IoT-enabled Smart Home Healthcare Systems for Aging-in-Place
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Keywords

IoT
privacy

How to Cite

[1]
Dr. Ricardo Vargas, “IoT-enabled Smart Home Healthcare Systems for Aging-in-Place”, Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 2, pp. 39–48, Sep. 2024, Accessed: Sep. 18, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/80

Abstract

This paper proposes the design and implementation of IoT-enabled smart home healthcare systems for aging-in-place. The goal is to promote independence and improve the quality of life for elderly individuals living at home. By leveraging IoT technologies, these systems can monitor health metrics, provide assistance with daily activities, and connect seniors with healthcare providers and family members. The paper discusses the key components of such systems, including sensors, actuators, communication protocols, and data analytics. It also explores the challenges and opportunities in deploying these systems, such as privacy concerns, interoperability issues, and user acceptance. Overall, IoT-enabled smart home healthcare systems offer a promising solution for addressing the needs of an aging population and supporting independent living.

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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.

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, “Artificial Intelligence for Standardized Data Flow in Healthcare: Techniques, Protocols, and Real-World Case Studies”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 435–474, Jun. 2023

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.

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. "Integrate security practices and compliance requirements into DevOps processes." MZ Computing Journal 2.2 (2021): 1-19.

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