Abstract
In the field of sports, the formulation of existing training plans mainly relies on the manual observation and personal experience of coaches. This method is inevitably subjective. The application of artificial intelligence technology to the training of athletes to recognize athletes’ posture can help coaches assist in decision-making and greatly enhance athletes’ competitive ability. The human body movements embodied in sports are more complicated, and the accurate recognition of sports postures plays an active and important role in sports competitions and training. In this paper, inertial sensor technology is applied to attitude recognition in motion. First, in order to improve the accuracy of attitude calculation and reduce the noise interference in the preparation process, this article uses differential evolution algorithm to apply attitude calculation to realize multisensor data fusion. Secondly, a two-level neural network intelligent motion gesture recognition algorithm is proposed. The two-level neural network intelligent recognition algorithm effectively recognizes similar actions by splitting the traditional single-level neural network into two-level neural networks. Experiments show that the experimental method designed in this article for the posture in motion can obtain the motion information of the examinee in real time, realize the accurate extraction of individual motion data, and complete the recognition of the motion posture. The average accuracy rate can reach 98.85%. There is a certain practical value in gesture recognition.
Highlights
With the rapid development of science and technology, the use of scientific and technological means to improve the quality of sports training has gradually attracted people’s attention
There are still many shortcomings in the recognition of human motion posture, and there is a lack of relevant research in the recognition of motion posture. In response to this situation, this paper proposes an inertial sensor-based motion posture recognition algorithm
In order to achieve a more accurate attitude calculation and reduce the noise interference of the sensor, in the process of calculating the node attitude, the three data of angular velocity, acceleration, and magnetic field strength are fused, the space attitude is expressed by the quaternion method, and the differential evolution algorithm is selected to improve the accuracy of the attitude calculation results
Summary
With the rapid development of science and technology, the use of scientific and technological means to improve the quality of sports training has gradually attracted people’s attention. Sokolova and Konushin [9] used multiple cameras to perform multiangle detection of human action poses and used neural network algorithms to train and classify image and video data. Sensor equipment has become the best effect due to its small size, high precision, flexibility and easy wear, low environmental requirements, high sensitivity, low energy consumption, and good real-time performance. It is widely used in various fields, such as competitive sports [12], rehabilitation therapy [13], somatosensory games [14], and other aspects. The experimental results show that the algorithm in this paper has achieved better recognition performance
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