A wearable textile that is engineered to reflect incoming sunlight and allow the transmission of mid-infrared radiation simultaneously would have a great impact on the human body’s thermal regulation in an outdoor environment. However, developing such a textile is a tough challenge. Using nanoparticle-doped polymer (zinc oxide and polyethylene) materials and electrospinning technology, we have developed a nanofabric with the desired optical properties and good applicability. The nanofabric offers a cool fibrous structure with outstanding solar reflectivity (91%) and mid-infrared transmissivity (81%). In an outdoor field test under exposure of direct sunlight, the nanofabric was demonstrated to reduce the simulated skin temperature by 9 °C when compared to skin covered by a cotton textile. A heat-transfer model is also established to numerically assess the cooling performance of the nanofabric as a function of various climate factors, including solar intensity, ambient air temperature, atmospheric emission, wind speed, and parasitic heat loss rate. The results indicate that the nanofabric can completely release the human body from unwanted heat stress in most conditions, providing an additional cooling effect as well as demonstrating worldwide feasibility. Even in some extreme conditions, the nanofabric can also reduce the human body’s cooling demand compared with traditional cotton textile, proving this material as a feasible solution for better thermoregulation of the human body. The facile fabrication of such textiles paves the way for the mass adoption of energy-free personal cooling technology in daily life, which meets the growing demand for healthcare, climate change, and sustainability.

Radio frequency (RF) sensors such as radar are instrumental for continuous, contactless sensing of vital signs, especially heart rate (HR) and respiration rate (RR). However, decades of related research mainly focused on static subjects, because the motion artifacts from other body parts may easily overwhelm the weak reflections from vital signs. This paper marks a first step in enabling RF vital sign sensing under ambulant daily living conditions. Our solution is inspired by existing physiological research that revealed the correlation between vital signs and body movement. Specifically, we propose to combine direct RF sensing for static instances and indirect vital sign prediction based on movement power estimation. We design customized machine learning models to capture the sophisticated correlation between RF signal pattern, movement power, and vital signs. We further design an instant calibration and adaptive training scheme to enable cross-subjects generalization, without any explicit data labeling from unknown subjects. We prototype and evaluate the framework using a commodity radar sensor. Under a variety of moving conditions, our solution demonstrates an average estimation error of 5.57 bpm for HR and 3.32 bpm for RR across multiple subjects, which largely outperforms state-of-the-art systems.

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