Abstract
Human motion recognition based on inertial sensor is a new research direction in the field of pattern recognition. It carries out preprocessing, feature selection, and feature selection by placing inertial sensors on the surface of the human body. Finally, it mainly classifies and recognizes the extracted features of human action. There are many kinds of swing movements in table tennis. Accurately identifying these movement modes is of great significance for swing movement analysis. With the development of artificial intelligence technology, human movement recognition has made many breakthroughs in recent years, from machine learning to deep learning, from wearable sensors to visual sensors. However, there is not much work on movement recognition for table tennis, and the methods are still mainly integrated into the traditional field of machine learning. Therefore, this paper uses an acceleration sensor as a motion recording device for a table tennis disc and explores the three‐axis acceleration data of four common swing motions. Traditional machine learning algorithms (decision tree, random forest tree, and support vector) are used to classify the swing motion, and a classification algorithm based on the idea of integration is designed. Experimental results show that the ensemble learning algorithm developed in this paper is better than the traditional machine learning algorithm, and the average recognition accuracy is 91%.
Highlights
Human action recognition technology has received attention and importance from research scholars in many fields such as machine learning, distributed computing, situational awareness, security monitoring, and smart home because of its unique research value
The integrated learning algorithm can effectively distinguish four types of swings, and the experimental results show that the integrated learning algorithm designed in this paper outperforms the traditional machine learning algorithm and achieves an average recognition accuracy of 91%
The field of human motion recognition has benefited from the rise and development of sensor technology, and while visionbased approaches are dominant, there is still a lot of room for sensor-based approaches
Summary
Human action recognition technology has received attention and importance from research scholars in many fields such as machine learning, distributed computing, situational awareness, security monitoring, and smart home because of its unique research value. In the field of intelligent elderly care, researchers try to analyze the behavior of the elderly through body sensors and surveillance cameras for the purpose of safety and in the field of rehabilitation robotics, the study of symbiotic robots helps to make patients and assistive robots work together harmoniously [3]. The integrated learning algorithm can effectively distinguish four types of swings, and the experimental results show that the integrated learning algorithm designed in this paper outperforms the traditional machine learning algorithm and achieves an average recognition accuracy of 91%
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