A Two-Stage Corrective Markov Model for Activities of Daily Living Detection

In this paper we propose a two-stage, supervised statistical model for detecting the activities of daily living (ADL) from sensor data streams. In the first stage each activity is modeled separately by a Markov model where sensors correspond to states. By modeling each sensor as a state we capture the absolute and relational temporal features of the atomic activities. A novel data segmentation approach is proposed for accurate inferencing at the first stage. To boost the accuracy, a second stage consisting of a Hidden Markov Model is added that serves two purposes. Firstly, it acts as a corrective stage, as it learns the probability of each activity being incorrectly inferred by the first stage, so that they can be corrected at the second stage. Secondly, it introduces inter-activity transition information to capture possible time-dependent relationships between two contiguous activities. We applied our method to three ADL datasets to show its suitability to this domain.