Abstract
With the development of electronic technology and sensor technology, more and more intelligent electronic devices integrate micro inertial sensors, which makes the research of human action recognition based on action sensing data have great application value. Data‐based action recognition is a new research direction in the field of pattern recognition, which is essentially a process of action data acquisition, feature extraction, feature extraction, and recognition, the process of classification and recognition. Inertial motion information includes acceleration and angular velocity information, which is ubiquitous in daily life. Compared with motion recognition based on visual information, it can more directly reflect the meaning of action. This study mainly discusses the method of analyzing and managing volleyball action by using the action sensor of mobile device. Based on the motion recognition algorithm of support vector machine, the motion recognition process of support vector machine is constructed. When the data terminal and gateway of volleyball players are not in the same LAN, the classification algorithm classifies the samples to be tested through the characteristic data, which directly affects the recognition results. In this paper, the support vector machine algorithm is selected as the data classification algorithm, and the calculation of the classification process is reduced by designing an appropriate kernel function. For multiclass problems, the hierarchical structure of directed acyclic graph is optimized to improve the recognition rate. We need to bind motion sensors to human joints. In order to realize real‐time recognition of human motion, mobile devices need to add windows to the motion capture data, that is, divide the data into a small sequence of specified length, and provide more application scenarios for the device. This method of embedding motion sensors into devices to read motion information is widely used, which provides a convenient data acquisition method for human motion pattern recognition based on motion information. The multiclassification support vector machine algorithm is used to train the classification algorithm model with action data. When the signal strength of the sensor is 90 t and the speed is 2.0 m/s and 0.5 m/s, the detection accuracy of the adaptive threshold is 93% and 95%, respectively. The results show that the SVM method based on hybrid kernel function can greatly improve the recognition accuracy of volleyball stroke, and the recognition time is short.
Highlights
In recent years, more and more researchers have begun to analyze the movements of the human body, and the field of research is expanding
The motion recognition method based on optical motion capture has obvious advantages
In the research process of volleyball motion recognition, this research will focus on the Support vector machine (SVM) algorithm based on the hybrid kernel function and compare it with the experimental template matching method
Summary
More and more researchers have begun to analyze the movements of the human body, and the field of research is expanding. Yurtman and Barshan proposed a novel noniterative direction estimation method based on the physical and geometric characteristics of acceleration, angular velocity, and magnetic field vector to estimate the direction of the motion sensor unit They obtain the orientation of the sensor unit according to the rotation quaternion transformation between the sensor unit frames. Behera et al proposed a method to analyze 3D signatures captured using leap motion sensors They extended the original 2D function from the original signature to 3D and applied a well-known classifier for identification and verification. This research mainly uses the support vector machine method based on the hybrid kernel function to determine the movement recognition of the human body in the volleyball movement database. In the research process of volleyball motion recognition, this research will focus on the SVM algorithm based on the hybrid kernel function and compare it with the experimental template matching method. The SVM algorithm based on the hybrid kernel function proves that the recognition of volleyball stroke is effective and fast
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