Deep Learning for Human Activity Recognition Based on Causality Feature Extraction

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...