Analysis and System Construction of ALSTM-LSTM Model-based Sports Jumping Rope Movement

Abstract

Computer technology's maturity has enabled intelligent and interactive sports training. Jumping rope test in secondary school faces difficulties due to bulky testing equipment and inefficient data measurement. An ALSTM-LSTM model based on visual human posture estimation is proposed for motion system analysis. Joint pose features are fused through LSTM, and the attention mechanism assigns weights to feature sequences to achieve motion recognition, considering the data's multidimensional and hierarchical nature. The model's precision value is 95.83. Its average accuracy is much higher than LSTM, ML-KNN, and RSN models. Additionally, the model has 95.2% accuracy in localizing jump rope stance movements with low data consumption. The model can improve the accuracy of the analysis of the jump rope sport's posture based on the characteristics of human movement, and inspire new technical tools for teaching instruction.