Biomechanical Analysis of Volleyball Players' Spike Swing Based on Deep Learning.

Deep learning is to learn the inherent laws and representation levels of sample data. The information obtained during these learning processes is of great help in the interpretation of data such as text, images, and sounds. Through the deep learning method, the image features are learned independently, and feature extraction is realized, which greatly simplifies the feature extraction process. It uses deep learning technology to capture the motion of volleyball players and realizes the recognition and classification of motion types in the data. It finds the characteristics and deficiencies of the current volleyball players' spiking skills by comparing the test data of 8 volleyball players' spiking skills and biological analysis. The results show that the front and rear spiking balls with double-arm preswing technology have very obvious technical differences. In the take-off stage, there was no significant difference in the buffering time, the kick-off time, and the take-off time in the front and rear row spikes of the A-type. The buffer time of the B-type spike is 0.26 s in the front row and 0.44 s in the rear row. The range of motion of the front row spike is greater than the range of motion of the back row spike. In the air hitting stage, the range of action of the back row spiking is larger than that of the front row spiking, but the range of action of the back row is greater than that of the front row spiking.

Biomechanics of Volleyball Players' Run-Up and Take-Off Link under Deep Learning.

In volleyball, the correct approach and start (including the number of steps and stride speed) are a prerequisite for all technical movements to attack. It can not only improve the horizontal speed of the athlete, but also properly convert the total speed into vertical speed, so that the hitting point is improved and the ball speed is accelerated. To explore the biomechanical characteristics of lower limb movements in the run-up and take-off stage of volleyball spiking, this paper takes four male volleyball players from the Physical Education College of X University as the research objects to analyze the kinematics and dynamics of the run-up process and the take-off process. This paper uses the precise recognition method under the background of deep learning to accurately capture the movements of the research object. This paper discusses the effects of time, speed, distance, knee, and hip parameters (angle, joint muscle torque, and power) on the effect of spiking techniques. It is expected to provide reference for the diagnosis, guidance, and muscle strength training of this special technical movement. The research results show that the horizontal speed of No. 2 athlete is 3.62 m/s and the vertical speed is 2.71 m/s when he takes off. The landing time is 0.375 s and the lift-off time is 0.16 s. The torque and power of the knee joint changed greatly during the take-off process, and the change of the hip joint was small.

Analysis of a passive radio frequency excited acoustic transducer.

In this work, the physical processes that govern the operation of a passive acoustic transducer are analyzed and modeled. The wireless battery-free transducer derives its power from an externally applied electromagnetic field generated by a radio transmitter. The audio signal is encoded in the backscattered electromagnetic field. Electro-mechano-acoustical analogies are developed and presented. Power generation, sound transduction, and radio frequency backscatter transmission of the audio signal are analyzed. The resonant frequency of the passive acoustic transducer derived from this analysis is comparable to those reported in the literature.