An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments
Nirmalya Thakur, Chia Y. Han

TL;DR
This paper presents a comprehensive ambient intelligence framework that monitors, analyzes, and detects anomalies in human behavior during daily activities using IoT, machine learning, and semantic analysis, aiming to enhance assisted living.
Contribution
It introduces two novel functionalities: semantic analysis of user interactions and an intelligent decision-making algorithm for anomaly detection in ubiquitous environments.
Findings
Behavioral pattern identification accuracy: 76.71%
Anomaly detection accuracy: 83.87%
Potential to improve quality of life for aging populations
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…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Technology Use by Older Adults
MethodsSeventeen Ways to Call Uphold Helpline Full Guide USA 24 Hour Assistance
