Design of a Real-Time Interactive System for Dance Motion Feature Extraction Based on Flexible Textile Sensor Networks

Traditional dance motion capture systems suffer from limited wearability, insufficient motion feature extraction accuracy, and delayed real-time interaction, posing challenges for intelligent wearable sensing and wireless information acquisition. To address these issues, this study proposes a real-time interactive framework for dance motion feature extraction based on a flexible textile sensor network, with potential relevance to low-power wearable sensing and electromagnetic-assisted wireless monitoring technologies. A wearable sensing architecture integrating flexible pressure sensors and inertial measurement units (IMUs) is constructed and deployed at key human joints to achieve unconstrained acquisition of multidimensional motion information. A multi-source data fusion preprocessing strategy combining Kalman filtering and sliding-window temporal alignment is introduced to improve signal quality and synchronization. Subsequently, a Bi-GRU-Attention model is developed to extract dynamic features including joint trajectories, angular velocity, and acceleration, while a motion feature library covering classical, modern, and street dance is established. Furthermore, an integrated real-time interaction system incorporating data acquisition, feature extraction, wireless transmission, and interactive feedback is implemented, where extracted motion features are compared with predefined standard actions to generate visual guidance and haptic feedback. The proposed framework provides an effective solution for intelligent wearable motion analysis and offers valuable references for flexible sensing, wireless signal processing, and real-time interactive systems in advanced engineering applications.