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

Summary

This study presents a reproducible somatosensory music-learning protocol combining residual LSTM recognition with TRPO for adaptive difficulty. It covers preprocessing, FFT features, training, personalization, and evaluation. On a public dataset, the hybrid model reached Acc 95.0 / P 93.5 / R 94.6 / F1 94.2 over three subject-disjoint folds.

Abstract

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.

Introduction

Recent advances in artificial intelligence (AI) and somatosensory technology are reshaping music education by enabling learners to interact with music through body movements, where gestures are translated into notes, rhythms, or controls for virtual instruments^1,2. These interactive features enhance engagement, retention, and creativity compared to traditional classroom instruction, and somatosensory tools allow students to practice rhythm, coordination, and expression through body percussion, conducting gestures, and ensemble simulations³. Combined with AI-driven adaptive pathways, lea....

Protocol

This study analyzed anonymized, publicly available data and did not involve human subjects or animals. Therefore, additional ethical approval was not required.

1. Overview

This protocol describes a reproducible framework for somatosensory music education based on deep residual LSTM recognition and Trust Region Policy Optimization (TRPO) for adaptive difficulty control. It includes dataset preparation, preprocessing, frequency-domain feature extraction, model architecture, training, personalization, and evaluation. Figure 1 illustrates the end-to-end workflow

Results

Experimental Setup
The TRPO-ResLSTM framework was implemented in Python 3.10.1 with GPU acceleration. The computing environment, motion-sensing hardware, and Python libraries are listed in the Table of Materials. Data were split into subject-disjoint training/validation/test partitions as shown in Table 1 (70/15/15). Key hyperparameters are summarized in Table 2. Three models were evaluated: baseline TRPO, baseline ResLSTM, and the integrated TRPO-ResLSTM. This set.......

Discussion

This study proposes a hybrid protocol, TRPO-ResLSTM, that integrates reinforcement learning and residual temporal modeling for gesture-based music education. By combining the stability of Trust Region Policy Optimization (TRPO) with the sequence-learning capacity of residual LSTMs, the framework delivers real-time gesture recognition together with adaptive difficulty control, enabling personalized feedback and progressive skill acquisition²⁵. To ensure reproducibility, subject-disjoint folds, fixe.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Accelerometer sensor data	Kaggle (Public domain)		Multimodal input signals (motion patterns, timing features) included in dataset
GPU workstation	NVIDIA Corporation, USA		Training hardware: NVIDIA RTX 3080 (10 GB), 32 GB RAM, Ubuntu 20.04
Hand - joint position data	Kaggle (Public domain)		Somatosensory input for gesture recognition
Matplotlib (v3.7)	https://matplotlib.org		Visualization library for plotting figures and performance metrics
NumPy (v1.23)	https://numpy.org		Numerical computation library for array operations
Public music gesture and rhythm dataset	Kaggle (Public domain)		Anonymized dataset of 2,730 samples recording body responses to tempo and beat; used for training/validation/testing (70/15/15)
Python 3.10.1	Python Software Foundation, https://www.python.org		Programming environment for model implementation and analysis
PyTorch (v1.13)	https://pytorch.org		Deep learning framework for implementing ResLSTM and TRPO modules
scikit - learn (v1.2)	https://scikit-learn.org		Machine learning utilities for preprocessing and evaluation
SciPy (v1.10)	https://scipy.org		Scientific computing library (used for Wiener filtering)