Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

Author:

Zhao Yuliang12,Sun Tianang12ORCID,Ju Zhongjie12,Dong Fanghecong12,Yang Le12,Lv Xiaoyong12,Lian Chao12ORCID,Chen Meng3,Li Wen Jung3ORCID

Affiliation:

1. School of Information Science and Engineering Northeastern University Shenyang 110819 China

2. Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004 China

3. Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR 999077 China

Abstract

Human activity recognition (HAR) has gained popularity in the field of computer vision such as video surveillance, security, and virtual reality. However, traditional methods are limited in terms of computations and holistic learning of human skeletal sequences. In this article, a new time‐series skeleton joint data imaging method is infused into an improved convolutional neural network to handle these problems. First, the raw time‐series data of 33 body nodes are transformed to red–green–blue images by encoding the 3D positional information to one pixel. Second, the LeNet‐5 network is enhanced by expanding the receptive field, introducing coordinate attention and the smooth maximum unit to improve smoothness and feature extraction. Third, the ability of coded images to express human activities is studied in various environments. It is shown in the experimental results that the method achieves an impressive accuracy of 98.02% in recognizing 25 daily human activities, such as running, writing, and walking. In addition, it is shown that the number of floating point operations, parameters, and inference time of the method are 0.08%, 0.47%, and 3.05%, respectively, of the average values for six other networks (including AlexNet, GoogLeNet, and MobileNet). The proposed method is thus a novel, lightweight, and high‐precision solution for HAR.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

Publisher

Wiley

Subject

General Medicine

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Weighted voting ensemble of hybrid CNN-LSTM Models for vision-based human activity recognition;Multimedia Tools and Applications;2024-06-08

2. Skeleton-based Human Action Recognition in a Thermal Comfort Context;Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation;2023-11-15

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