Characteristics of different swimming styles of swimming events based on artificial neural network data acquisition system.

In recent years, remote data collection and remote monitoring technologies based on artificial neural networks have been increasingly used in various industries. In order to in-depth study whether the data collection system based on artificial neural network theory can analyze the characteristics of different strokes in swimming events, this paper uses simulation comparison method, data integration method, and step-by-step construction method to collect samples, analyze the data collection system, and streamline the algorithm. And integrate and create a data collection system that can analyze the characteristics of swimming styles. After constructing the system, select the image frequency 450 ms once, and set the signal frequency to 2.5 KHZ. Set the waveforms to sawtooth wave and sine wave, respectively; the voltage range of sine wave is − 6 to 8 V, and the sampling frequency is 250. The voltage range of the sawtooth wave is − 8 to 6 V, and the sampling frequency is 45 KHZ. Experiments show that the system basically works normally during the sampling process. To further study the stability of the system, this test is in a swimming pool with a one-story building with a relative humidity of 85%. It is set to send 110 data frames from the coordinator segment to the normal segment every 2.5 s. The acquisition success rate is 88% when there is interference and 96% when there is no interference, which is much higher than that when there is interference. Therefore, a retransmission mechanism must be used when designing the software for common segment points to ensure reliable data transmission. In general, the data acquisition system we designed basically meets the design standards. It is basically realized that starting from the artificial neural network, a data acquisition system that can analyze swimming styles is designed. [ABSTRACT FROM AUTHOR]