A Hybrid Context-aware Framework to Detect Abnormal Human Daily Living Behavior

In Ambient Assisted Living (AAL) systems, one of the main objectives is to provide intelligent services to enhance the quality of people's lives in terms of safety, well-being, and autonomy. One of the challenges in designing these systems is abnormal human behavior detection, which is critically important to prevent users, especially elderlies, from dangerous situations. Abnormality detection has been widely explored in various fields; however, challenges remain in developing effective approaches that take into account the limitations of data-driven and knowledge-driven approaches in detecting abnormal human behaviors in AAL systems. In this paper, a hybrid context-aware framework combining a machine-learning model and probabilistic reasoning is proposed to detect abnormal human behavior. An LSTM model is firstly used to classify input data into a set of labels describing human activities. Different human activity contexts, including the duration, frequency, time of the day, locations, used objects, and sequences of the frequent activities, are then extracted to analyze human behaviors. The obtained human activities and behaviors are mapped to the proposed ontology called Human AcTivity (HAT) ontology, which conceptualizes human behavior contexts. Afterward, the abnormal human behaviors are detected using Markov Logic Network (MLN), which combines logic and probability. The concepts and relationships defined in HAT ontology are exploited in defining the FOL rules used in MLN. The proposed framework is evaluated on the Orange4Home dataset and HAR dataset using smartphones. The obtained results demonstrate the ability of the proposed framework to detect abnormal human daily living behavior with high accuracy.