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This study presents a multimodal fusion monitoring method for Freezing of Gait (FOG) using data from inertial measurement units (IMUs) and force-sensitive insoles (FSIs) to enhance detection and prediction accuracy. By employing deep learning techniques, the proposed model improves FOG detection performance and introduces an automatic labeling method for pre-FOG events, resulting in increased sample accuracy. Experimental results indicate that the multimodal approach significantly outperforms unimodal model

Author

Raghunathan N

Language

EN