Centre for Data Analytics and Cognition, La Trobe Business School, La Trobe University, Melbourne, VIC 3086, Australia.
Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia.
Sensors (Basel). 2024 Oct 12;24(20):6585. doi: 10.3390/s24206585.
Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.
中风是全球导致长期残疾的主要原因。随着传感器技术和数据可用性的进步，人工智能 (AI) 有望提高护理的数量、质量和效率，并提高中风康复的精准度。我们旨在确定和描述人工智能在成人中风康复和恢复中的应用的现有研究，包括应用的类别和技术随时间的发展。数据是从截至 2024 年 1 月的各种电子数据库中的同行评审文章中收集的。使用 AI 增强的多方法、数据驱动技术，包括主题和话题的聚类，提取了见解。这篇范围综述总结了 704 项研究的结果。确定了四个常见主题（障碍、辅助干预、预测和成像以及神经科学），其中出现了与时间相关的模式。障碍主题揭示了对运动功能、步态和移动性的关注，而辅助干预主题包括机器人和脑机接口 (BCI) 技术的应用。人工智能应用随着时间的推移而发展，从概念化开始，然后扩展到监督学习、人工神经网络 (ANN)、自然语言处理 (NLP) 等更广泛的技术。对手臂康复的应用进行了更详细的审查，使用机器学习 (ML)、深度学习技术和惯性测量单元 (IMU) 等传感器进行手臂和功能运动分析。人工智能应用有可能促进定制的治疗交付，从而有助于优化康复结果，并促进从康复到现实世界环境的持续恢复。
