A Fall Detection Algorithm Based on Convolutional Neural Network and Multi-Discriminant Feature

被引：0

作者：

Wang X. ^{[1
]}

Zheng X. ^{[1
]}

Gao H. ^{[2
,3
]}

Zeng Z. ^{[4
]}

Zhang Y. ^{[5
,6
,7
]}

机构：

[1] College of Information and Control Engineering, Shenyang Jianzhu University, Shenyang

[2] School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan

[3] Shandong Key Laboratory of Intelligent Buildings Technology, Jinan

[4] School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen

[5] Key Laboratory of Networked Control System, Chinese Academy of Sciences, Shenyang

[6] State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang

[7] Institute of Robotics and Intelligent Manufacturing Innovation, Chinese Academy of Sciences, Shenyang

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2023年 / 35卷 / 03期

关键词：

cloud server; convolutional neural network; fall detection; internet of things; multi-discriminant features;

D O I：

10.3724/SP.J.1089.2023.19361

中图分类号：

学科分类号：

摘要：

In order to solve the problems that insufficient feature extraction, poor generalization of fall discrimination conditions, and poor real-time performance in traditional algorithms, a fall detection algorithm based on convolutional neural network and multi-discriminant features is proposed. In order to complete the extraction of richer feature information and ensure real-time performance, firstly, the MobileNetV3 lightweight network is used to complete the accurate and fast extraction of the character feature information in the in-put image. Secondly, the superposition of three small convolution kernels and the residual network are used to reduce the number of parameters of the network model in the case of the same receptive field, so as to guarantee the real-time detection of human key points in the image. In order to improve the accuracy of fall state discrimination, the angle between human torso, limbs and the ground, and the change of the height-to-width ratio of the human calibration frame, are used as fall discrimination features. Finally, an internet of things system based on cloud server is designed to alleviate the problem of poor real-time performance caused by insufficient computing power of user terminals. A large number of experiments on the URFD dataset and self-built dataset show that the accuracy of the proposed algorithm is 99.0% and 98.5%, respectively, and the proposed algorithm has higher accuracy and better universality than the traditional fall detection algorithms. © 2023 Institute of Computing Technology. All rights reserved.

引用

页码：452 / 462

页数：10

共 36 条

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