ABSTRACT
Wearable sensors and machine learning algorithms are widely used for predicting an individual's thermal sensation. However, most of the studies are limited to controlled laboratory experiments with inconvenient wearable sensors without considering the dynamic behavior of ambient conditions. In this study, we focused on predicting individual dynamic thermal sensation based on physiological and psychological data. We designed a smart face mask that can measure skin temperature (SKT) and exhaled breath temperature (EBT) and is powered by a rechargeable battery. Real-time human experiments were performed in a subway cabin with twenty male students under natural conditions. The data were collected using a smartphone application, and we created features using the wavelet decomposition technique. The bagged tree algorithm was selected to train the individual model, which showed an overall accuracy and f-1 score of 98.14% and 96.33%, respectively. An individual's thermal sensation was significantly correlated with SKT, EBT, and associated features.
Subject(s)
Masks , Railroads , Humans , Skin Temperature , Temperature , Thermosensing/physiologyABSTRACT
BACKGROUND: Needle-free jet injection (NFJI) systems enable a controlled and targeted delivery of drugs into skin tissue. However, a scarce understanding of their underlying mechanisms has been a major deterrent to the development of an efficient system. Primarily, the lack of a suitable visualization technique that could capture the dynamics of the injected fluid-tissue interaction with a microsecond range temporal resolution has emerged as a main limitation. A conventional needle-free injection system may inject the fluids within a few milliseconds and may need a temporal resolution in the microsecond range for obtaining the required images. However, the presently available imaging techniques for skin tissue visualization fail to achieve these required spatial and temporal resolutions. Previous studies on injected fluid-tissue interaction dynamics were conducted using in vitro media with a stiffness similar to that of skin tissue. However, these media are poor substitutes for real skin tissue, and the need for an imaging technique having ex vivo or in vivo imaging capability has been echoed in the previous reports. METHODS: A near-infrared imaging technique that utilizes the optical absorption and fluorescence emission of indocyanine green dye, coupled with a tissue clearing technique, was developed for visualizing a NFJI in an ex vivo porcine skin tissue. RESULTS: The optimal imaging conditions obtained by considering the optical properties of the developed system and mechanical properties of the cleared ex vivo samples are presented. Crucial information on the dynamic interaction of the injected liquid jet with the ex vivo skin tissue layers and their interfaces could be obtained. CONCLUSIONS: The reported technique can be instrumental for understanding the injection mechanism and for the development of an efficient transdermal NFJI system as well.
ABSTRACT
This study developed a thermal-sensation prediction model for individuals by incorporating sensors into a face mask. Conventional prediction of thermal sensation relies on population models, which do not satisfy the requirements of modeling an individual. Developing a model for individuals opens the door to control personalized microclimates. The COVID-19 pandemic has normalized the wearing of face masks;however, their comfort is variable and subjective. We embedded wearable sensors into a face mask to monitor heart rate, skin temperature, and exhalation temperature, determining factors in thermal sensation. Skin temperature, through its thermoregulatory mechanism, plays a vital role in regulating body temperature. As heart rate and exhalation temperature change with metabolic activity, they can predict these temperature changes. During our experiments, we collected physiological and psychological data from human participants at various room temperatures. From this, we found skin temperature and exhalation temperature to show a significant (p < 0.05) positive correlation with perceived thermal sensation. We also developed a random forest classification model for each participant to assess the accuracy of our modeling. We found that smart face masks present a nonintrusive method of measuring physiological data relevant to developing individualized thermal-sensation prediction models, which can be used to improve comfort in indoor environments. The mask we developed could also be adapted further to measure respiration rate, monitor activity, and record other physiological data.