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Comparison of Motion History Image and Approximated Ellipse Method in Human Fall Detection System
https://doi.org/10.22146/ijccs.43632 Mohammad Brado Frasetyo(1*), Elvira Sukma Wahyuni(2), Hendra Setiawan(3)
(1) Universitas Islam Indonesia
(2) Universitas Islam Indonesia
(3) Universitas Islam Indonesia
(*) Corresponding Author
Abstract
This paper compares two different method in human fall detection system namely motion history image and approximated ellipse. Research has been done in small studio with 4 CCTV camera as video data recorder, whereas video data are processed using MATLAB software. The experiment was carried out using three object’s fall direction and two type of falling movement. The fall direction is consist of front, side, and back fall. Whereas the falling movement is consist of direct and indirect fall movement. Meanwhile, the object’s initial position is standing and size of captured object is constant. The result is motion history image has accuracy 74.26% for direct falling movement, and 75.69% for indirect falling movement. Whereas approximated ellipse has accuracy 56.85% for direct falling movement, and 61.81% for indirect falling movement. Therefore, motion history image is better than approximated ellipse in human fall detection system.
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[1] Setiawan Hendra, and Elvira Sukma Wahyuni, "Comparison of Some Methods for the Elderly Patient Telemonitoring System," Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 3, No. 3, 2018 [Online]. Available : http://kinetik.umm.ac.id/index.php/kinetik/article/view/627 [Accessed : 16-Oct-2018].
[2] Ye Z., Li Y., Zhao Q., & Liu X., “A falling detection system with wireless sensor for the elderly people based on ergonomics,” International Journal of Smart Home, 8(1), pp.187-196, 2014 [Online].
Available : https://www.semanticscholar.org/paper/A-Falling-Detection-System-with-wireless-sensor-for-Ye-Li/3d5282d2705f6f9dcbc6b9dfb8b2585a92b2da0b [Accessed : 10-Oct-2018]
[3] Dias P. V. G., Costa E. D. M., Tcheou M. P., & Lovisolo F., “Fall detection monitoring system with position detectionfor elderly at indoor environments under supervision,” In communications (LATINCOM), 8th IEEE Latin-American Conference on, pp. 1-6, 2016 [Online].
Available : https://ieeexplore.ieee.org/document/7811576[Accessed : 16-Oct-2018].
[4] Santiago J., Cotto E., Jaimes L. G., & Vergara-Laurens I. “Fall detection system for the elderly,” In Computing and Communication Workshop and Conference (CCWC), 2017 IEEE 7th Annual, pp. 1-4, 2017 [Online].
Available :https://ieeexplore.ieee.org/document/7868363 [Accessed : 16-Oct-2018].
[5] Hwang S., Ahn D., Park H., & Park T. “Maximizing Accuracy of Fall Detection and Alert Systems Based on 3D Convolutional Neural Network,” In Proceedings of the Second International Conference on Internet-of-Things Design and Implementation, pp. 343-344, ACM, 2017 [Online].
Available : https://ieeexplore.ieee.org/document/7946918[Accessed : 16-Oct-2018].
[6] Castillo J. C., Carneiro D., Serrano-Cuerda J., Novais, P., Fernández-Caballero A., & Neves J. “A multi-modal approach for activity classification and fall detection,” International Journal of Systems Science, 45(4), pp.810-824, 2014 [Online].
Available:https://www.researchgate.net/publication/262171738_A_Multi-Modal_Approach_for_Activity_Classification_and_Fall_Detection [Accessed : 14-Oct-2018].
[7] Rougier Caroline, et al., "Fall detection from human shape and motion history using video surveillance," Advanced Information Networking and Applications Workshops, 2007, AINAW'07. 21st International Conference on. Vol. 2, IEEE, 2007 [Online].
Available :https://ieeexplore.ieee.org/document/4224216 [Accessed : 12-Oct-2018].
[8] Worrakulpanit, Nuttapong, and Pranchalee Samanpiboon, "Human fall detection using standard deviation of C-motion method," Journal of Automation and Control Engineering, Vol. 2, No. 4, 2014 [Online].
Available:http://www.joace.org/uploadfile/2014/0114/20140114120626761.pdf [Accessed : 12-Oct-2018].
[9] Albawendi, Suad, et al., "Video based fall detection with enhanced motion history images," Proceedings of the 9th ACM International Conference on Pervasive Technologies Related to Assistive Environments, ACM, 2016 [Online].
Available:https://dl.acm.org/citation.cfm?id=2935832 [Accessed : 12-Oct-2018].
[10] Bobick Aaron F., and James W. Davis, "The recognition of human movement using temporal templates." IEEE Transactions on pattern analysis and machine intelligence, Vol. 23, No. 3, 2001 [Online].
Available : https://ieeexplore.ieee.org/document/910878 [Accessed : 10-Oct-2018] .
[11] ES Wahyuni, et al. ”Combination of motion history image and approximated ellipse on human fall detection system,” 8th International Conference on Intelligent Systems, Modelling and Simulation ISMS2018, IJSST, Vol. 19, No. 3, 2018 [Online].
Available:http://ijssst.info/Vol-19/No-3/paper13.pdf [Accessed : 11-Oct-2018].
DOI: https://doi.org/10.22146/ijccs.43632
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