The accelerating demographic shift toward an aging global population presents urgent challenges for healthcare systems, social infrastructures, and families. Cognitive decline, physical limitations, and the growing gap between the desire to age in place and the feasibility of institutional care demand new approaches beyond traditional elderly support models. This thesis explores the integration of Smart Homes, Mixed Reality (MR), Artificial Intelligence (AI), and Conversational Agents into a unified framework for promoting cognitive resilience, safety, and independence among older adults. Smart home technologies, powered by IoT sensors and adaptive automation, create responsive living environments that monitor daily activities, detect health risks, and optimize comfort. MR systems extend this ecosystem with immersive, engaging cognitive training that leverages neuroplasticity to preserve memory, attention, and executive functions. AI-driven personalization combining machine learning, reinforcement learning, and ontology-based reasoning transforms passive monitoring into predictive, adaptive, and proactive care. Finally, conversational AI agents provide human-centric interfaces, ensuring accessibility, emotional support, and collaborative personalization through natural interaction. By bridging physical and digital worlds, this research advances a holistic technological framework for Ambient Assisted Living (AAL) that empowers older adults to age in place safely, independently, and with dignity. The findings demonstrate that the synergy of AI, MR, and smart home systems not only addresses critical cognitive and physical challenges of aging but also redefines elderly care as an empathetic, adaptive, and participatory ecosystem. This work contributes both conceptual models and practical design strategies for future elderly care technologies, offering a pathway toward sustainable, scalable, and human-centered solutions for the 21st century.
The accelerating demographic shift toward an aging global population presents urgent challenges for healthcare systems, social infrastructures, and families. Cognitive decline, physical limitations, and the growing gap between the desire to age in place and the feasibility of institutional care demand new approaches beyond traditional elderly support models. This thesis explores the integration of Smart Homes, Mixed Reality (MR), Artificial Intelligence (AI), and Conversational Agents into a unified framework for promoting cognitive resilience, safety, and independence among older adults. Smart home technologies, powered by IoT sensors and adaptive automation, create responsive living environments that monitor daily activities, detect health risks, and optimize comfort. MR systems extend this ecosystem with immersive, engaging cognitive training that leverages neuroplasticity to preserve memory, attention, and executive functions. AI-driven personalization combining machine learning, reinforcement learning, and ontology-based reasoning transforms passive monitoring into predictive, adaptive, and proactive care. Finally, conversational AI agents provide human-centric interfaces, ensuring accessibility, emotional support, and collaborative personalization through natural interaction. By bridging physical and digital worlds, this research advances a holistic technological framework for Ambient Assisted Living (AAL) that empowers older adults to age in place safely, independently, and with dignity. The findings demonstrate that the synergy of AI, MR, and smart home systems not only addresses critical cognitive and physical challenges of aging but also redefines elderly care as an empathetic, adaptive, and participatory ecosystem. This work contributes both conceptual models and practical design strategies for future elderly care technologies, offering a pathway toward sustainable, scalable, and human-centered solutions for the 21st century.
Mahroo, A (2026). Towards Intelligent Environments for Aging in Place: A Holistic AI-Powered Mixed Reality Smart Home Framework for Elderly Health. (Tesi di dottorato, , 2026).
Towards Intelligent Environments for Aging in Place: A Holistic AI-Powered Mixed Reality Smart Home Framework for Elderly Health
MAHROO, ATIEH
2026
Abstract
The accelerating demographic shift toward an aging global population presents urgent challenges for healthcare systems, social infrastructures, and families. Cognitive decline, physical limitations, and the growing gap between the desire to age in place and the feasibility of institutional care demand new approaches beyond traditional elderly support models. This thesis explores the integration of Smart Homes, Mixed Reality (MR), Artificial Intelligence (AI), and Conversational Agents into a unified framework for promoting cognitive resilience, safety, and independence among older adults. Smart home technologies, powered by IoT sensors and adaptive automation, create responsive living environments that monitor daily activities, detect health risks, and optimize comfort. MR systems extend this ecosystem with immersive, engaging cognitive training that leverages neuroplasticity to preserve memory, attention, and executive functions. AI-driven personalization combining machine learning, reinforcement learning, and ontology-based reasoning transforms passive monitoring into predictive, adaptive, and proactive care. Finally, conversational AI agents provide human-centric interfaces, ensuring accessibility, emotional support, and collaborative personalization through natural interaction. By bridging physical and digital worlds, this research advances a holistic technological framework for Ambient Assisted Living (AAL) that empowers older adults to age in place safely, independently, and with dignity. The findings demonstrate that the synergy of AI, MR, and smart home systems not only addresses critical cognitive and physical challenges of aging but also redefines elderly care as an empathetic, adaptive, and participatory ecosystem. This work contributes both conceptual models and practical design strategies for future elderly care technologies, offering a pathway toward sustainable, scalable, and human-centered solutions for the 21st century.| File | Dimensione | Formato | |
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phd_unimib_898254.pdf
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Descrizione: Tesi di Mahroo Atieh - 898254
Tipologia di allegato:
Doctoral thesis
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1.53 MB
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