  This framework for human behavior monitoring aims to take a holistic approach
to study, track, monitor, and analyze human behavior during activities of daily
living (ADLs). The framework consists of two novel functionalities. First, it
can perform the semantic analysis of user interactions on the diverse
contextual parameters during ADLs to identify a list of distinct behavioral
patterns associated with different complex activities. Second, it consists of
an intelligent decision-making algorithm that can analyze these behavioral
patterns and their relationships with the dynamic contextual and spatial
features of the environment to detect any anomalies in user behavior that could
constitute an emergency. These functionalities of this interdisciplinary
framework were developed by integrating the latest advancements and
technologies in human-computer interaction, machine learning, Internet of
Things, pattern recognition, and ubiquitous computing. The framework was
evaluated on a dataset of ADLs, and the performance accuracies of these two
functionalities were found to be 76.71% and 83.87%, respectively. The presented
and discussed results uphold the relevance and immense potential of this
framework to contribute towards improving the quality of life and assisted
living of the aging population in the future of Internet of Things (IoT)-based
ubiquitous living environments, e.g., smart homes.
