IoT and AI-Integrated Smart Home Architecture for Adaptive Assisted Living Support
Abstract
The aging population has been experiencing rapid growth, along with the increasing prevalence of chronic health conditions. This has created an increasing need for innovative assisted living solutions. Traditional smart home systems typically employ fixed, rule-based automation that cannot adapt to the dynamic physiological and behavioral changes exhibited by users over time. This paper focuses on developing a comprehensive, multilayer architecture that combines the Internet of Things (IoT) with Artificial Intelligence (AI) in order to create an adaptive assisted living environment. The proposed system will utilize a hybrid edge-cloud computing paradigm to ensure real-time anomaly detection, in addition to facilitating long-term behavior trend analysis. A wide variety of non-intrusive ambient and wearable sensors will be used to continuously monitor Activities of Daily Living (ADLs) and vital signs. To recognize patterns and predict potential health risks, such as falls or cognitive decline, deep learning algorithms will be used to combine Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Simulated evaluations of this architecture have been conducted, and the results demonstrate that processing latency is significantly decreased, contextual activity recognition accuracy is significantly increased, and data privacy protocols are strictly adhered to. Overall, the proposed adaptive system provides vulnerable individuals the opportunity to continue living independently, while providing caregivers with predictive, actionable information that may fundamentally change the way we think about smart assisted living.