Fall Detection Algorithm Based on TSSI and STB-CNN

Huang Xiao-yong; Li Wei-tong

doi:10.12052/gdutxb.220078

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Fall Detection Algorithm Based on TSSI and STB-CNN

更新时间：2026-05-27

- Fall Detection Algorithm Based on TSSI and STB-CNN
- Vol. 40, Issue 4, Pages: 53-59(2023)
- 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：
- 基金信息：
- DOI：10.12052/gdutxb.220078
  CLC： TP391.4
- Published：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

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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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