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MSAP for Efficient HAR Feature Screening by m18233511508 is a document available to read on EtoBox.

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This document presents a novel attention purification mechanism, MSAP, for human activity recognition (HAR) that effectively reduces feature redundancy while maintaining low resource consumption. The proposed model integrates deep learning techniques with a network correction module to enhance performance in sensor-based HAR tasks, addressing challenges such as noise interference and the balance between performance and efficiency. Experimental results demonstrate that the MSAP-DM model outperforms existing

Author
m18233511508
Language
EN

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