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Comparative Study Of Computer Vision Algorithms For Fall Detection

José Camilo Eraso Guerrero
Universidad CESMAG
Elena Muñoz España
Universidad del Cauca
Mariela Muñoz Añasco
Universidad del Cauca
Miller Manuel Rúales Luna
Universidad CESMAG
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Abstract

Objective: To compare the performance of three human fall recognition algorithms, focusing on computer vision. The comparison will be carried out by evaluating their performance on various databases commonly used by the scientific community, as well as on a new database called CAUCAFall. Methodology: The study compares three algorithms, selected through a systematic review that considered articles working with computer vision, RGB cameras and public databases. The selected algorithms focus on feature extraction and convolutional neural networks using YOLO and OPENPOSE. Results: The study found that all three algorithms perform well on databases commonly used by the scientific community. However, inferior performance was observed when evaluating the algorithms on CAUCAFall, which contains loosely controlled environments closer to reality. Conclusions: The research highlights the importance of evaluating human fall recognition algorithms in more realistic scenarios. It raises the importance of future research that focuses on creating and evaluating algorithms on databases that contain scenarios closer to reality, which would be a significant advance in the area of human fall recognition.

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References

  1. OMS | “Datos interesantes acerca del envejecimiento,” WHO. (2015). En: https://www.who.int/ageing/about/facts/es/
  2. G. Horng y K. Chen, “The Smart Fall Detection Mechanism for Healthcare Under Free‑Living Conditions,” Springer, Wireless Personal Communications. 118:715–753, 2021. https://doi.org/10.1007/s11277-020-08040-4.
  3. N. Thakur y C. Han, “A Study of Fall Detection in Assisted Living: Identifying and Improving the Optimal Machine Learning Method,” J. Sens. Actuator Netw. 10, 39, 2021. https://doi.org/10.3390/jsan10030039
  4. F. Shu y J. Shu, “An eight‑camera fall detection system using human fall pattern recognition via machine Learning by a low‑cost android box,” Scientific Reports, 2021. 11:2471. https://doi.org/10.1038/s41598-021-81115-9
  5. H. Li, H. Shrestha, F. Fioranelli, K. Le y H. Heidari, “Hierarchical Classification on Multimodal Sensing for Human Activity Recogintion and Fall Detection,” 2018 IEEE SENSORS, pp. 1–4. 2018. Doi: 10.1109/ICSENS.2018.8589797
  6. S. Amiri, M. Pourazad, P. Nasiopoulos y V. Leung, “Improved human action recognition in a smart home environment setting,” IRBM. Vol. (35), no. 6, pp. 321–328, 2014.
  7. M. Yu, Y. Yu, Y. Rhuma, S. Naqvi, L. Wang y J. Chambers, “An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment,” IEEE J. Biomed. Heal. Informatics. Vol. (17), no. 6, pp. 1002–1014, 2013.
  8. F. Concone, G. Re y M. Morana, “A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices,” ACM Transactions on Internet Technology. Vol. (19), no. 2, 2019.
  9. G. Debard, M. Mertens, M. Deschodt, E. Vlaeyen, E. Devriendt, E. Dejaeger, K. Milisen, J. Tournoy, T. Croonenborghs, T. Goedemé, T. Tuytelaars, B. Vanrumste," Camera-based fall detection using real-world versus simulated data: how far are we from the solution?," Journal of Ambient Intelligence and Smart Environments, pp. 1-19, 2015.
  10. Y. Fan, M. Levine, G. Wen, S. Qiu," A deep neural network for real-time detection of falling humans in naturally occurring scenes," Neurocomputing. Vol.260, pp.43-18, oct, 2017.
  11. S. Nwe Htun, T. Zin y P. Tin. " Image Processing Technique and Hidden Markov Model for an Elderly Care Monitoring System," J. Imaging 6, 49, 2020. doi:10.3390/jimaging6060049
  12. Q. Feng, C.Gao, L. Wang, Y. Zhao, T. Song, Q. Li, “Spatio-temporal fall event detection in complex scenes using attention guided LSTM”, Pattern Recognition Letters .2020. doi: https://doi.org/10.1016/j.patrec.2018.08.031
  13. Q. Xu, G. Huang, M. Yu, Y. Guo" Fall prediction based on key points of human bones," Physica A. 2019. https://doi.org/10.1016/j.physa.2019.123205
  14. J.C. Eraso, E. Muñoz, M. Muñoz, J. Pinto, “Dataset for human fall recognition in an uncontrolled environment,” Data in Brief, Volume 45, December 2022. https://doi.org/10.1016/j.dib.2022.108610
  15. G. Goudelis, G. Tsatiris, T. Karpouzis y S. Kollias, “Fall detection using history triple features,” 8th ACM Int. Conf. PErvasive Technol. Relat. to Assist. Environ. PETRA 2015 - Proc., 2015.
  16. Z. Khan y W. Sohn, “Abnormal human activity recognition system based on R-transform and kernel discriminant technique for elderly home care,” IEEE Trans. Consum. Electron. Vol. (57), no. 4, pp. 2021.
  17. M. Yu, S. Naqvi, A. Rhuma y J. Chambers, “One class boundary method classifiers for application in a video-based fall detection system,” IET Comput. Vis. Vol. (6), no. 2, pp. 90–100, 2012.
  18. H. Foroughi, B. Aski, y H. Pourreza, “Intelligent Video Surveillance for Monitoring Fall Detection of Elderly in Home Environment,” Proc. 11th Int. Conf. Comput. Inf. Technol. pp. 219–224, 2008.
  19. C. Rougier, J. Meunier, A. St-Arnaud y J. Rousseau, “Fall detection from human shape and motion history using video surveillance,” Proc. - 21st Int. Conf. Adv. Inf. Netw. Appl. Work. AINAW’07, 2007.
  20. E. Auvinet, F. Multon, A. St-Arnaud, J. Rousseau y J. Meunier, “Fall detection with multiple cameras: An occlusion-resistant method based on 3-D silhouette vertical distribution,” IEEE Trans. Inf. Technol. Biomed. Vol. (15), no. 2, pp. 290–300, 2011.
  21. A. Efros, A. Berg, G. Mori y J. Malik, “Recognizing action at a distance,” Proc. IEEE Int. Conf. Comput. Vis., pp. 726–733, 2008.
  22. S. Zhang, Z. Wei, J. Nie, L. Huang, L. Wang y Z. Li, “A Review on Human Activity Recognition Using Vision-Based Method,” Journal of Healthcare Engineering, jul, 2017.
  23. S. Venkatesha y M. Turk, “Human activity recognition using local shape descriptors,” Proc. - Int. Conf. Pattern Recognit., pp. 3704–3707, 2010.
  24. X. Peng, L. Wang, X. Wang y Y. Qiao, “Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice,” Comput. Vis. Image Underst., 2016.
  25. D.Dawn y S. Shaikh, “A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector,” Vis. Comput., mar, 2015.
  26. Y. Zhu, X. Zhao, Y. Fu y Y. Liu, Y, “Sparse coding on local spatial-temporal volumes for human action recognition,” Springer-Verlag Berlin Heidelberg, pp. 660–671, 2011.
  27. Zhang et al., “A comprehensive survey of vision-based human action recognition methods,” Sensors (Switzerland). Vol. (19), no. 5, pp. 1–20, 2019.
  28. Y. Hsieh y Y. Jeng, “Development of Home Intelligent Fall Detection IoT System based on Feedback Optical Flow Convolutional Neural Network,” IEEE Access. Vol. (6), pp. 6048 - 6057, 2017.
  29. A.Núñez, G. Azkune, I. Arganda, " Vision-Based Fall Detection with Convolutional Neural Networks," Wireless Communications and Mobile Computing. 2017.
  30. R. Espinosa, H. Ponceb, S. Gutiérreza, L. Martínez, J. Brievab, E. Moya, "A Vision-Based Approach for Fall Detection Using Multiple Cameras and Convolutional Neural Networks: A Case Study Using the UP-Fall Detection Dataset," Computers in Biology and Medicine. 2019. doi: https://doi.org/10.1016/j.compbiomed.2019.103520
  31. M. Rahnemoonfar, H. Alkittawi, "Spatio-temporal convolutional neural network for elderly fall detection in depth video camerasl," IEEE International Conference on Big Data, 2018.
  32. K. Adhikari, H. Bouchachia y H. Nait, "Activity Recognition for Indoor Fall Detection Using Convolutional Neural Network," International Conference on Machine Vision Applications (MVA). 2017.
  33. N. Lu, X. Ren, J. Song, Y. Wu, "Visual Guided Deep Learning Scheme for Fall Detection, "IEEE Conference on Automation Science and Engineering (CASE). 2017.
  34. Y. Guo, A. Shimada, H. Uchiyama, C. Ma, H. Nagahara, R. Taniguchi, " CNN for face detection with thermal image," 2018(16・18-23):2018.3.26 p.5-8, 2018. http://id.ndl.go.jp/bib/028999107
  35. C. Khraief, F. Benzarti y H. Amiri, " Elderly fall detection based on multi-stream deep convolutional networks," Multimedia Tools and Applications volume 79, pages19537–19560, 2020. https://link.springer.com/article/10.1007%2Fs11042-020-08812-x
  36. A. Kaid, K. Baina, J. Baina, "Reduce False Positive Alerts for Elderly Person Fall Video-Detection Algorithm by convolutional neural network model," Procedia Computer Science. Vol. 149, pp.2-11, 2019.
  37. J. C. Eraso Guerrero, E. Muñoz España, y M. Muñoz Añasco, “Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review,” Tecnura, vol. 26, n.º 74, pp. 213–236, oct. 2022. https://doi.org/10.14483/22487638.17413
  38. J. Gutiérrez, V. Rodríguez y S. Martin, “Comprehensive Review of Vision-Based Fall Detection Systems," Sensors 2021, 21, 947. 2021. https://doi.org/10.3390/s21030947
  39. M. Kepski, B. Kwolek, “Embedded system for fall detection using body-worn accelerometer and depth sensor,” In Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Warsaw, Poland, 24–26 September 2015; Volume 2, pp. 755–759, 2015.
  40. E. Auvinet, C. Rougier, J. Meunier, A. St-Arnaud, J. Rousseau, “Multiple Cameras Fall Data Set,” Technical Report Number 1350; University of Montreal: Montreal, QC, Canada, 8 July, 2011.
  41. I. Charfi, J. Miteran, J. Dubois, M. Atri, R. Tourki, “Optimized spatio-temporal descriptors for real-time fall detection: Comparison of support vector machine and Adaboost-based classification,” J. Electron. Imaging 2013, 22, 041106,2013.
  42. K. Adhikari, H. Bouchachia, H. Nait-Charif, “Activity recognition for indoor fall detection using convolutional neural network,” In Proceedings of the 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), Nagoya, Japan, 8–12 May, 2017.
  43. L. Martínez-Villaseñor, H. Ponce, J. Brieva, E. Moya-Albor, J. Núñez-Martínez, C. Peñafort-Asturiano, “UP-Fall Detection Dataset: A Multimodal Approach”, Sensors 19(9), 1988: 2019. doi:10.3390/s19091988.
  44. MOT Dataset. Available online: https://motchallenge.net/ (accessed on 11 November 2023).
  45. T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, C. Zitnick, “Microsoft coco: Common objects in context,” In Computer Vision-ECCV 2014, ECCV 2014. Lecture Notes in Computer Science; Springer: Cham, Swizerland, 2014; pp. 740–755
  46. CENTRE FOR DIGITAL HOME—MMU. Available online: http://foe.mmu.edu.my/digitalhome/FallVideo.zip (accessed on 11 November 2023)
  47. A. Shahroudy, J. Liu, T. Ng, G. Wang, “NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis,” In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 1010–1019, 2016.
  48. F. Harrou, N. Zerrouki, Y. Sun, A. Houacine, “Vision-based fall detection system for improving safety of elderly people,” IEEE Instrum. Meas. Mag. 2017, 20, 49–55, 2017. [CrossRef]
  49. S. Ali, R. Khan, A. Mahmood, M. Hassan, M. Jeon, “Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection,” Sensors 2018, 18, 1918, 2018. [CrossRef]
  50. D. Kumar, A. Ravikumar, V. Dharmalingam, V. Kafle, “Elderly Health Monitoring System with Fall Detection Using Multi-Feature Based Person Tracking,” In Proceedings of the 2019 ITU Kaleidoscope: ICT for Health: Networks, Standards and Innovation (ITU K), Atlanta, GA, USA, 4–6 December 2019. [CrossRef]
  51. F. Harrou, N. Zerrouki, Y. Sun, A. Houacine, “An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment,” IEEE Access 2019, 7, 114966–114974, 2019. [CrossRef]
  52. Y. Fan, M.D. Levine, G. Wen, S. Qiu, “ A deep neural network for real-time detection of falling humans in naturally occurring scenes,” Neurocomputing 2017, 260, 43–58.2017. [CrossRef]
  53. W. Min, H. Cui, H. Rao, Z. Li, L. Yao, “Detection of Human Falls on Furniture Using Scene Analysis Based on Deep Learning and Activity Characteristics,” IEEE Access 2018, 6, 9324–9335. 2018. [CrossRef]
  54. C. Ma, A. Shimada, H. Uchiyama, H. Nagahara, R. Taniguchi, “Fall detection using optical level anonymous image sensing system,” Opt. Laser Technol. 2019, 110, 44–61. 2019. [CrossRef]
  55. R. Espinosa, H. Ponce, S. Gutiérrez, L. Martínez-Villaseñor, J. Brieva, E. Moya-Albor, “vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection Dataset,” Comput. Biol. Med. 2019, 115, 103520. 2019. [CrossRef]
  56. B. Wang, J. Yu, K. Wang, X. Bao, K. Mao, “Fall Detection Based on Dual-Channel Feature Integration,“ IEEE Access 2020, 8, 103443–103453. 2020. [CrossRef]
  57. S. Htun, T. Zin y H. Hama, “Virtual Grounding Point Concept for Detecting Abnormal and Normal Events in Home Care Monitoring Systems,” Appl. Sci. 2020, 10, 3005; 2020. doi:10.3390/app10093005
  58. J. Redmon, A. Farhadi, “YOLOv3: An Incremental Improvement,”. 8 Apr, 2018 https://arxiv.org/abs/1804.02767
  59. E. De la Hoz Domínguez, T. Fontalvo Herrera., y A. Mendoza Mendoza, “Machine Learning and SMEs: Opportunities for an improved decision -making process”, Investigación e Innovación en Ingenierías, vol. 8, n°. 1, 2020. DOI: https://doi.org/10.17081/invinno.8.1.3506
  60. Z. Cao, G. Hidalgo, T. Simon, S. Wei y Y. A. Sheikh, “OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields,“ IEEE Transactions on Pattern Analysis and Machine Intelligence. 2019. https://github.com/CMU-Perceptual-Computing-Lab/openpose
How to Cite
[1]
J. C. Eraso Guerrero, E. Muñoz España, M. Muñoz Añasco, and M. M. Rúales Luna, “Comparative Study Of Computer Vision Algorithms For Fall Detection”, Investigación e Innovación en Ingenierías, vol. 12, no. 2, pp. 38–57, Aug. 2024.

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