基于TSSI和STB-CNN的跌倒检测算法

黄晓湧; 李伟彤

doi:10.12052/gdutxb.220078

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基于TSSI和STB-CNN的跌倒检测算法

计算机科学与技术 | 更新时间：2026-05-27

- 基于TSSI和STB-CNN的跌倒检测算法
- Fall Detection Algorithm Based on TSSI and STB-CNN
- 广东工业大学学报 2023年40卷第4期页码：53-59
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
  
  黄晓湧 (1996–) ，男，硕士研究生，主要研究方向为计算机视觉
  李伟彤 (1969–)，男，副教授，主要研究方向为图像处理，E-mail：liweitong@gdut.edu.cn
- 基金信息：
  
  广东省科技计划项目(2017A010101016)
- DOI：10.12052/gdutxb.220078
  中图分类号： TP391.4
- 纸质出版：2023
- 稿件说明：
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黄晓湧, 李伟彤. 基于TSSI和STB-CNN的跌倒检测算法[J]. 广东工业大学学报, 2023, 40(4): 53-59.

Huang Xiao-yong, Li Wei-tong. Fall Detection Algorithm Based on TSSI and STB-CNN[J]. 2023, 40(4): 53-59.
黄晓湧, 李伟彤. 基于TSSI和STB-CNN的跌倒检测算法[J]. 广东工业大学学报, 2023, 40(4): 53-59. DOI： 10.12052/gdutxb.220078.

Huang Xiao-yong, Li Wei-tong. Fall Detection Algorithm Based on TSSI and STB-CNN[J]. 2023, 40(4): 53-59. DOI： 10.12052/gdutxb.220078.

摘要

跌倒行为会给老人特别独居老人带来严重伤害，准确识别跌倒并及时报警可以有效降低这种危险。本文提出一种基于树结构骨架图像(Tree Structure Skeleton Image，TSSI) 和可学习时空块卷积神经网络 (Spatio-temporal Block Convolution Neural Network，STB-CNN) 的跌倒检测方法。首先使用三维姿态估计算法提取人体关节点，进而获得骨架序列；然后利用基于深度优先搜索(Depth First Search，DFS) 算法将骨架序列编码为TSSI；最后构建由时空差分模块、可学习时空框架和时空多分支卷积模块组成的可学习STB-CNN网络，实现跌倒检测。该方法在公开数据集和自建数据集上进行仿真实验分别取得98.6%和98.3%的准确率，优于其他相关算法。

Abstract

Falling behavior will bring serious injury to the elderly

especially to the elderly living alone. How to correctly identify the falling behavior and issue a warning is an important factor for reducing the injury. A fall detection algorithm is proposed

which is based on TSSI (tree structure skeleton image) and learnable STB-CNN (spatio-temporal block convolutional neural network) . Firstly

human joint point is extracted by the three-dimensional pose estimation algorithm

and the corresponding skeleton sequence can be obtained. Secondly

the skeleton sequence is encoded into TSSI by the algorithm based DFS (depth first search) method. Finally

a learnable STB-CNN is proposed to classify TSSI and detect the fall behavior

which consists of spatio-temporal difference module

learnable spatio-temporal framework and spatio-temporal multi-branch convolution module

. Experiments are carried out on the public datasets UR FALL Detection Datasets and the simulation datasets. Experimental results are shown that our fall detection algorithm is more accurate than other related algorithms

especially the accuracy to 98.6% and 98.3% respectively.

关键词

Keywords

references

FOX J A, FOX M A. Falls in older people–an overview for the acute physician [J]. Acute Med, 2011, 10(2): 99-102.

MUBASHIR M, SHAO L, SEED L. A survey on fall detection: principles and approaches [J]. Neurocomputing, 2013, 100: 144-152.

LIU K C, HSIEH C Y, HUANG H Y, et al . An analysis of segmentation appr oaches and window sizes in wearable-based critical fall detection systems with machine learning models [J ] . IEEE Sensors Journal, 2019, 20(6): 3303-3313.

CHEN X, XUE H, KIM M, et al . Detection of falls with smartphone using machine learning technique[C ] //2019 8th International Congress on Advanced Applied Informatics. Toyama: IEEE, 2019: 611-616.

DROGHINI D, FERRETTI D, PRINCIPI E, et al . A combined one-class SVM and template-matching approach for user-aided human fall detection by means of floor acoustic features [J ] . Computational Intelligence and Neuroscience, 2017, 2017(8): 1512670.

WU J, WANG K, CHENG B, et al . Skeleton based fall detection with convolutional neural network[C ] //2019 Chinese Control And Decision Conference (CCDC) . Nanchang: IEEE, 2019: 5266-5271.

YAO L, YANG W, HUANG W. A fall detection method based on a joint motion map using double convolutional neural networks [J]. Multimedia Tools and Applications, 2020, 81(4): 4551-4568.

LI C, ZHONG Q, XIE D, et al . Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation[C ] // Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm: ACM , 2018: 786-792.

SU H, CHANG Z, YU M, et al . Convolutional neural network with adaptive inferential framework for skeleton-based action recognition [J ] . Journal of Visual Communication and Image Representation, 2020, 73: 102925.

LIU J, ROJAS J, LI Y, et al . A graph attention spatio-temporal convolutional network for 3D human pose estimation in video[C ] //2021 IEEE International Conference on Robotics and Automation (ICRA) . Xi'an: IEEE, 2021: 3374-3380.

KE Q, BENNAMOUN M, AN S, et al . A new representation of skeleton sequences for 3D action recognition[C ] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE, 2017: 3288-3297.

KIM T S, REITER A. Interpretable 3D human action analysis with temporal convolutional networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Honolulu: IEEE, 2017: 1623-1631.

YANG Z, LI Y, YANG J, et al . Action recognition with spatio–temporal visual attention on skeleton image sequences [J ] . IEEE Transactions on Circuits and Systems for Video Technology, 2018, 29(8): 2405-2415.

SZEGEDY C, VANHOUCKE V, IOFFE S, et al . Rethinking the inception architecture for computer vision[C ] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2818-2826.

KWOLEK B, KEPSLI M. Human fall detection on embedded platform using depth maps and wireless accelerometer [J]. Neurocomputing, 2015, 168(3): 637-645.

TAN T H, HUS J H, LIU S H, et al . Using direct acyclic graphs to enhance skeleton-based action recognition with a linear-map convolution neural network [J ] . Sensors, 2021, 21(9): 3112-3118.

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