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Related Experiment Video

Updated: Nov 20, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Published on: December 15, 2023

[Research on first aid measures based on convolutional neural network recognition human actions].

Qing Yu¹, Peijing Jiang², Yaoguo Wang³

¹School of Information Science and Engineering, Xinjiang University, Urumqi 830046, Xinjiang Uygur Autonomous Region, China.

Zhonghua Wei Zhong Bing Ji Jiu Yi Xue

|January 19, 2021

Summary

Convolutional neural networks (CNNs) can recognize human behavior for pre-hospital first aid. This technology achieved high accuracy in identifying normal and abnormal actions, aiding in timely critical state warnings.

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Area of Science:

Computer Vision
Artificial Intelligence
Emergency Medicine

Background:

Pre-hospital first aid requires rapid assessment of patient conditions.
Human behavior recognition technology can assist in identifying critical situations.

Purpose of the Study:

To evaluate the effectiveness of convolutional neural network (CNN) based human behavior recognition for pre-hospital first aid applications.

Main Methods:

Sixty videos were used for training and testing a CNN model.
Image processing techniques including singular value decomposition and Fourier transform were employed.
OpenCV and machine learning libraries were used for pose estimation and feature extraction.
CNN was applied to recognize normal and abnormal human behaviors in video frames.

Main Results:

The CNN model achieved a recognition rate of 90.33% for normal behavior.
The recognition rate for abnormal behavior was 87.74%.

Conclusions:

CNN-based human behavior recognition can accurately identify critical states in individuals.
This technology offers vital support for timely interventions in pre-hospital first aid scenarios.