Abstract
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.
Highlights
The elderly population across the globe is increasing at a very fast rate
It provides a novel approach to perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs in order to identify a list of distinct behavioral patterns associated with different complex activities performed in an IoTbased environment
We present the results obtained from the proposed framework by using the dataset [34]
Summary
The elderly population across the globe is increasing at a very fast rate. It has been estimated [1] that by the year 2050, around 20% of the world’s population will be aged 60 years or more. In addition to aiding during order to detect any anomalies in user behavior that could constitute an ADLs, emergency, human behavior allows for the early various formsand of cognitive such asmonitoring a fall or unconsciousness This detection algorithmof was developed implemented impairment, Alzheimer’s, range of other limitations associated withof old bydementia, using a k-NN classifier, and ita achieved an overall performance accuracy. 1. It provides a novel approach to perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs in order to identify a list of distinct behavioral patterns associated with different complex activities performed in an IoTbased environment. It provides a novel approach to perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs in order to identify a list of distinct behavioral patterns associated with different complex activities performed in an IoTbased environment These behavioral patterns include walking, sleeping, sitting, and lying. It provides a novel intelligent decision-making algorithm that can analyze such dis-
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