Abstract
Falls are reported to be the leading causes of accidental deaths among elderly people. Automatic detection of falls from video sequences is an assistant technology for low-cost health care systems. In this paper, we present a novel slow feature analysis based framework for fall detection in a house care environment. Firstly, a foreground human body is extracted by a background subtraction technique. After morphological operations, the human silhouette is refined and covered by a fitted ellipse. Secondly, six shape features are quantified from the covered silhouette to represent different human postures. With the help of the learned slow feature functions, the shape feature sequences are transformed into slow feature sequences with discriminative information about human actions. To represent the fall incidents, the squared first order temporal derivatives of the slow features are accumulated into a classification vector. Lastly, falls are distinguished from other daily actions, such as walking, crouching, and sitting, by the trained directed acyclic graph support vector machine. Experiments on the multiple-camera fall dataset and the SDUFall dataset demonstrate that our method is comparable to other state-of-the-art methods, achieving 94.00% recognition rate on the former dataset and 96.57% on the latter one.
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References
Ageing WHO (2008) Who global report on falls prevention in old age. Technical Report, World Health Organization
Amin MG, Zang YD, Ahmad F et al (2016) Radar signal process for elderly fall detection: the future for in-home monitoring. IEEE Signal Proc Mag 33(2):71–80
Aslan M, Sengur A, Xiao Y et al (2015) Shape feature encoding via fisher vector for efficient fall detection in depth-videos. Appl Soft Comput 37:1023–1028
Auvinet E, Rougier C, Meunier J et al (2010) Multiple cameras fall dataset. Technical Report, DIRO-Université de Montréal
Barnich O, Van D (2011) Vibe: a universal background subtraction algorithm for video sequences. IEEE T Image Process 20(6):1709–1724
Berkes P, Wiskott L (2005) Slow feature analysis yields a rich repertoire of complex cell properties. J Vision 5(6):579–602
Bosch-Jorge M, Sanchez-Salmeron AJ, Valera A (2014) Fall detection based on the gravity vector using a wide-angle camera. Expert Syst Appl 41(17):7980–7986
Chaudhuri S, Thompson H, Demiris G (2014) Fall detection devices and their use with older adults: a systematic review. J Geriatr Phys Ther 37(4):178–196
Chua J, Chang Y, Lim W (2013) A simple vision-based fall detection technique for indoor video surveillance. Signal Image Video P 9(3):623–633
Crispim-Junior C, Buso V, Avgerinakis K et al (2016) Semantic event fusion of different visual modality concepts for activity recognition. IEEE Trans Patt Anal Mach Intell 38(8):1598–1611
Duda R, Hart P, Stork D (2000) Pattern classification, 2nd edn. Wiley, New Jersey
Erden F, Velipasalar S, Alkar AZ et al (2016) Sensors in assisted living: a survey of signal and image processing methods. IEEE Signal Proc Mag 33(2):36–44
Hamm J, Money A, Atwal A et al (2016) Fall prevention intervention technologies: a conceptual framework and survey of the state of the art. J Biomed Inform 59:319–335
Hassan MM, Lin K, Yue X et al (2017) A multimedia healthcare data sharing approach through cloud-based body area network. Future Gener Comp Sy 66:48–58
Heikkil M, Pietikinen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Patt Anal Mach Intell 28(4):657–662
Horprasert T, Harwood D, Davis L (1999) A statistical approach for real-time robust background subtraction and shadow detection. In: Proceedings of international conference on computer vision, pp 1-19
Hossain MS, Hossain SA, Alamri A et al (2013) Ant-based service selection framework for a smart home monitoring environment. Multimed Tools Appl 67(2):433–453
Igual R, Medrano C, Plaza I (2013) Challenges issues and trends in fall detection systems. Biomed Eng Online 12(66):1–66
Islam SMR, Kwak D, Kabir MDH et al (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708
Khan MS, Yu M, Feng P et al (2015) An unsupervised acoustic fall detection system using source separation for sound interference suppression. Signal Process 110 (61):199–210
Khan S, Hoey J (2017) Review of fall detection techniques: a data availability perspective. Med Eng Phys 39:12–22
Koshmak G, Loutfi A, Linden M (2015) Challenges and issues in multisensor fusion approach for fall detection: review paper. J Sensors 2016:1–16
Liu CL, Lee CH, Lin PM (2010) A fall detection system using k-nearest neighbor classifier. Expert Syst Appl 37(10):7174–7178
Ma X, Wang H, Xue B (2014) Depth-based human fall detection via shape features and improved extreme learning machine. IEEE J Biomed Health 18 (6):1915–1922
Madarshahian R, Caicedo J, Zambrana DA (2016) Benchmark problem for human activity identification using floor vibrations. Expert Syst Appl 62:263–272
Meng L, Miao C, Leung C (2017) Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing. Multimed Tools Appl 76(8):10779–10799
Mirmahboub B, Samavi S, Karimi N et al (2013) Automatic monocular system for human fall detection based on variations in silhouette area. IEEE T Biomed Eng 60(2):427–436
Mubashir M, Shao L, Seed L (2013) A survey on fall detection: principles and approaches. Neurocomputing 100(16):144–152
Noury N, Fleury A, Rumeau P et al (2007) Fall detection-principles and methods. In: Proceedings of 29th annual international conference of the engineering in medicine and biology society, pp 1663– 1666
Olivieri DN, Conde IG, Sobrino XA (2012) Eigenspace-based fall detection and activity recognition from motion templates and machine learning. Expert Syst Appl 39(5):5935–5945
Platt JC, Cristianini N, Shawe-Taylor J (1999) Large margin dags for multiclass classification. In: Proceedings of Conference on Neural Information Processing Systems, pp 547–553
Poppe R (2010) A survey on vision-based human action recognition. Image Vision Comput 28(6):976–990
Pratt WK, Adams JE (2007) Digital image processing, 4th edn. Prentice Hall, New Jersey
Salem O, Guerassimov A, Mehaoua A et al (2013) Anomaly detection scheme for medical wireless sensor networks. Springer, New York
Shin I, Son J, Ahn S et al (2015) A novel short-time fourier transform-based fall detection algorithm using 3-axis accelerations. Math Probl Eng 2015(2015):1–8
Su S, Wu SS, Chen SY (2016) Multi-view fall detection based on spatio-temporal interest points. Multimed Tools Appl 75(14):8469–8492
Vapnik V (2013) The nature of statistical learning theory. Springer Science and Business Media, Berlin
Wang S, Chen L, Zhou Z et al (2016) Human fall detection in surveillance video based on pacnet. Multimed Tools Appl 75(19):11603–11613
Weinland D, Ronfard R, Boyer E (2011) A survey of vision-based methods for action representation segmentation and recognition. Comput Vis Image Und 115(2):224–241
Wickramasinghe A, Torres RLS, Ranasinghe DC (2017) Recognition of falls using dense sensing in an ambient assisted living environment. Pervasive Mob Comput 34:14–24
Wiskott L, Sejnowski TJ (2002) Slow feature analysis: unsupervised learning of invariances. Neural Comput 14(4):715–770
Yoon HJ, Ra HK, Park T et al (2015) Fades: behavioral detection of falls using body shapes from 3D joint data. J Amb Intel Smart En 7(6):861–877
Yu M, Rhuma A, Naqvi S et al (2012) A posture recognition based fall detection system for monitoring an elderly person in a smart home environment. IEEE T Inf Technol B 16(6):1274–1286
Yun Y, Gu YH (2016) Human fall detection in videos by fusing statistical features of shape and motion dynamics on riemannian manifolds. Neurocomputing 207:726–734
Zhang Z, Conly C, Athitsos V (2015) A survey on vision-based fall detection. In: Proceedings of 8th ACM international conferences on pervasive technologies related to assistive environments, pp 1-7
Zhang Z, Tao D (2012) Slow feature analysis for human action recognition. IEEE Trans Patt Anal Mach Intell 34(3):436–450
Acknowledgements
This work was supported by Department of Science and Technology in Hebei Province China with Grant No.12213519D1. The authors also would like to thank the anonymous editors and reviewers for their insightful comments and suggestions which improved this work.
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Fan, K., Wang, P. & Zhuang, S. Human fall detection using slow feature analysis. Multimed Tools Appl 78, 9101–9128 (2019). https://doi.org/10.1007/s11042-018-5638-9
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DOI: https://doi.org/10.1007/s11042-018-5638-9