Application of LSTM Algorithm in Motion Trajectory Monitoring System

With the advancement of technology and people's emphasis on health, various exercise monitoring tools are becoming increasingly popular. Therefore, this article discusses the application of LSTM algorithm in motion trajectory monitoring systems. However, traditional monitoring systems are often limited to specific scenarios and encounter difficulties in handling complex or irregular motion trajectories. Neural networks simulate the working mode of human brain neurons and can automatically learn and analyze a large amount of motion data, thereby improving the accuracy of monitoring. This article first reviews the relevant research on motion trajectory monitoring and neural network algorithms, and then provides a detailed introduction to the principle and model training process of the LSTM algorithm. The improvement effect of neural network algorithms on system performance has been verified through experiments. Research has shown that motion trajectory monitoring systems combining neural network algorithms perform better than traditional systems in identifying user motion trajectories and monitoring anomalies.