PETSAI-Ext: Physical Education Teaching Support with Artificial Intelligence

Physical Education Subject (PES) is a challenging problem for conveying IT into education. From the perspective of Artificial Intelligence (AI) and realizing PES’s abstractness in knowledge representation, we pay attention to the idea of an intelligent system with a combination breath of computer vi...

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Bibliographic Details
Published in:SN computer science Vol. 5; no. 7; p. 855
Main Authors: Ma, Thanh, Tran-Nguyen, Minh-Thu
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 02.09.2024
Springer Nature B.V
Springer
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Computer vision
Data Structures and Information Theory
Education
Gesture recognition
Human motion
Information Systems and Communication Service
Intelligent Systems and Applications
Knowledge representation
Original Research
Pattern Recognition and Graphics
Physical education
Pose estimation
Regression models
Software Engineering/Programming and Operating Systems
Speech recognition
Students
Support systems
Teaching
Vision
Vietnam
Physical education subject
Human estimation pose
Convolutional neural network
Machine learning
Speech recognition
Human Estimation Pose
Speech Recognition
Physical Education Subject
Machine Learning
ISSN:2661-8907, 2662-995X, 2661-8907
Online Access:Get full text
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Summary:Physical Education Subject (PES) is a challenging problem for conveying IT into education. From the perspective of Artificial Intelligence (AI) and realizing PES’s abstractness in knowledge representation, we pay attention to the idea of an intelligent system with a combination breath of computer vision and speech recognition. Inspired by that vision, we propose a support system called PETSAI (another name: PETSAI-Ext) for teaching the movements of the PES. Our primary idea is to introduce a “audio-visual” framework to compute the scores of the movements. To this end, we leverage human pose estimation (HPE) to detect a student’s skeleton for each movement and utilize convolutional neural networks (CNN) for motion and gesture classification. In addition, we also use the power of speech recognition to control the entire framework. Two operations, the speech and image processes, will be performed in parallel. Moreover, we also provide an algorithm to calculate scores (from gestures to movements). In addition, the regression models has also been introduced to calculate scores in this paper. Finally, we implement the system on both platforms, including the website and desktop application.
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-024-03192-7