A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches

  • Ryan Cameron
  • Zheming Zuo
  • Graham Sexton
  • Longzhi YangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 650)


Physically falling down amongst the elder helpless party is one of the most intractable issues in the era of ageing society, which has attracted intensive attentions in academia ranging from clinical research to computer vision studies. This paper proposes a fall detection/recognition system within the realm of computer vision. The proposed system integrates a group of gradient-based local visual feature extraction approaches, including histogram of oriented gradients (HOG), histogram of motion gradients (HMG), histogram of optical flow (HOF), and motion boundary histograms (MBH). A comparative study of the descriptors with the support of an artificial neural network was conducted based on an in-house captured dataset. The experimental results demonstrated the effectiveness of the proposed system and the power of these descriptors in real-world applications.


Fall detection Local feature extraction HOG HMG HOF MBH Artificial neural network 


  1. 1.
    Litvak, D., Zigel, Y., Gannot, I.: Fall detection of elderly through floor vibrations and sound. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4632–4635 (2008)Google Scholar
  2. 2.
    Zhu, L., Zhou, P., Pan, A., Guo, J., Sun, W., Wang, L., Chen, X., Liu, Z.: A survey of fall detection algorithm for elderly health monitoring. In: 2015 IEEE Fifth International Conference on Big Data and Cloud Computing, pp. 270–274 (2015)Google Scholar
  3. 3.
    Mohamed, O., Choi, H.J., Iraqi, Y.: Fall detection systems for elderly care: a survey. In: 2014 6th International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–4 (2014)Google Scholar
  4. 4.
    AlZubi, H.S., Gerrard-Longworth, S., Al-Nuaimy, W., Goulermas, Y., Preece, S.: Human activity classification using a single accelerometer. In: 2014 14th UK Workshop on Computational Intelligence (UKCI), pp. 1–6. IEEE (2014)Google Scholar
  5. 5.
    Zhang, Z., Conly, C., Athitsos, V.: A survey on vision-based fall detection. In: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 46. ACM (2015)Google Scholar
  6. 6.
    Mirmahboub, B., Samavi, S., Karimi, N., Shirani, S.: Automatic monocular system for human fall detection based on variations in silhouette area. IEEE Trans. Biomed. Eng. 60(2), 427–436 (2013)CrossRefGoogle Scholar
  7. 7.
    Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Robust video surveillance for fall detection based on human shape deformation. IEEE Trans. Circuits Syst. Video Technol. 21(5), 611–622 (2011)CrossRefGoogle Scholar
  8. 8.
    Merrouche, F., Baha, N.: Depth camera based fall detection using human shape and movement. In: IEEE International Conference on Signal and Image Processing (ICSIP), pp. 586–590. IEEE (2016)Google Scholar
  9. 9.
    Duta, I.C., Uijlings, J.R.R., Nguyen, T.A., Aizawa, K., Hauptmann, A.G., Ionescu, B., Sebe, N.: Histograms of motion gradients for real-time video classification. In: 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 1–6. IEEE (2016)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  11. 11.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR 2008, pp. 1–8, June 2008Google Scholar
  12. 12.
    Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006). doi: 10.1007/11744047_33 CrossRefGoogle Scholar
  13. 13.
    Popescu, M., Mahnot, A.: Acoustic fall detection using one-class classifiers. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3505–3508 (2009)Google Scholar
  14. 14.
    Delahoz, Y.S., Labrador, M.A.: Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14(10), 19806–19842 (2014)CrossRefGoogle Scholar
  15. 15.
    Hwang, J.Y., Kang, J.M., 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, 2004. IEMBS 2004, vol. 1, pp. 2204–2207. IEEE (2004)Google Scholar
  16. 16.
    Luštrek, M., Kaluža, B.: Fall detection and activity recognition with machine learning. Informatica 33(2), 205–212 (2009)Google Scholar
  17. 17.
    Kerdegari, H., Samsudin, K., Ramli, A.R., Mokaram, S.: Evaluation of fall detection classification approaches. In: 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS), vol. 1, pp. 131–136. IEEE (2012)Google Scholar
  18. 18.
    Degen, T., Jaeckel, H., Rufer, M., Wyss, S.: Speedy: a fall detector in a wrist watch. In: ISWC, pp. 184–189 (2003)Google Scholar
  19. 19.
    Planinc, R., Kampel, M.: Introducing the use of depth data for fall detection. Personal Ubiquit. Comput. 17(6), 1063–1072 (2013)CrossRefGoogle Scholar
  20. 20.
    Rougier, C., Auvinet, E., Rousseau, J., Mignotte, M., Meunier, J.: Fall detection from depth map video sequences. In: Toward useful Services for Elderly and People with Disabilities, pp. 121–128 (2011)Google Scholar
  21. 21.
    Albawendi, S., Appiah, K., Powell, H., Lotfi, A.: Video based fall detection with enhanced motion history images. In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 29. ACM (2016)Google Scholar
  22. 22.
    Uijlings, J., Duta, I.C., Sangineto, E., Sebe, N.: Video classification with densely extracted hog/hof/mbh features: an evaluation of the accuracy/computational efficiency trade-off. Int. J. Multimed. Inf. Retr. 4(1), 33–44 (2015)CrossRefGoogle Scholar
  23. 23.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision (IJCV) 60(2), 91–110 (2004)CrossRefGoogle Scholar
  24. 24.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR 2001, vol. 1, pp. I-511-I-518 (2001)Google Scholar
  25. 25.
    Horn, B.K., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1–3), 185–203 (1981)CrossRefGoogle Scholar
  26. 26.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence - vol. 2, IJCAI 1981, pp. 674–679. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1981)Google Scholar
  27. 27.
    Wang, S., Yang, K.J.: An image scaling algorithm based on bilinear interpolation with VC++. Tech. Autom. Appl. 27(7), 44–45 (2008)Google Scholar
  28. 28.
    Arandjelović, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2911–2918. IEEE (2012)Google Scholar
  29. 29.
    Jolliffe, I.: Principal Component Analysis. Wiley, Hoboken (2002)zbMATHGoogle Scholar
  30. 30.
    Haykin, S.S.: Neural Networks and Learning Machines. Prentice Hall, Upper Saddle River (2009)Google Scholar
  31. 31.
    Liu, Y., Jing, W., Xu, L.: Parallelizing backpropagation neural network using mapreduce and cascading model. Comput. Intell. Neurosci. 2016, 2842780:1–2842780:11 (2016)Google Scholar
  32. 32.
    De Villiers, J., Barnard, E.: Backpropagation neural nets with one and two hidden layers. IEEE Trans. Neural Netw. 4(1), 136–141 (1993)CrossRefGoogle Scholar
  33. 33.
    Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)CrossRefGoogle Scholar
  34. 34.
    Jensen, R., Shen, Q.: Are more features better? A response to attributes reduction using fuzzy rough sets. IEEE Trans. Fuzzy Syst. 17(6), 1456–1458 (2009)CrossRefGoogle Scholar
  35. 35.
    Goyette, N., Jodoin, P.M., Porikli, F., Konrad, J., Ishwar, P.: a new change detection benchmark dataset. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–8. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ryan Cameron
    • 1
  • Zheming Zuo
    • 1
  • Graham Sexton
    • 1
  • Longzhi Yang
    • 1
    Email author
  1. 1.Department of Computer and Information SciencesNorthumbria UniversityNewcastle upon TyneUK

Personalised recommendations