LINKING TEMPORAL FIRST ORDER LOGIC AND HIDDEN MARKOV MODELS WITH ABSTRACT EVENTS

Abstract

In previous work, we introduced a novel concept of a generalised event, an abstract event, which we define as a change of state of abstract predicates that represent knowledge about the surrounding world. Abstract predicates are defined by formulae in temporal first-order logic (Abstract Event Specification Language (AESL)) whose leaf predicates represent low-level sensor-derived knowledge. Abstract events are detected by Rete Networks structured as a deductive knowledge-base. Current Abstract Event detectors cannot express sufficiently well certain high-level situations, such activity derived from user trajectories. In this work we introduce a novel type of abstract event detector, a hidden Markov Model detector (hMM-detector). hMM-detectors are implemented as pattern recognition engines that use several stochastic models, hidden Markov Models (hMMs), in order to classify observed activities to the most likely activity class. We link hMM-detectors with AESL by specifying a new AESL operator for defining hMM-based Abstract Events, thus increasing AESL's expressive power. We describe the experimental evaluation of the above work that was carried out at the University of Cambridge. hMM-Detectors were trained and tested with real data from the Active BAT location system. We evaluate the expressiveness of the enhanced AESL by discussing three case studies in healthcare that relate to continuous monitoring of elderly or injured patients. We demonstrate that AESL can be used in order to improve the dependability of continuous monitoring of patients and the provision of high-quality healthcare.