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This framework for human behavior monitoring aims to take a holistic approachto study, track, monitor, and analyze human behavior during activities of dailyliving (ADLs). The framework consists of two novel functionalities. First, itcan perform the semantic analysis of user interactions on the diversecontextual parameters during ADLs to identify a list of distinct behavioralpatterns associated with different complex activities. Second, it consists ofan intelligent decision-making algorithm that can analyze these behavioralpatterns and their relationships with the dynamic contextual and spatialfeatures of the environment to detect any anomalies in user behavior that couldconstitute an emergency. These functionalities of this interdisciplinaryframework were developed by integrating the latest advancements andtechnologies in human-computer interaction, machine learning, Internet ofThings, pattern recognition, and ubiquitous computing. The framework wasevaluated on a dataset of ADLs, and the performance accuracies of these twofunctionalities were found to be 76.71% and 83.87%, respectively. The presentedand discussed results uphold the relevance and immense potential of thisframework to contribute towards improving the quality of life and assistedliving of the aging population in the future of Internet of Things (IoT)-basedubiquitous living environments, e.g., smart homes.

An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments