Requena Carmen, Plaza-Carmona María, Álvarez-Merino Paula, López-Fernández Verónica
Department of Psychology, University of León, León, Spain.
Department of Education, University Internacional de La Rioja and León University Hospital Complex, León, Spain.
Front Public Health. 2024 Dec 4;12:1476916. doi: 10.3389/fpubh.2024.1476916. eCollection 2024.
Monitoring daily activities in older adults using sensor technologies has grown significantly over the past two decades, evolving from simple tools to advanced systems that integrate Artificial Intelligence (AI) and the Internet of Things (IoT) for predictive monitoring. Despite these advances, there is still a need for a comprehensive review that addresses both technological progress and its impact on autonomous aging.
To conduct a systematic review of sensor technologies used to monitor the daily activities of independent older adults, focusing on sensor types, applications, usage contexts, and their evolution over time.
A search was conducted in PubMed, Scopus, Web of Science, PsycInfo, and Google Scholar databases, covering studies published between 2000 and 2024. The 37 selected studies were assessed in terms of methodological quality and organized into four chronological stages, allowing for an examination of the progressive development of these technologies. Each stage represents an advance in sensor type, technological application, and implementation context, ranging from basic sensors to intelligent systems in multi-resident homes.
Findings indicate a clear progression in the accuracy and applicability of sensors, which evolved from fall detection to predictive interventions tailored to each user's needs. Furthermore, the taxonomic classification of studies shows how sensors have been adapted to monitor physical, cognitive, and social dimensions, laying the groundwork for personalized care.
Sensors represent a promising tool for promoting the independence and well-being of older adults, enabling proactive and personalized interventions in everyday settings. However, the lack of standardization in key parameters limits comparability between studies and highlights the need for consensus to facilitate the design of effective interventions that promote autonomous and healthy aging.
在过去二十年中，利用传感器技术监测老年人日常活动的情况显著增加，从简单工具发展到集成人工智能（AI）和物联网（IoT）进行预测性监测的先进系统。尽管取得了这些进展，但仍需要进行全面综述，以探讨技术进步及其对自主老龄化的影响。
对用于监测独立老年人日常活动的传感器技术进行系统综述，重点关注传感器类型、应用、使用背景及其随时间的演变。
在PubMed、Scopus、Web of Science、PsycInfo和谷歌学术数据库中进行搜索，涵盖2000年至2024年发表的研究。对所选的37项研究进行方法学质量评估，并按时间顺序分为四个阶段，以便考察这些技术的逐步发展。每个阶段代表传感器类型、技术应用和实施背景的进步，从基本传感器到多住户家庭中的智能系统。
研究结果表明传感器在准确性和适用性方面有明显进展，从跌倒检测发展到根据每个用户的需求量身定制的预测性干预措施。此外，研究的分类显示了传感器如何被调整以监测身体、认知和社会层面，为个性化护理奠定了基础。
传感器是促进老年人独立性和福祉的有前途的工具，能够在日常环境中进行主动和个性化干预。然而，关键参数缺乏标准化限制了研究之间的可比性，并凸显了达成共识以促进有效干预措施设计的必要性，这些措施有助于实现自主和健康老龄化。
