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 short-term sensor data but also considers the continuity from past activities of the preceding long-term sensor data. In terms of the oncoming data, the causality feature is extracted using the direct transfer entropy to determine the unique pattern of an activity, which represents the quantified causal relationship between sensor activations. In terms of the preceding data, several hours of historical data are compressed to fuzzy features based on FTWs. Subsequently, the causality and fuzzy features are fused by matrix multiplication to express distinct features of activities. To effectively learn the spatiotemporal dependencies of the fused feature, deep long short-term memory (LSTM), two-dimensional convolutional neural network (2D-CNN), and hybrid models composed of a combination of LSTM and CNN were used. Leave-one-day-out cross-validation was performed based on the CASAS open datasets, including Aruba, Cairo, and Milan. The results showed that the macro-F1-scores were improved by 16.4, 37.5, and 18.5%, respectively, compared with those of the FTW-only environments. In addition, the proposed approach could improve the precision of activity recognition and mitigate the problems associated with the environments containing unlabeled data.