Composite Activity Recognition in Smart Homes Using Markov Logic Network

Abstract

Smart environments have progressed and evolved into a significant research area with development of sensor technology, wireless communication and machine learning strategies. Ambient Intelligence incorporated into smart environment assists in resolving many social related applications to facilitate the future society. The initiative of modeling Activity of Daily Living (ADL) and Ambient Assisted Living (AAL) in smart homes have helped in the deployment of applications to various domains like elderly care, health care etc. Activity recognition is the task involved in reasoning within smart homes with the aim of recognizing the ongoing activity of the occupant. Constructing an activity model is essential to carry out recognition and is achieved through various machine learning and artificial intelligence techniques. Data driven approach constructs activity model through statistical machine learning mechanisms while knowledge driven approach constructs activity model through knowledge representation and modeling strategies. Uncertainty and temporal data are better handled by data driven approach while re-usability and context based analysis is handled better by knowledge driven approach of activity modeling. To combine the features of data driven and knowledge driven approaches, a hybrid activity modeling technique is required. The proposed system performs activity modeling via Markov Logic Network, a machine learning strategy that combines probabilistic reasoning and logical reasoning with a single framework. Activities in a smart home are categorized as simple and composite activities, wherein composite activities are defined as related simple activities within a given time interval. The proposed system models both simple and composite activity using soft and hard rules of MLN. Experiments carried over the proposed system shows the effectiveness of the proposed work for recognizing simple and composite activity.