Home-Based Elderly Care Monitoring and Healthy Aging Promotion System Using Smart Technologies

互操作性 可穿戴技术 医疗保健 就地老化 远程医疗 健康 可穿戴计算机 晋升(国际象棋) 电子健康 数字健康 范式转换 成功老龄化 远程医疗 医学 数字革命 健康衰老 知识管理 弹性(材料科学) 业务 计算机科学 云计算 独立生活 生活质量(医疗保健) 过程管理 互联网隐私 智能设备 工作(物理) 自治 福祉科技 计算机安全 工程类 远程护理 最佳实践 风险分析(工程) 信息隐私
作者
Renuka,Navdeep Singh,Latha Venkatesh
出处
期刊: 卷期号:: 13-25
标识
DOI:10.65900/jnfcai.2026.v01i03.002
摘要

The rapid escalation of the global aging demographic presents an unprecedented burden on contemporary healthcare infrastructure, necessitating a paradigm shift from traditional, reactive institutionalised care to proactive, home-based management strategies. This comprehensive review article evaluates the current state of the art in home-based elderly care monitoring and healthy aging promotion systems driven by integrated smart technologies. By synthesizing multi-disciplinary evidence across nursing, healthcare informatics, biomedical engineering, and digital health domains, this paper examines how the convergence of the Internet of Things (IoT), wearable physiological sensors, Artificial Intelligence (AI), Machine Learning (ML), smart home environments, mobile health (mHealth) platforms, and telemedicine networks can cooperatively support independent living.We delineate the structural architecture of these systems, mapping the transition of raw sensory telemetry from edge devices through cloud computing layers to actionable clinical decision-making interfaces. Clinical outcomes, including significantly reduced hospitalization rates, accelerated emergency response times, enhanced medication adherence, and improved quality of life indicators, are evaluated alongside the pervasive challenges that hinder widespread clinical translation. These systematic barriers include severe technical limitations in interoperability and battery longevity, ethical concerns surrounding continuous surveillance, and critical gaps in data privacy and cybersecurity infrastructure.Finally, this review outlines essential future paradigms, emphasizing user-centered co-design methodologies, edge-computing solutions to optimize data bandwidth, and the integration of next-generation generative AI frameworks capable of delivering predictive, context-aware personalized care. Ultimately, this work provides a rigorous roadmap for researchers, nursing professionals, healthcare practitioners, and engineers to collaborate on developing robust, ethically sound, and sustainable smart ambient assisted living ecosystems that safeguard autonomy and promote holistic wellness among the global aging population.

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