The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

导致 2019 年冠状病毒病 (COVID-19) 大流行的严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 已影响全球 4 亿多人。随着最近新的 Delta 和 Omicron 变体的兴起，疫苗的功效已成为一个重要问题。各种研究的目标是通过利用无线传感技术来限制病毒的传播，以防止人与人之间的互动，尤其是对医护人员而言。在本文中，我们讨论了当前有关已有效用于检测、诊断和监控人类活动的侵入式/接触式和非侵入式/非接触式技术（包括 Wi-Fi、雷达和软件无线电）的文献和 COVID-19 相关症状，例如呼吸不规律。此外，我们专注于尖端机器学习算法（例如生成对抗网络、随机森林、多层感知器、支持向量机、极度随机树和 k 最近邻）及其在智能医疗保健系统中的重要作用。此外，本研究强调了与非侵入性技术和前瞻性研究方向相关的局限性。