Abstract
The elderly population is increasing rapidly around the world. There are no enough caretakers for them. Use of AI-based in-home medical care systems is gaining momentum due to this. Human fall detection is one of the most important tasks of medical care system for the aged people. Human fall is a common problem among elderly people. Detection of a fall and providing medical help as early as possible is very important to reduce any further complexity. The chances of death and other medical complications can be reduced by detecting and providing medical help as early as possible after the fall. There are many state-of-the-art fall detection techniques available these days, but the majority of them need very high computing power. In this paper, we proposed a lightweight and fast human fall detection system using pose estimation. We used ‘Movenet’ for human joins key-points extraction. Our proposed method can work in real-time on any low-computing device with any basic camera. All computation can be processed locally, so there is no problem of privacy of the subject. We used two datasets ‘GMDCSA’ and ‘URFD’ for the experiment. We got the sensitivity value of 0.9375 and 0.9167 for the dataset ‘GMDCSA’ and ‘URFD’ respectively. The source code and the dataset GMDCSA of our work are available online to access.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alam, E., Sufian, A., Dutta, P., Leo, M.: Vision-based human fall detection systems using deep learning: a review. Comput. Biol. Med. 146, 105626 (2022)
Wang, Z., Ramamoorthy, V., Gal, U., Guez, A.: Possible life saver: a review on human fall detection technology. Robotics 9(3), 55 (2020)
Gutiérrez, J., Rodríguez, V., Martin, S.: Comprehensive review of vision-based fall detection systems. Sensors 21(3), 947 (2021)
Alam, E., Sufian, A., Das, A.K., Bhattacharya, A., Ali, M.F., Rahman, M.H.: Leveraging deep learning for computer vision: a review. In: 2021 22nd International Arab Conference on Information Technology (ACIT), pp. 1–8. IEEE (2021)
Wang, X., Ellul, J., Azzopardi, G.: Elderly fall detection systems: a literature survey. Front. Robot. AI 7, 71 (2020)
Munea, T.L., Jembre, Y.Z., Weldegebriel, H.T., Chen, L., Huang, C., Yang, C.: The progress of human pose estimation: a survey and taxonomy of models applied in 2d human pose estimation. IEEE Access 8, 133330–133348 (2020)
Chen, Y., Tian, Y., He, M.: Monocular human pose estimation: a survey of deep learning-based methods. Comput. Vis. Image Underst. 192, 102897 (2020)
Bajpai, R., Joshi, D.: Movenet: a deep neural network for joint profile prediction across variable walking speeds and slopes. IEEE Trans. Instrum. Meas. 70, 1–11 (2021)
MoveNet: Ultra fast and accurate pose detection model. — TensorFlow Hub — tensorflow.org. https://www.tensorflow.org/hub/tutorials/movenet. Accessed 21 Oct 2022
Sufian, A., Alam, E., Ghosh, A., Sultana, F., De, D., Dong, M.: Deep learning in computer vision through mobile edge computing for IoT. In: Mukherjee, A., De, D., Ghosh, S.K., Buyya, R. (eds.) Mobile Edge Computing, pp. 443–471. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69893-5_18
Asif, U., Von Cavallar, S., Tang, J., Harrer, S.: SSHFD: single shot human fall detection with occluded joints resilience. arXiv preprint arXiv:2004.00797
Chen, Z., Wang, Y., Yang, W.: Video based fall detection using human poses. In: Liao, X., Zhao, W., Chen, E., Xiao, N., Wang, L., Gao, Y., Shi, Y., Wang, C., Huang, D. (eds.) BigData 2022. CCIS, vol. 1496, pp. 283–296. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-9709-8_19
Apicella, A., Snidaro, L.: Deep neural networks for real-time remote fall detection. In: Del Bimbo, A., Cucchiara, R., Sclaroff, S., Farinella, G.M., Mei, T., Bertini, M., Escalante, H.J., Vezzani, R. (eds.) ICPR 2021. LNCS, vol. 12662, pp. 188–201. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68790-8_16
Leite, G.V., da Silva, G.P., Pedrini, H.: Three-stream convolutional neural network for human fall detection. In: Wani, M.A., Khoshgoftaar, T.M., Palade, V. (eds.) Deep Learning Applications, Volume 2. AISC, vol. 1232, pp. 49–80. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-6759-9_3
Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291–7299 (2017)
Chen, T., Ding, Z., Li, B.: Elderly fall detection based on improved YOLOV5s network. IEEE Access 10, 91273–91282 (2022)
Ultralytics, Yolov5, https://github.com/ultralytics/yolov5. Accessed 14 Jan 2023
Liu, W., et al.: Fall detection for shipboard seafarers based on optimized Blazepose and LSTM. Sensors 22(14), 5449 (2022)
Beddiar, D.R., Oussalah, M., Nini, B.: Fall detection using body geometry and human pose estimation in video sequences. J. Vis. Commun. Image Represent. 82, 103407 (2022)
Amsaprabhaa, M., et al.: Multimodal spatiotemporal skeletal kinematic gait feature fusion for vision-based fall detection. Expert Syst. Appl. 212, 118681 (2023)
Kwolek, B., Kepski, M.: Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput. Methods Programs Biomed. 117(3), 489–501 (2014)
Kocabas, M., Karagoz, S., Akbas, E.: MultiPoseNet: fast multi-person pose estimation using pose residual network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 437–453. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_26
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Alam, E., Sufian, A., Dutta, P., Leo, M. (2024). Real-Time Human Fall Detection Using a Lightweight Pose Estimation Technique. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1956. Springer, Cham. https://doi.org/10.1007/978-3-031-48879-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-48879-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48878-8
Online ISBN: 978-3-031-48879-5
eBook Packages: Computer ScienceComputer Science (R0)