Optimization Modeling and Simulation of Motion Monitoring based on Multi layer Perceptual Neural Network

In order to solve the problems of signal noise interference, low accuracy of motion pattern recognition, and high real-time requirements in motion monitoring, an optimization method based on MLP (multi-layer perceptron) is used to improve the accuracy and real-time performance of the motion monitoring system. First, the signal preprocessing algorithms such as wavelet transform denoising and filtering are used to ensure the quality of input data. Then, combined with the MLP model, data parallel processing strategy and model pruning technology are adopted to ensure that the motion monitoring system can run efficiently under large-scale data input. In all 20 tests, the response time and processing delay of the lightweight model have a small fluctuation range, with a response time of 97~100 milliseconds and a processing delay of 104~107 milliseconds, providing a new technical path for the field of motion monitoring.