Human Motion Recognition Information Based on LSTM Recurrent Neural Network Algorithm

This article discusses a method for human motion recognition based on the Long Short-Term Memory (LSTM) recurrent neural network algorithm. Employing the WISDM dataset, it extracts high-quality training samples through meticulous data preprocessing steps such as data cleansing and normalization. Subsequently, a network architecture centered around LSTM is devised to capture temporal features inherent in human motion, followed by rigorous model training and optimization. The experimental section provides a detailed analysis of the impacts of time steps and neuron parameters on model performance, ultimately validating its efficacy and robustness through comparative analysis. Experimental results demonstrate that the LSTM-based model achieves high accuracy and stability in human motion recognition, effectively discerning various types of motion patterns. This study furnishes significant insights and guidance for future research and applications in related fields.