Dance Performance Action Recognition Technology Based on Micro-Electro-Mechanical System Sensors
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The widespread use of intelligent algorithms in motion recognition has driven the continuous evolution of technology for recognizing dance performances. The goal of this study is to improve the accuracy, robustness, and temporal modeling capabilities of dance motion recognition using micro-electro-mechanical systems sensors. This objective is achieved by developing a hybrid analysis framework that integrated a calibrated and denoised MEMS acquisition process, a TimeGAN-based sequence augmentation module, and a 1DCNN-ResBi-LSTM-Attention recognition network. The proposed method first uses window-based segmentation to extract multichannel motion representations. Then, the TimeGAN model generates high-fidelity synthetic samples to address data imbalance and improve intra-class variability. Subsequently, spatial-temporal features are learned through deep convolutional encoding and bidirectional residual recurrent layers. An attention mechanism then highlights key, discriminative motion frames. The experimental results demonstrated that the enhanced dataset improved recognition accuracy by 6.8%. The full model achieved peak accuracy of 98.7%, reduced RMSE, and an improved response time compared to the CNN-LSTM and CNN-ResBi-LSTM baselines. The main novelty lies in the combination of MEMS data augmentation and hierarchical, spatial-temporal modeling. This combination simultaneously addresses the issues of insufficient data diversity, weak long-sequence dependency representation, and suboptimal focus on salient motion details. The result is a more reliable solution for real-time dance movement analysis.
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