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Deep Learning Based Fall Detection Using Simplified Human Posture

Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif


Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: Fall detection, machine learning, deep learning, pose estimation, tracking.

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[1] M. Kangas, I. Vikman, J. Wiklander, P. Lindgren, L. Nyberg, and T. J¨ams¨a, “Sensitivity and specificity of fall detection in people aged 40 years and over,” Gait & posture, vol. 29, no. 4, pp. 571–574, 2009.
[2] W. C. H. A. a Fall, “Important facts about falls,” 2016.
[3] U. Age, “Later life in the united kingdom,” Age UK Factsheet, 2018.
[4] H. Nait-Charif and S. J. McKenna, “Activity summarisation and fall detection in a supportive home environment,” in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 4. IEEE, 2004, pp. 323–326.
[5] D.-S. Jang, S.-W. Jang, and H.-I. Choi, “2d human body tracking with structural kalman filter,” Pattern Recognition, vol. 35, no. 10, pp. 2041–2049, 2002.
[6] J.-L. Chua, Y. C. Chang, and W. K. Lim, “A simple vision-based fall detection technique for indoor video surveillance,” Signal, Image and Video Processing, vol. 9, no. 3, pp. 623–633, 2015.
[7] Z.-P. Bian, J. Hou, L.-P. Chau, and N. Magnenat-Thalmann, “Fall detection based on body part tracking using a depth camera,” IEEE journal of biomedical and health informatics, vol. 19, no. 2, pp. 430–439, 2015.
[8] C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, “Monocular 3d head tracking to detect falls of elderly people,” in Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE. IEEE, 2006, pp. 6384–6387.
[9] M. Yu, S. M. Naqvi, and J. Chambers, “Fall detection in the elderly by head tracking,” in Statistical Signal Processing, 2009. SSP’09. IEEE/SP 15th Workshop on. IEEE, 2009, pp. 357–360.
[10] K. de Miguel, A. Brunete, M. Hernando, and E. Gambao, “Home camera-based fall detection system for the elderly,” Sensors, vol. 17, no. 12, p. 2864, 2017.
[11] A. Doulamis and N. Doulamis, “Adaptive deep learning for a vision-based fall detection,” in Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference. ACM, 2018, pp. 558–565.
[12] A. N´u˜nez-Marcos, G. Azkune, and I. Arganda-Carreras, “Vision-based fall detection with convolutional neural networks,” Wireless Communications and Mobile Computing, vol. 2017, 2017.
[13] A. Shojaei-Hashemi, P. Nasiopoulos, J. J. Little, and M. T. Pourazad, “Video-based human fall detection in smart homes using deep learning,” in Circuits and Systems (ISCAS), 2018 IEEE International Symposium on. IEEE, 2018, pp. 1–5.
[14] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” in CVPR, 2017.
[15] R. A. G¨uler, N. Neverova, and I. Kokkinos, “Densepose: Dense human pose estimation in the wild,” arXiv preprint arXiv:1802.00434, 2018.
[16] K. Adhikari, “Fall detection dataset,” 2017, last accessed 10 December 2018.
[Online]. Available:
[17] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in neural information processing systems, 2014, pp. 3320–3328.
[18] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ar, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision. Springer, 2014, pp. 740–755.
[19] S. Johnson and M. Everingham, “Clustered pose and nonlinear appearance models for human pose estimation,” in Proceedings of the British Machine Vision Conference, 2010, doi:10.5244/C.24.12.