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The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions. 

Abstract Liquid metals (LMs), renowned for their high conductivity and large deformability, find increasing applications including in flexible electronics and soft robotics. One critical process in these applications is the precise patterning of LMs into desired shapes. Yet, existing LM patterning techniques predominantly focus on 2D patterns due to challenges posed by the inherent fluidity and leakage of LMs. Here, we introduce an approach that bypasses these limitations, enabling the creation of complex 3D leakage‐free LM structures. This is achieved through mechanical programming of 2D magnetically immobilized LM paste formed via incorporating magnetic particles into LMs. Such composite effectively resists leakage due to the combined effect of strong magnetic inter‐attraction within the porous magnetic networks and the high surface tension of LMs, while retaining the high conductivity. Diverse freestanding magnetic LM structures, obtained upon LM solidification at ambient temperature, dynamically morph between their 2D and various 3D configurations through multiple cycles of induction heating and magnetic‐assisted reprogramming, featuring large compression resistance and self‐healing capabilities. Potential applications of these leakage‐resistant, shape‐adaptable structures are demonstrated through a helical magnetic LM antenna, which showcases its efficiency in wireless communication and energy harvesting.