Causality-Aware Pattern Mining Scheme for Group Activity Recognition

Human activity recognition is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. Specifically, for human activity recognition in smart spaces without privacy and accessibility issues, data streams generated by deployed ambient sensors are leveraged. In this paper, we focus on group activities by which a group of users perform a collaborative task without user identification and propose an efficient group activity recognition scheme that extracts causality patterns from ambient sensor event sequences, to support as good recognition accuracy as the state-of-the-art models with missing or false data tolerance. To filter out irrelevant noise events from a given data stream, a set of rules is leveraged to highlight causally related events. Then, a pattern-tree algorithm extracts frequent causal patterns by means of a growing tree structure. Based on the extracted patterns, a weighted sum-based pattern-matching algorithm computes the likelihood of stored group activities to the given test event sequence using matched event pattern counts for group activity recognition. We evaluate the proposed scheme using the data collected from real-world testbed and open datasets where users perform their tasks on a daily basis. Experiment results show that the proposed scheme performs higher recognition accuracy and is tolerant to missing or false data with a smaller amount of runtime overhead than the existing schemes.