ObjectiveTo study the method of rapid and accurate measurement of body temperature in dense population during the coronavirus disease 2019 pandemic.MethodsFrom January 27th to February 8th, 2020, subjects were respectively measured with two kinds of non-contact infrared thermometers (blue thermometer and red one) to measure the temperature of forehead, neck, and inner side of forearm under the conditions of 4–6℃ (n=152), 7–10℃ (n=103), and 11–25℃ (n=209), while the temperature of axillary was measured with mercury thermometer under the same conditions. Taking the mercury thermometer temperature as the gold standard, the measurement results with non-contact infrared thermometers were compared.ResultsAt 7–10℃, there was no statistical difference among the forehead temperatures measured by the two non-contact infrared thermometers and the axillary temperature (P>0.05); there was no difference among the temperature measured by blue thermometer on forehead, neck, and inner side of forearm (P>0.05); no difference was found between the temperature measured by the red thermometer on forehead and inner side of forearm (P>0.05), while there was statistical difference between the temperatures measured by the red thermometer on forehead and neck (P<0.05). Under the environment of 11−25℃, there was no statistical difference among the forehead temperatures measured by the two infrared thermometers and the axillary temperature (P>0.05); the difference between the temperatures of forehead and inner side of forearm measured by the blue thermometer was statistically significant (P<0.05), while no difference appeared between the forehead and neck temperatures measured by the blue thermometer (P>0.05); there was no statistical difference among the temperatures of three body regions mentioned above measured by the red thermometer (P>0.05). According to the manual, the allowable fluctuation range of the blue thermometer was 0.3℃, and that of the red one was 0.2℃. The mean differences in measured values between different measured sites of the two products were within the allowable fluctuation range. Therefore, the differences had no clinical significance in the environment of 7–25℃. Under the environment of 4–6℃, the detection rate of blue thermometer was 2.2% and that of the red one was 19.1%.ConclusionsThere is no clinical difference between the temperature measured by mercury thermometer and the temperature measured by temperature guns at 7–10 or 11–25℃, so temperature guns can be widely used. In order to maintain the maximum distance between the measuring and the measured persons and reduce the infection risk, it is recommended to choose the inner forearm for temperature measurement. Under the environment of ambient temperature 4–6℃, the detection rate of non-contact electronic temperature gun is low, requiring taking thermal measures for the instrument.
The existing one-time identity authentication technology cannot continuously guarantee the legitimacy of user identity during the whole human-computer interaction session, and often requires active cooperation of users, which seriously limits the availability. This study proposes a new non-contact identity recognition technology based on cardiac micro-motion detection using ultra wideband (UWB) bio-radar. After the multi-point micro-motion echoes in the range dimension of the human heart surface area were continuously detected by ultra wideband bio-radar, the two-dimensional principal component analysis (2D-PCA) was exploited to extract the compressed features of the two-dimensional image matrix, namely the distance channel-heart beat sampling point (DC-HBP) matrix, in each accurate segmented heart beat cycle for identity recognition. In the practical measurement experiment, based on the proposed multi-range-bin & 2D-PCA feature scheme along with two conventional reference feature schemes, three typical classifiers were selected as representatives to conduct the heart beat identification under two states of normal breathing and breath holding. The results showed that the multi-range-bin & 2D-PCA feature scheme proposed in this paper showed the best recognition effect. Compared with the optimal range-bin & overall heart beat feature scheme, our proposed scheme held an overall average recognition accuracy of 6.16% higher (normal respiration: 6.84%; breath holding: 5.48%). Compared with the multi-distance unit & whole heart beat feature scheme, the overall average accuracy increase was 27.42% (normal respiration: 28.63%; breath holding: 26.21%) for our proposed scheme. This study is expected to provide a new method of undisturbed, all-weather, non-contact and continuous identification for authentication.
To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [–4.78, 4.78] beats per minute, and a consistency error of –0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.