ArticleHeliyon2024
Practical application of interactive AI technology based on visual analysis in professional system of physical education in universities.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07517016 (The Impact of Real-Time Pose Recognition Technology on Tennis Learning in Individuals With Intellectual Disabilities), which is not on this map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Impact of Real-Time Pose Recognition Technology on Tennis Learning in Individuals With Intellectual Disabilities: A Comparative Study With Traditional Methods
Who cites it
5 citing papers in PubMed.
- An empirical study of digital competence, technostress, and job satisfaction among college physical education teachers in China.Scientific reports · 2026Article
- Relationship between school support and digital teaching adaptation among physical education teachers: the mediating roles of self-efficacy, digital teaching beliefs and teaching intention.Frontiers in psychology · 2026Article
- Reliability and Validity Examination of a New Gait Motion Analysis System.Sensors (Basel, Switzerland) · 2025Article
- Validity Verification of Human Pose-Tracking Algorithms for Gait Analysis Capability.Sensors (Basel, Switzerland) · 2024Article
- Artificial intelligence in physical education: comprehensive review and future teacher training strategies.Frontiers in public health · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, physical education teaching in universities tends to adopt the traditional model of one teacher for multiple students, which has high requirements for teachers and is difficult to consider students' strengths. On this basis, an interactive system has been established, including three modules: behavior information, user data collection, and behavior evaluation. Taking the 400 m running physical education teaching as an example, Kinectv2 was used to collect students' movements and contours while running, and ORB (Oriented FAST and Rotated BRIEF) feature extraction algorithm was used to extract students' movement features. After importing the data into the interactive system, students and teachers could view it in the system and provide guidance based on the students' actions. This article took 10 students as examples to test their performance changes in the 400 m running before and after systematic training. The results showed that the evaluation score after receiving systematic instruction increased by 6-7 s compared to the score without receiving instruction, with a significant change. This indicated that the interactive AI (artificial intelligence) system constructed in this article can play a significant role in sports teaching of 400 m running.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.