Skip to main content

Advertisement

SpringerLink
Log in

ViFa: an analytical framework for vision-based fall detection in a surveillance environment

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The decrease in fertility rates and the increase in the average age of individuals are the main reasons behind the aging of the population. Challenges that come with an aging population include proper nursing care. Because the cost of healthcare is high and falls that cause injury or death in the elderly are escalating, they are a challenge for public welfare and research into reliable surveillance is essential. Non-intrusive fall detection systems are vital for reducing fall trauma and machine vision is an appropriate solution for detecting unusual events such as falls. Because there are varieties of vision-based fall detection (VBFD) methods and a comprehensive framework is lacking, comparison and evaluation of existing methods are difficult. In the current study, an analytical framework having three main components is proposed. First, existing VBFD methods are classified into three categories. Next, general evaluation criteria are defined for analysis of the proposed categorizations. Finally, each method is qualitatively analyzed using the proposed categorizations. The proposed framework can accurately provide identification and analytical comparison of existing methods. In addition, it can allow selection of the most appropriate methods and suggest improvements for existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. ADER NS (2013) Detecting falls and poses in image silhouettes. J Igual R, Medrano C, Plaza I. Challenges, issues and trends in fall detection systems 12(66):1–66

    Google Scholar 

  2. Ageing WHO, Unit LC (2008) WHO Global report on falls prevention in older age. World Health Organization

  3. Alaliyat S (2008) Video-based fall detection in elderlys houses. Master’s thesis, Gjvik University College

  4. Auvinet E, Multon F, Saint-Arnaud A, Rousseau J, Meunier J (2011) Fall detection with multiple cameras: an occlusion-resistant method based on 3-d silhouette vertical distribution. IEEE Trans Inf Technol Biomed 15(2):290–300

    Article  Google Scholar 

  5. Auvinet E, Rougier C, Meunier J, St-Arnaud A, Rousseau J (2010) Multiple cameras fall dataset. DIRO-universite de Montreal, Tech Rep 1350

  6. Baldewijns G, Debard G, Mertes G, Vanrumste B, Croonenborghs T (2016) Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms. Healthcare Technology Letters 3(1):6–11

    Article  Google Scholar 

  7. Baptista-Ríos M, Martínez-García C, Losada-Gutiérrez C, Marrón-Romera M (2016) Human activity monitoring for falling detection. a realistic framework. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, pp 1–7

  8. Castillo JC, Carneiro D, Serrano-Cuerda J, Novais P, Fernández-Caballero A, Neves J (2014) A multi-modal approach for activity classification and fall detection. Int J Syst Sci 45(4):810–824

    Article  Google Scholar 

  9. Chen MC (2016) A video surveillance system designed to detect multiple falls. Adv Mech Eng 8(4):1687814016642,914

    Google Scholar 

  10. Chen YT, Lin YC, Fang WH (2010) A hybrid human fall detection scheme. In: 2010 17th IEEE International Conference on Image Processing (ICIP), IEEE, pp 3485–3488

  11. Cheng WC, Jhan DM (2013) Triaxial accelerometer-based fall detection method using a self-constructing cascade-adaboost-svm classifier. IEEE J Biomed Health Inform 17(2):411–419

    Article  Google Scholar 

  12. Chua JL, Chang YC, Lim WK (2015) A simple vision-based fall detection technique for indoor video surveillance. SIViP 3(9):623–633

    Article  Google Scholar 

  13. Cucchiara R, Prati A, Vezzani R (2007) A multi-camera vision system for fall detection and alarm generation. Expert Syst 24(5):334–345

    Article  Google Scholar 

  14. Dai X (2013) Vision-based 3d human motion analysis for fall detection and bed-exiting. Ph.D. thesis, University of Denver

  15. Delahoz YS, Labrador MA (2014) Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14(10):19,806–19,842

    Article  Google Scholar 

  16. Doukas CN, Maglogiannis I (2011) Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components. IEEE Trans Inf Technol Biomed 15(2):277–289

    Article  Google Scholar 

  17. Doulamis A, Doulamis N, Kalisperakis I, Stentoumis C (2010) A real-time single-camera approach for automatic fall detection. ISPRS Commission V Close Range Image Measurements Techniques 38:207–212

    Google Scholar 

  18. El-Bendary N, Tan Q, Pivot FC, Lam A (2013) Fall detection and prevention for the elderly: a review of trends and challenges. Int J Smart Sens Intell Syst 6(3)

  19. Ezatzadeh S, Keyvanpour MR (2017) Fall detection for elderly in assisted environments: Video surveillance systems and challenges. In: 9th International Conference on Information and Knowledge Technology (IKT 2017), IEEE, pp 93–98

  20. Fan K, Wang P, Hu Y, Dou B (2017) Fall detection via human posture representation and support vector machine. Int J Distrib Sens Netw 13(5):1–21

    Article  Google Scholar 

  21. Fan K, Wang P, Zhuang S (2018) Human fall detection using slow feature analysis. Multimedia Tools and Applications pp 1–28

  22. Feng W, Liu R, Zhu M (2014) Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera. SIViP 6 (8):1129–1138

    Article  Google Scholar 

  23. Foroughi H, Aski BS, Pourreza H (2008) Intelligent video surveillance for monitoring fall detection of elderly in home environments. In: 2008. ICCIT 2008. 11th International Conference on Computer and Information Technology, IEEE, pp 219–224

  24. Foroughi H, Rezvanian A, Paziraee A (2008) Robust fall detection using human shape and multi-class support vector machine. In: 2008. ICVGIP’08. Sixth Indian Conference on Computer Vision, Graphics & Image Processing, IEEE, pp 413–420

  25. Gripentog RJ (2015) Fall detection by using video. Master’s thesis. University of Nevada, Las Vegas

    Google Scholar 

  26. Hijaz F, Afzal N, Ahmad T, Hasan O (2010) Survey of fall detection and daily activity monitoring techniques. In: 2010 International Conference on Information and Emerging Technologies (ICIET), IEEE, pp 1–6

  27. Hsieh CY, Liu KC, Huang CN, Chu WC, Chan CT (2017) Novel hierarchical fall detection algorithm using a multiphase fall model. Sensors 17(2):307

    Article  Google Scholar 

  28. Htike ZZ, Egerton S, Chow KY (2011) A monocular view-invariant fall detection system for the elderly in assisted home environments. In: 2011 7th International Conference on Intelligent Environments (IE), IEEE, pp 40–46

  29. Igual R, Medrano C, Plaza I (2013) Challenges, issues and trends in fall detection systems. Biomed Eng Online 12(1):66

    Article  Google Scholar 

  30. Jansen B, Deklerck R (2006) Context aware inactivity recognition for visual fall detection. In: Pervasive Health Conference and Workshops, 2006, IEEE, pp 1–4

  31. Jiang M, Chen Y, Zhao Y, Cai A (2013) A real-time fall detection system based on hmm and rvm. In: Visual Communications and Image Processing (VCIP), 2013, IEEE, pp 1–6

  32. Khan SS, Hoey J (2017) Review of fall detection techniques: a data availability perspective. Med Eng Phys 39:12–22

    Article  Google Scholar 

  33. Koohzadi M, Keyvanpour M (2015) Otwc: an efficient object-tracking method. SIViP 9(6):1235–1247

    Article  Google Scholar 

  34. Koohzadi M, Keyvanpour MR (2014) An analytical framework for event mining in video data. Artif Intell Rev 41(3):401–413

    Article  Google Scholar 

  35. Koshmak G, Loutfi A, Linden M (2016) Challenges and issues in multisensor fusion approach for fall detection. Journal of Sensors 2016

  36. Liu H, Guo Y (2017) A vision-based fall detection algorithm of human in indoor environment. In: Second International Conference on Photonics and Optical Engineering, International Society for Optics and Photonics, pp 1025,644–1025,644

  37. Lotfi A, Albawendi S, Powell H, Appiah K, Langensiepen C (2018) Supporting independent living for older adults; employing a visual based fall detection through analysing the motion and shape of the human body. IEEE Access 6:70,272–70,282

    Article  Google Scholar 

  38. Ma X, Wang H, Xue B, Zhou M, Ji B, Li Y (2014) Depth-based human fall detection via shape features and improved extreme learning machine. IEEE J Biomed Health Inform 18(6):1915–1922

    Article  Google Scholar 

  39. Makantasis K, Protopapadakis E, Doulamis A, Doulamis N, Matsatsinis N (2016) 3d measures exploitation for a monocular semi-supervised fall detection system. Multimed Tools Appl 75(22):15,017–15,049

    Article  Google Scholar 

  40. Mastorakis G (2018) Human fall detection methodologies: from machine learning using acted data to fall modelling using myoskeletal simulation, Ph.D. thesis, Kingston University

  41. Min W, Zou S, Li J (2018) Human fall detection using normalized shape aspect ratio. Multimedia Tools and Applications, pp 1–23

  42. Mirmahboub B, Samavi S, Karimi N, Shirani S (2013) Automatic monocular system for human fall detection based on variations in silhouette area. IEEE Trans Biomed Eng 60(2):427–436

    Article  Google Scholar 

  43. Mohamed O, Choi HJ, Iraqi Y (2014) Fall detection systems for elderly care: a survey. In: 2014 6th International Conference on New Technologies, Mobility and Security (NTMS), IEEE, pp 1–4

  44. Mourchid Y, El Hassouni M, Cherifi H (2019) A general framework for complex network-based image segmentation. Multimedia Tools and Applications, pp 1–26

  45. Mubashir M, Shao L, Seed L (2013) A survey on fall detection: Principles and approaches. Neurocomputing 100:144–152

    Article  Google Scholar 

  46. Nadi M, El-Bendary N, Hassanien AE, Kim TH (2015) Falling detection system based on machine learning. In: 2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS), IEEE, pp 71–75

  47. Nizam Y, Mohd MNH, Jamil MMA (2016) A study on human fall detection systems: Daily activity classification and sensing techniques. International Journal of Integrated Engineering 8(1)

  48. Noury N, Fleury A, Rumeau P, Bourke A, Laighin G, Rialle V, Lundy J (2007) Fall detection-principles and methods. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, IEEE, pp 1663–1666

  49. Núñez-Marcos A, Azkune G, Arganda-Carreras I (2017) Vision-based fall detection with convolutional neural networks. Wirel Commun Mob Comput 2017

  50. Paul M, Haque SM, Chakraborty S (2013) Human detection in surveillance videos and its applications-a review. EURASIP Journal on Advances in Signal Processing 2013(1):176

    Article  Google Scholar 

  51. Rajabi H, Nahvi M (2015) An intelligent video surveillance system for fall and anesthesia detection for elderly and patients. In: 2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA), IEEE, pp 1–6

  52. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2006) 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, pp 6384–6387

  53. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2007) Fall detection from human shape and motion history using video surveillance. In: 2007, AINAW’07. 21st International Conference on Advanced Information Networking and Applications Workshops, vol 2. IEEE, pp 875–880

  54. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2011) Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circuits Syst Video Technol 21(5):611–622

    Article  Google Scholar 

  55. Shoaib M, Dragon R, Ostermann J (2010) View-invariant fall detection for elderly in real home environment. In: 2010 Fourth Pacific-rim Symposium on Image and Video Technology (PSIVT), IEEE, pp 52–57

  56. Solbach MD, Tsotsos JK (2017) Vision-based fallen person detection for the elderly. arXiv:1707.07608

  57. Su S, Wu SS, Chen SY, Duh DJ, Li S (2016) Multi-view fall detection based on spatio-temporal interest points. Multimed Tools Appl 75(14):8469–8492

    Article  Google Scholar 

  58. Suriani NS, Hussain A (2012) Sudden fall classification using motion features. In: 2012 IEEE 8th International Colloquium on Signal Processing and its Applications (CSPA), IEEE, pp 519–524

  59. Thome N, Miguet S, Ambellouis S (2008) A real-time, multiview fall detection system: a lhmm-based approach. IEEE Trans Circuits Syst Video Technol 18 (11):1522–1532

    Article  Google Scholar 

  60. Toreyin BU, Dedeoglu Y, Çetin AE (2005) Hmm based falling person detection using both audio and video. Lect Notes Comput Sci 3766:211

    Article  Google Scholar 

  61. Tu Z (2007) Learning generative models via discriminative approaches. In: 2007. CVPR’07. IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 1–8

  62. Vaidehi V, Ganapathy K, Mohan K, Aldrin A, Nirmal K (2011) Video based automatic fall detection in indoor environment. In: 2011 International Conference on Recent Trends in Information Technology (ICRTIT), IEEE, pp 1016–1020

  63. Vishwakarma V, Mandal C, Sural S (2007) Automatic detection of human fall in video. Pattern Recognition and Machine Intelligence, pp 616–623

  64. Wang K, Cao G, Meng D, Chen W, Cao W (2016) Automatic fall detection of human in video using combination of features. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1228–1233

  65. Wang S, Chen L, Zhou Z, Sun X, Dong J (2016) Human fall detection in surveillance video based on pcanet. Multimedia Tools Appl 75(19):11,603–11,613

    Article  Google Scholar 

  66. Yan C, Li L, Zhang C, Liu B, Zhang Y, Dai Q (2019) Cross-modality bridging and knowledge transferring for image understanding. IEEE Transactions on Multimedia

  67. Yan C, Xie H, Chen J, Zha Z, Hao X, Zhang Y, Dai Q (2018) A fast uyghur text detector for complex background images. IEEE Trans Multimedia 20 (12):3389–3398. https://doi.org/10.1109/TMM.2018.2838320

    Article  Google Scholar 

  68. Yang L, Ren Y, Hu H, Tian B (2015) New fast fall detection method based on spatio-temporal context tracking of head by using depth images. Sensors 15 (9):23,004–23,019

    Article  Google Scholar 

  69. Yang L, Ren Y, Zhang W (2016) 3d depth image analysis for indoor fall detection of elderly people. Digital Communications and Networks 2(1):24–34

    Article  Google Scholar 

  70. Yu M, Rhuma A, Naqvi SM, Wang L, Chambers J (2012) A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans Inf Technol Biomed 16(6):1274–1286

    Article  Google Scholar 

  71. Yu X (2008) Approaches and principles of fall detection for elderly and patient. In: 2008. Healthcom 2008. 10th International Conference on E-health Networking, Applications and services, IEEE, pp 42–47

  72. Zandian ZK, Keyvanpour M (2017) Systematic identification and analysis of different fraud detection approaches based on the strategy ahead. International Journal of Knowledge-based and Intelligent Engineering Systems 21(2):123–134

    Article  Google Scholar 

  73. Zerrouki N, Houacine A (2018) Combined curvelets and hidden markov models for human fall detection. Multimed Tools Appl 77(5):6405–6424

    Article  Google Scholar 

  74. Zhang Z, Conly C, Athitsos V (2015) A survey on vision-based fall detection. In: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, ACM, p 46

  75. Zhang Z, Liu W, Metsis V, Athitsos V (2012) A viewpoint-independent statistical method for fall detection. In: 2012 21st International Conference on Pattern Recognition (ICPR), IEEE, pp 3626–3630

  76. Zolfaghari S, Keyvanpour MR (2016) Sarf: Smart activity recognition framework in ambient assisted living. In: 2016 Federated Conference on Computer Science and Information Systems (fedCSIS), IEEE, pp 1435–1443

  77. Zolfaghari S, Keyvanpour MR, Zall R (2017) Analytical review on ontological human activity recognition approaches. Int J E-Bus Res (IJEBR) 13(2):58–78

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Alzahra University, Tehran, Iran

    Shabnam Ezatzadeh & Mohammad Reza Keyvanpour

Authors
  1. Shabnam Ezatzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Reza Keyvanpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Reza Keyvanpour.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ezatzadeh, S., Keyvanpour, M.R. ViFa: an analytical framework for vision-based fall detection in a surveillance environment. Multimed Tools Appl 78, 25515–25537 (2019). https://doi.org/10.1007/s11042-019-7720-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7720-3

Keywords

Search

Navigation