School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Department of Systems Design Engineering, University of Waterloo, ON, Canada.
Comput Biol Med. 2024 May;173:108340. doi: 10.1016/j.compbiomed.2024.108340. Epub 2024 Mar 18.
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.
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.
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.
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.
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.
人口老龄化稳步增长，给全球医疗体系带来新的挑战和机遇。技术进步，特别是在商业化的主动辅助生活设备方面，提供了有希望的替代方案。这些易于获得的产品，从智能手表到家庭自动化系统，通常配备人工智能功能，可以监测健康指标、预测不良事件，并促进更安全的生活环境。然而，目前还没有综述探讨人工智能如何融入商业化的主动辅助生活技术，以及这些设备如何在现实世界环境中监测健康指标并提供医疗保健解决方案，以实现健康老龄化。这项综述至关重要，因为它填补了理解人工智能在促进现实世界环境中健康老龄化方面融入主动辅助生活技术方面的知识空白，确定了未来研究中需要解决的关键问题。
本综述旨在概述当前的理解，确定潜在的研究机会，并突出从已发表的研究中关于人工智能在商业化的主动辅助生活技术中的应用的研究空白，这些技术可以帮助在家中老龄化的老年人。
我们在六个数据库（PubMed、CINAHL、IEEE Xplore、Scopus、ACM Digital Library 和 Web of Science）中进行了全面搜索，以确定 2013 年至 2024 年过去十年中发表的相关研究。我们的方法遵循 PRISMA 扩展的范围审查，以确保整个审查过程的严谨性和透明度。在对 825 篇检索文章应用预先确定的纳入和排除标准后，共有 64 篇文章被纳入分析和综合。
从对 64 篇选定论文的分析中出现了几个趋势。大部分工作（39/64，61%）是在 2020 年之后发表的。从地理位置上看，大多数研究来自东亚和北美（36/64，56%）。文献中人工智能的主要应用目标集中在活动识别（34/64，53%），其次是日常监测（10/64，16%）。在方法学上，基于树的和基于神经网络的方法是研究中最常用的人工智能算法（32/64，50%和 31/64，48%）。相当一部分研究（32/64，50%）使用专门设计的智能家居测试床进行研究，这些测试床模拟现实世界的条件。此外，环境技术是一个共同的线索（49/64，77%），占用相关数据（如运动和电器使用日志）和环境传感器（如温度和湿度）是最常使用的。
我们的研究结果表明，在过去十年中，人工智能在现实世界的主动辅助生活环境中得到了越来越多的应用，提供了多种旨在实现健康老龄化和促进老年人独立生活的应用。各种智能家居指标被用于全面数据分析，探索和增强解决方案的潜力和有效性。然而，我们的综述已经确定了多个需要进一步研究的研究空白。首先，大多数研究都是在受控的测试床环境中进行的，缺乏可以验证技术有效性和可扩展性的真实世界应用。其次，利用云技术的研究明显缺乏，而云技术是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力。
