Original paper
Deep Learning for Human Activity Recognition Based on Causality Feature Extraction
Abstract
We propose a novel data-driven feature extraction approach based on direct causality and fuzzy temporal windows (FTWs) to improve the precision of human activity recognition and mitigate the problems of easily-confused activities and unlabeled data, which significantly degrade classification performance owing to the correlation of labeled data. In recognizing activities, the proposed approach not only considers the importance of oncoming...
Paper Details
Title
Deep Learning for Human Activity Recognition Based on Causality Feature Extraction
Published Date
Jan 1, 2021
Journal
Volume
9
Pages
112257 - 112275
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