An Ontology and Pattern Clustering Approach for Activity Recognition in Smart Environments

Abstract

Activity recognition aims at modeling the occupants’ behavior by analyzing the sensor data collected from the smart environment. Though most of the activity recognition systems use supervised learning techniques for building such models there is a shift towards the unsupervised learning paradigm as the process of annotating and labeling the data is prone to errors. This paper proposes an Event Pattern Activity Modeling Framework (EPAM) to identify the occupant activity pattern from the sensor data by using an unsupervised machine learning approach and further analysis is done with a knowledge driven approach. In the context of smart environments, an activity is considered as a sequence of events that are generated continuously from the sensor data. The segmentation algorithm proposed in EPAM is used to identify appropriate event patterns for an activity that are then grouped together using a pattern clustering algorithm that presents a hierarchy of activities. The set of activities of the occupant, observed in a smart environment is not always sequential but is highly interleaved and discontinuous. The proposed algorithm accommodates this valid factor by an innovative use of the Jaro Winkler similarity measure. The hierarchy of activity generated by the pattern clustering approach is used for activity modeling. Ontology based activity modeling is preferred over other modeling techniques because of its unified modeling, representation and semantically clear reasoning. The experimental results show that the proposed EPAM framework of segmentation, pattern clustering and ontological modeling is efficient and more effective than the existing approach of activity modeling.