Indoor Human Fall Detection Method Based on Infrared Images and Back-Projection Algorithm

CHEN Han; YU Lei; PENG Sitian; NIE Hong; OU Qiaofeng; XIONG Bangshu

Volume 43 Issue 10

Oct. 2021

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Infrared Technology > 2021 > 43(10): 968-978.

CHEN Han, YU Lei, PENG Sitian, NIE Hong, OU Qiaofeng, XIONG Bangshu. Indoor Human Fall Detection Method Based on Infrared Images and Back-Projection Algorithm[J]. Infrared Technology , 2021, 43(10): 968-978.

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Indoor Human Fall Detection Method Based on Infrared Images and Back-Projection Algorithm

1.
Key Laboratory of Image Processing and Pattern Recognition of Jiangxi Province Nanchang Hangkong University, Nanchang 330063, China
2.
Department of Technology, University of Northern Iowa, Iowa, 50614-0178, USA

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Received Date: November 09, 2020
Revised Date: January 11, 2021

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Abstract

Abstract

Falls are reported to be a major cause of injury in China's elderly population. Shortening the time between the fall and subsequent treatment can reduce injuries caused by falls; therefore, the demand for indoor fall detection is increasing annually. Infrared image-based human fall detection methods are becoming increasingly popular owing to advantages such as being unaffected by light and non-intrusive. However, the traditional methods perform low accuracy because it is difficult to extract the features from the infrared videos at low resolution and high noise. Hence, this paper proposes an indoor human fall detection method based on a back-projection algorithm. First, the distance between the human body and the sensor is calculated using the human body temperature. Second, the height of the human body in the real world is reversely deduced using image information. Finally, the human body height data are smoothed and used for fall detection based on the height variation. The experimental results show that the detection accuracy of the proposed method is 98.57%, which is better than that of traditional projection methods. Therefore, it can be used for detection in real-life situations.
- human fall detection,
- back projection algorithm,
- infrared image,
- human localization,
- feature smoothing

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[1]	邓志锋, 闵卫东, 邹松. 一种基于CNN和人体椭圆轮廓运动特征的摔倒检测方法[J]. 图学学报, 2018, 39(6): 1042-1047. https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX201806005.htm DENG Zhifeng, MIN Weidong, ZOU Song. A fall detection method based on CNN and motion features of human elliptical contour[J]. Journal of Graphics, 2018, 39(6): 1042-1047. https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX201806005.htm
[2]	Giovanna S, Ivanoe D, Giuseppe D P. A supervised approach to automatically extract a set of rules to support fall detection in an mHealth system[J]. Applied Soft Computing, 2015, 34: 205-216. DOI: 10.1016/j.asoc.2015.04.060
[3]	胡双杰, 秦建邦, 郭薇. 基于特征自动提取的跌倒检测算法[J]. 传感技术学报, 2018, 31(12): 1842-1847. https://www.cnki.com.cn/Article/CJFDTOTAL-CGJS201812011.htm HU Shuangjie, QIN Jianbang, GUO Wei. A fall detection algorithm with automatic feature extraction[J]. Chinese Journal of Sensors and Actuators, 2018, 31(12): 1842-1847. https://www.cnki.com.cn/Article/CJFDTOTAL-CGJS201812011.htm
[4]	郑毅, 李凤, 张丽, 等. 基于长短时记忆网络的人体姿态检测方法[J]. 计算机应用, 2018, 38(6): 1568-1574. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201806008.htm ZHENG Yi, LI Feng, ZHANG Li, et al. Human posture detection method based on long short term memory network[J]. Journal of Computer Applications, 2018, 38(6): 1568-1574. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201806008.htm
[5]	Giuffrida D, Benetti G, Martini D D, et al. Fall detection with supervised machine learning using wearable sensor[C]//Proceedings of 17th International Conference on Industrial Informatics (INDIN) of IEEE, 2019: 253-259.
[6]	Kumar V S, Acharya K G, Sandeep B, et al. Wearable sensor-based human fall detection wireless system[J]. Wireless Communication Networks and Internet of Things, 2019, 493: 217-234 DOI: 10.1007/978-981-10-8663-2_23
[7]	Mehmood A, Nadeem A, Ashraf M, et al. A novel fall detection algorithm for elderly using Shimmer wearable sensors[J]. Health and Technology, 2019, 9(4): 631-646. DOI: 10.1007/s12553-019-00298-4
[8]	Kerdjidj O, Ramzan N, Ghanem K, et al. Fall detection and human activity classification using wearable sensors and compressed sensing[J]. Journal of Ambient Intelligence and Humanized Computing, 2020, 11(1): 349-361. DOI: 10.1007/s12652-019-01214-4
[9]	MIN W D, CUI H, RAO H, et al. Detection of human falls on furniture using scene analysis based on deep learning and activity characteristics[J]. IEEE Access, 2018, 6(99): 9324-9335. http://www.onacademic.com/detail/journal_1000040195826110_71bc.html
[10]	KONG Y Q, HUANG J H, HUANG S S, et al. Learning spatiotemporal representations for human fall detection in surveillance video[J]. Journal of Visual Communication and Image Representation, 2019, 59: 215-230. DOI: 10.1016/j.jvcir.2019.01.024
[11]	QIU Z, LIANG X Q, CHEN Q Q, et al. Old man fall detection based on surveillance video object tracking[C]//Proceedings of 10th International Symposium on Parallel Architectures, Algorithms and Programming, 2019: 159-167.
[12]	FAN K B, WANG P, ZHUANG S. Human fall detection using slow feature analysis[J]. Multimedia Tools and Applications, 2019, 78: 9101-9128. DOI: 10.1007/s11042-018-5638-9
[13]	Shota M, Jihoon H and Tomoaki O. A fall detection system using low resolution infrared array sensor[C]//Proceedings of 25th International Symposium on Personal, Indoor and Mobile Radio Communications of IEEE, 2014: 2109-2113.
[14]	杨任兵, 程文播, 钱庆, 等. 红外图像中基于多特征提取的跌倒检测算法研究[J]. 红外技术, 2017, 39(12): 1131-1138. http://hwjs.nvir.cn/article/id/hwjs201712011 YANG Renbing, CHENG Wenbo, QIAN Qing, et al. Research on fall detection algorithm based on multi-feature extraction in infrared image[J]. Infrared Technology, 2017, 39(12): 1131-1138. http://hwjs.nvir.cn/article/id/hwjs201712011
[15]	王召军, 许志猛, 陈良琴. 基于红外阵列传感器的人体行为识别系统研究[J]. 红外技术, 2020, 42(3): 231-237. http://hwjs.nvir.cn/article/id/hwjs202003005 WANG Zhaojun, XU Zhimeng, CHEN Liangqin. Human behavior recognition system based on infrared array sensors[J]. Infrared Technology, 2020, 42(3): 231-237. http://hwjs.nvir.cn/article/id/hwjs202003005
[16]	LIANG Q S, YU L, ZHAI X P, et al. Activity recognition based on thermopile imaging array sensor[C]//Proceedings of International Conference on Electro/Information Technology (EIT) of IEEE, 2018: 770-773.

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