The unprecedented non-contact, non-invasive, and privacy-preserving nature of radar sensors has enabled various healthcare applications, including vital sign monitoring, fall detection, gait analysis, activity recognition, fitness evaluation, and sleep monitoring. Machine learning (ML) is revolutionizing every domain, with radar-based healthcare being no exception. Progress in the field of healthcare radars and ML is complementing the existing radar-based healthcare industry. This article provides an overview of ML usage for two major healthcare applications: vital sign monitoring and activity recognition. Vital sign monitoring is the most promising healthcare application of radar, as it can predict several chronic cardiac and respiratory diseases. Activity recognition is also a prominent application since the inability to perform activities may result in critical suffering. The article presents an overview of commercial radars, radar hardware, and historical progress of healthcare radars, followed by the usage of ML for healthcare radars. Subsequently, the paper discusses how ML can overcome the limitations of conventional radar data processing chains for healthcare radars. The article also touches upon recent generative ML concepts used in healthcare radars. Among several interesting findings, it was discovered that ML does not completely replace existing vital sign monitoring algorithms; rather, ML is deployed to overcome the limitations of traditional algorithms. On the other hand, activity recognition always relies on ML approaches. The most widely used algorithms for both applications are Convolutional Neural Network (CNN) followed by Support Vector Machine (SVM). Generative AI has the capability to augment data and is expected to have a significant impact soon. Recent trends, lessons learned from these trends, and future directions for both healthcare applications are presented in detail. Finally, the future work section discusses a wide range of healthcare topics for humans, ranging from neonates to elderly individuals.

中文翻译：

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用于医疗雷达的机器学习：人体生命体征测量和活动识别的最新进展

雷达传感器前所未有的非接触式、非侵入性和隐私保护特性使得各种医疗保健应用成为可能，包括生命体征监测、跌倒检测、步态分析、活动识别、健康评估和睡眠监测。机器学习 (ML) 正在彻底改变每个领域，基于雷达的医疗保健也不例外。医疗保健雷达和机器学习领域的进展正在补充现有的基于雷达的医疗保健行业。本文概述了机器学习在两个主要医疗保健应用中的使用：生命体征监测和活动识别。生命体征监测是雷达最有前途的医疗保健应用，因为它可以预测多种慢性心脏和呼吸系统疾病。活动识别也是一个突出的应用，因为无法执行活动可能会导致严重的痛苦。本文概述了商用雷达、雷达硬件和医疗雷达的历史进展，然后介绍了机器学习在医疗雷达中的使用。随后，本文讨论了机器学习如何克服医疗雷达传统雷达数据处理链的局限性。本文还涉及了医疗保健雷达中最新使用的生成机器学习概念。在一些有趣的发现中，机器学习并没有完全取代现有的生命体征监测算法；相反，部署机器学习是为了克服传统算法的局限性。另一方面，活动识别始终依赖于机器学习方法。这两种应用中使用最广泛的算法是卷积神经网络 (CNN)，其次是支持向量机 (SVM)。生成式人工智能具有增强数据的能力，预计很快就会产生重大影响。详细介绍了这两种医疗保健应用的最新趋势、从这些趋势中吸取的经验教训以及未来的方向。最后，未来的工作部分讨论了从新生儿到老年人的广泛的人类医疗保健主题。