Development of Interactive Artificial Intelligence Tools for Personalized Somatosensory and Rhythm Evaluation in Intelligent Music Education Platforms

Traditional music education often lacks interactivity and real-time adaptability, especially in remote settings. This study introduces a personalized somatosensory framework, TRPO-ResLSTM, for music education platforms. The system captures movement, rhythm, and response time, preprocesses data with Wiener filtering and Z-score normalization, and extracts features via FFT. Gesture recognition is performed by DeepRes-LSTM, while adaptive difficulty is regulated by TRPO reinforcement learning. Incremental learning ensures personalization across sessions. Experiments on a publicly available, anonymized gesture-rhythm dataset (n = 2,730 samples; training/validation/test split 70/15/15) show superior performance over multimodal baselines, achieving 95% accuracy, 93.5% precision, 94.6% recall, and 94.2% F1-score. Ablation studies confirm the individual contributions of TRPO and Res-LSTM. The innovation of this protocol lies in integrating reinforcement learning with residual temporal modeling for adaptive gesture recognition, enabling stable yet personalized learning. This work demonstrates that adaptive, gesture-responsive tools can enhance engagement, personalization, and progressive skill development in intelligent music education. Limitations include reliance on a single dataset and the need for real-learner validation, which define directions for future work.