背景:老龄化人口正在稳步增加,给全球医疗保健系统带来新的挑战和机遇。技术进步,特别是在商业上可用的主动辅助生活设备中,提供一个有希望的替代方案。这些容易获得的产品,从智能手表到家庭自动化系统,通常配备了可以监控健康指标的人工智能功能,预测不良事件,并促进更安全的生活环境。然而,没有评论探索人工智能如何被整合到商业上可用的主动辅助生活技术中,以及这些设备如何监控健康指标,并在现实环境中提供健康老龄化的医疗保健解决方案。这篇评论是必不可少的,因为它填补了理解人工智能在主动辅助生活技术中的整合在现实环境中促进健康老龄化的知识空白。确定需要在未来研究中解决的关键问题。
目的:本概述的目的是概述当前的理解,确定潜在的研究机会,并强调了已发表的研究中有关在商业上可用的主动辅助生活技术中使用人工智能的研究差距,这些技术可以帮助老年人在家中衰老。
方法:在六个数据库中进行了全面搜索-PubMed,CINAHL,IEEEXplore,Scopus,ACM数字图书馆,和WebofScience-确定2013年至2024年过去十年发表的相关研究。我们的方法遵循PRISMA扩展范围审查,以确保整个审查过程的严密性和透明度。在对825篇检索到的文章应用预定义的纳入和排除标准后,共纳入64篇论文进行分析和综合.
结果:我们对选定的64篇论文的分析出现了一些趋势。大部分作品(39/64,61%)是在2020年之后发表的。地理上,大多数研究来自东亚和北美(36/64,56%)。在综述的文献中,人工智能的主要应用目标集中在活动识别(34/64,53%),其次是每日监测(10/64,16%)。方法上,基于树和基于神经网络的方法是研究中使用的最普遍的人工智能算法(分别为32/64,50%和31/64,48%)。相当比例的研究(32/64,50%)使用专门设计的智能家居测试床进行研究,以模拟现实世界中的条件。此外,环境技术是一个常见的线程(49/64,77%),与占用相关的数据(如运动和电器使用日志)和环境传感器(如温度和湿度指标)是最常用的。
结论:我们的研究结果表明,在过去的十年中,人工智能越来越多地部署在现实世界的主动辅助生活环境中。提供各种旨在健康老龄化和促进老年人独立生活的应用程序。广泛的智能家居指标被用于全面的数据分析,探索和提高解决方案的潜力和有效性。然而,我们的综述发现了多个需要进一步调查的研究空白.首先,大多数研究都是在受控的试验台环境中进行的,留下了一个缺乏现实世界的应用程序,可以验证技术\的功效和可扩展性。第二,明显缺乏利用云技术的研究,大规模部署和标准化数据收集和管理的重要工具。未来的工作应该优先考虑这些领域,以最大限度地发挥人工智能在主动辅助生活环境中的潜在优势。
BACKGROUND: The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI\'s integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies.
OBJECTIVE: The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home.
METHODS: A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis.
RESULTS: Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used.
CONCLUSIONS: Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies\' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.