Keywords
Privacy Concerns
How to Cite
Abstract
The integration of Internet of Things (IoT) technology into smart home healthcare systems has emerged as a promising solution to address the challenges associated with aging-in-place. This paper explores the design, implementation, and impact of IoT-enabled smart home healthcare systems for elderly individuals aiming to age gracefully in their own homes. By leveraging IoT sensors, devices, and platforms, these systems offer personalized and proactive healthcare services, promoting independence and enhancing the overall quality of life for seniors. This research investigates various aspects including system architecture, sensor deployment, data analytics, privacy concerns, and user acceptance. Through a comprehensive review of existing literature and case studies, this paper identifies key challenges and opportunities in designing IoT-enabled smart home healthcare systems for aging-in-place. Furthermore, it discusses potential future directions and recommendations for advancing this field to better meet the evolving needs of elderly individuals and their caregivers.
References
Saeed, A., Zahoor, A., Husnain, A., & Gondal, R. M. (2024). Enhancing E-commerce furniture shopping with AR and AI-driven 3D modeling. International Journal of Science and Research Archive, 12(2), 040-046.
Shahane, Vishal. "A Comprehensive Decision Framework for Modern IT Infrastructure: Integrating Virtualization, Containerization, and Serverless Computing to Optimize Resource Utilization and Performance." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 53-75.
Biswas, Anjanava, and Wrick Talukdar. "Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)." Journal of Science & Technology 4.6 (2023): 55-82.
N. Pushadapu, “AI-Powered Cloud Solutions for Improving Patient Experience in Healthcare: Advanced Models and Real-World Applications”, Hong Kong J. of AI and Med., vol. 4, no. 1, pp. 170–222, Jun. 2024
Talukdar, Wrick, and Anjanava Biswas. "Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity." arXiv preprint arXiv:2408.04023 (2024).
Chen, Jan-Jo, Ali Husnain, and Wei-Wei Cheng. "Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision." Proceedings of SAI Intelligent Systems Conference. Cham: Springer Nature Switzerland, 2023.
Alomari, Ghaith, et al. “AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.” International Journal for Multidisciplinary Research, vol. 6, no. 3, May 2024, pp. 1–24.
Choi, J. E., Qiao, Y., Kryczek, I., Yu, J., Gurkan, J., Bao, Y., ... & Chinnaiyan, A. M. (2024). PIKfyve, expressed by CD11c-positive cells, controls tumor immunity. Nature Communications, 15(1), 5487.
Borker, P., Bao, Y., Qiao, Y., Chinnaiyan, A., Choi, J. E., Zhang, Y., ... & Zou, W. (2024). Targeting the lipid kinase PIKfyve upregulates surface expression of MHC class I to augment cancer immunotherapy. Cancer Research, 84(6_Supplement), 7479-7479.
Gondal, Mahnoor Naseer, and Safee Ullah Chaudhary. "Navigating multi-scale cancer systems biology towards model-driven clinical oncology and its applications in personalized therapeutics." Frontiers in Oncology 11 (2021): 712505.
Saeed, Ayesha, et al. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 1-9.
Pelluru, Karthik. "Enhancing Cyber Security: Strategies, Challenges, and Future Directions." Journal of Engineering and Technology 1.2 (2019): 1-11.
Tatineni, Sumanth, and Sandeep Chinamanagonda. "Machine Learning Operations (MLOps) and DevOps Integration with Artificial Intelligence: Techniques for Automated Model Deployment and Management." Journal of Artificial Intelligence Research 2.1 (2022): 47-81.