Analysis of AI image recognition approach for monitoring primary school students' outdoor activity time in school

WU Yu; SHI Huiying; WANG Qian; HUANG Yuanyuan; CHENG Jinquan; ZHOU Li

doi:10.16835/j.cnki.1000-9817.2023.08.013

Volume 44 Issue 8

Aug. 2023

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WU Yu, SHI Huiying, WANG Qian, HUANG Yuanyuan, CHENG Jinquan, ZHOU Li. Analysis of AI image recognition approach for monitoring primary school students' outdoor activity time in school[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2023, 44(8): 1178-1181. doi: 10.16835/j.cnki.1000-9817.2023.08.013

Citation:

WU Yu, SHI Huiying, WANG Qian, HUANG Yuanyuan, CHENG Jinquan, ZHOU Li. Analysis of AI image recognition approach for monitoring primary school students' outdoor activity time in school[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2023, 44(8): 1178-1181. doi: 10.16835/j.cnki.1000-9817.2023.08.013

Citation:

WU Yu, SHI Huiying, WANG Qian, HUANG Yuanyuan, CHENG Jinquan, ZHOU Li. Analysis of AI image recognition approach for monitoring primary school students' outdoor activity time in school[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2023, 44(8): 1178-1181. doi: 10.16835/j.cnki.1000-9817.2023.08.013

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Analysis of AI image recognition approach for monitoring primary school students' outdoor activity time in school

doi: 10.16835/j.cnki.1000-9817.2023.08.013

1.
Department of School Health, Shenzhen Center for Disease Control and Prevention, Shenzhen (518000), Guangdong Province, China

Received Date: 2023-03-15
Rev Recd Date: 2023-05-18

Available Online: 2023-08-26

Publish Date: 2023-08-25

Abstract

Abstract

Objective To develop a method that can continuously monitor duration of students' outdoor activities for a long time, so as to provide data support for the relationship between outdoor activity duration and students' health. Methods From April 28 to July 6, 2022, 1 168 students from a primary school in Shenzhen were selected. Fixed cameras were placed on the top of school classrooms, corridors and critical paths were used to identify student data and duration in the picture. And AI, cloud computing and other methods were used to measure students' non-classroom time instead of outdoor activity time in school. Results The average length of time spend on outdoor activities in school of the 24 pilot classes were 67.6-113.0 min, and showed a downward trend by grade (F=42.74, P < 0.05). The duration of outdoor activities among students was negatively correlated with the detection rate of myopia and overweight(r=-0.74, -0.45, P < 0.05). Conclusion The data on outdoor activity time calculated by AI image recognition is basically in line with the reality. Monitoring students' outdoor activity time at school through AI image recognition is feasible and popularized.
- Public health surveillance,
- Artificial intelligence,
- Motor activity,
- Students

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References(20)

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