machine. IEEE J. Biomed. Health Inf. 18(6) (2014)

    Google Scholar 

  12. Cippitelli, E., Gambi, E., Gasparrini, S., Spinsante, S.: TST Fall detection dataset v2. IEEE Dataport (2016)

    Google Scholar 

  13. CMU Graphics Lab—Motion Capture Library (2021). http://mocap.cs.cmu.edu/

  14. Munaro, M.: A feature-based approach to people re-identification using skeleton keypoints (2014)

    Google Scholar 

  15. Adhikari, K.: Activity recognition for indoor fall detection using convolutional neural network (2017)

    Google Scholar 

  16. Tran, T.H.: A multi-modal multi-view dataset for human fall analysis and preliminary investigation on modality (2018)

    Google Scholar 

  17. Anitha, G., Priya, S.B.: Vision based real time monitoring system for elderly fall event detection using deep learning. Comput. Syst. Sci. Eng. 42(1), 87–103 (2022)

    Article  Google Scholar 

  18. Kong, X.B., Meng, Z.L., Nojiri, N., Iwahori, Y., Meng, L., Tomiyama, H.: A HOG-SVM based fall detection IoT system for elderly persons using deep sensor. Procedia Comput. Sci. 147, 276–282 (2019)

    Article  Google Scholar 

  19. Zhong, C.N., Ng, W.W.Y., Zhang, S., Nugent, C.D., Shewell, C., Medina-Quero, J.: Multi-occupancy fall detection using non-invasive thermal vision sensor. IEEE Sens. J. 21(4), 5377–5388 (2021)

    Article  Google Scholar 

  20. Feng, Z., Zhu, X., Xu, L., Liu, Y.: Research on human target detection and tracking based on artificial intelligence vision. In: 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), pp. 1051–1054 (2021)

    Google Scholar 

  21. Velasquez, J., Piech, K., Lehnhoff, S., Fischer, L., Garske, S.: Incremental development of a co-simulation setup for testing a generation unit controller for reactive power provision. Comput. Sci. Res. Dev. 32(1–2), 3–12 (2016). https://doi.org/10.1007/s00450-016-0319-2

    Article  Google Scholar 

  22. Satish, B., Jayakrishnan, P.: Hardware implementation of template matching algorithm and its performance evaluation. In: 2017 International Conference on Microelectronic Devices, Circuits and Systems (ICMDCS) (2017)

    Google Scholar 

  23. He, S.S., Liang, A., Lin, L., Song, T.: A continuously adaptive template matching algorithm for human tracking. In: 2017 First IEEE International Conference on Robotic Computing (IRC), pp. 303–309 (2017)

    Google Scholar 

  24. Ramirez, H., Velastin, S.A., Meza, I., Fabregas, E., Makris, D., Farias, G.: Fall detection and activity recognition using human skeleton features. IEEE Access 9, 33532–33542 (2021)

    Article  Google Scholar 

  25. Chaaraoui, A.A., Padilla-Lopez, J.R., Florez-Revuelta, F.: Fusion of skeletal and silhouette-based features for human action recognition with RGB-D devices. In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 91–97 (2013)

    Google Scholar 

  26. Zhang, H.B., Zhang, Y.X., Zhong, B., Lei, Q., Yang, L., Du, J.X., et al.: A comprehensive survey of vision-based human action recognition methods. Sensors (Basel) 19(5) (2019)

    Google Scholar 

  27. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  28. Ullah, A., Ahmad, J., Muhammad, K., Sajjad, M., Baik, S.W.: Action recognition in video sequences using deep bi-directional LSTM with CNN features. IEEE Access 6, 1155–1166 (2018)

    Article  Google Scholar 

  29. Luo, H., Liao, J., Yan, X., Liu, L.: Oversampling by a constraint-based causal network in medical imbalanced data classification. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2021)

    Google Scholar 

  30. Chen, Y., Kong, X., Meng, L., Tomiyama, H.: An edge computing based fall detection system for elderly persons. Procedia Comput. Sci. 174, 9–14 (2020)

    Article  Google Scholar 

  31. Fan, Y.X., Levine, M.D., Wen, G.J., Qiu, S.H.: A deep neural network for real-time detection of falling humans in naturally occurring scenes. Neurocomputing 260, 43–58 (2017)

    Article  Google Scholar 

  32. Chhetri, S., Alsadoon, A., Al-Dala’in, T., Prasad, P.W.C., Rashid, T.A., Maag, A.: Deep learning for vision-based fall detection system: enhanced optical dynamic flow. Comput. Intell. 37(1), 578–595 (2020)

    Article  MathSciNet  Google Scholar 

  33. Kong, X., Chen, L., Wang, Z., Chen, Y., Meng, L., Tomiyama, H.: Robust self-adaptation fall-detection system based on camera height. Sensors (Basel) 19(17) (2019)

    Google Scholar 

  34. Baldewijns, G., Debard, G., Mertes, G., Croonenborghs, T., Vanrumste, B.: Improving the accuracy of existing camera based fall detection algorithms through late fusion. In: P Annual International IEEE EMBS, pp. 2667–2671 (2017)

    Google Scholar 

  35. Khraief, C., Benzarti, F., Amiri, H.: Elderly fall detection based on multi-stream deep convolutional networks. Multimed. Tools Appl. 79(27–28), 19537–19560 (2020). https://doi.org/10.1007/s11042-020-08812-x

    Article  Google Scholar 

  36. He, K., Gkioxari, G., Doll´ar, P., Girshick, R.: Mask R-CNN. In: Facebook AI Research (FAIR) (2018)

    Google Scholar 

  37. Mobsite, S., Alaoui, N., Boulmalf, M.: A framework for elders fall detection using deep learning. IEEE (2020)

    Google Scholar 

  38. Lin, C.-B., Dong, Z., Kuan, W.-K., Huang, Y.-F.: A framework for fall detection based on OpenPose skeleton and LSTM/GRU models. Appl. Sci. 11(1) (2020)

    Google Scholar 

  39. Yin, Y., Lei, L., Liang, M., Li, X., He, Y., Qin, L.: Research on fall detection algorithm for the elderly living alone based on YOLO. In: 2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT), pp. 403–408 (2021)

    Google Scholar 

  40. Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: exceeding YOLO Series in 2021 (2021)

    Google Scholar 

  41. Lu, N., Wu, Y., Feng, L., Song, J.: Deep learning for fall detection: three-dimensional CNN combined with LSTM on video kinematic data. IEEE J. Biomed. Health Inform. 23(1), 314–323 (2019)

    Article  Google Scholar 

  42. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., et al.: Attention is all you need. Comput. Lang. (2017)

    Google Scholar 

  43. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., et al.: An image is worth 16x16 Words: transformers for image recognition at scale. Google Res. Brain Team (2021)

    Google Scholar 

  44. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Microsoft Research Asia (2021)

    Google Scholar 

  45. Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., et al.: Swin transformer V2: scaling up capacity and resolution. In: Microsoft Research Asia (2022)

    Google Scholar 

Download references

Acknowledgment

This work was supported by grants from the National Natural Science Foundation of China (grant no. 61977012), the China Scholarship Council (grant no. 201906995003), the Central Universities in China (grant no. 2021CDJYGRH011), and the Key Research Programme of Chongqing Science & Technology Commission (grant no. Cstc2019jscx-fxydX0054).

Author information

Authors and Affiliations

  1. Department of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

    Manling Yang

  2. School of Big Data and Software Engineering, Chongqing University, Chongqing, China

    Xiaohu Li, Jiawei Liu & Li Liu

  3. School of Materials and Energy, Southwest University, Chongqing, China

    Shu Wang

Authors
  1. Manling Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiawei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Liu .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Yinzhang Guo

  4. Shanxi Datong University, Datong, China

    Xiaoxia Song

  5. Tongji University, Shanghai, China

    Hongfei Fan

  6. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  7. University of Shanghai for Science and Technology, Shanghai, China

    Liping Gao

  8. Tongji University, Shanghai, China

    Bowen Du

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, M., Li, X., Liu, J., Wang, S., Liu, L. (2023). A Survey of Computer Vision-Based Fall Detection and Technology Perspectives. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1682. Springer, Singapore. https://doi.org/10.1007/978-981-99-2385-4_45

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-2385-4_45

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2384-7

  • Online ISBN: 978-981-99-2385-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation