Design an easy-to-use infection screening system for non-contact monitoring of vital-signs to prevent the spread of pandemic diseases

Abstract

The outbreak of infectious diseases such as influenza, dengue fever, and severe acute respiratory syndrome (SARS) are threatening the global health. Especially, developing countries in the South-East Asia region have been at serious risk. Rapid and highly reliable screening of infection is urgently needed during the epidemic season at mass gathering places, such as airport quarantine facilities, public health centers, and hospital outpatients units, etc. To meet this need, our research group is currently developing a multiple vital-signs based infection screening system that can perform human medical inspections within 15 seconds. This system remotely monitors facial temperature, heart and respiration rates using a thermopile array and a 24-GHz microwave radar, respectively. In this work, we redesigned our previous system to make a higher performance with a user-friendly interface. Moreover, the system newly included a multivariable logistic regression model (MLRM) to determine the possibility of infection. We tested the system on 34 seasonal influenza patients and 35 normal control subjects at the Japan Self-Defense Forces Central Hospital. The sensitivity and specificity of the screening system using the MLRM were 85.3% and 88.6%, respectively.

A non-contact infection screening system using medical radar and Linux-embedded FPGA: Implementation and preliminary validation

“…In addition, three previous methods to measure vital signs from the digital radar signal, namely Auto-Correlation [4], FFT [5,6] and MUSIC [7], were also evaluated by the t-test with the reference method. Those measures were operated on our dataset of 101 subjects.…”

“…Specifically, the software level of the board enabled the implementations of all algorithms in Python for both the signal processing and classification tasks. Instead of inflexible built-in algorithms on computer software [4][5][6][7], flexible digital filters were designed to process the radar signal. The filters were designed with SciPy [24], a Python library for mathematics, science, and engineering.…”

“…Therefore, the inflexible methods used in Refs. [4][5][6][7] should be highly influenced by this variability. In this study, to measure the subjects' respiratory rate more accurately, a low-pass filter was designed with different cut-off frequencies: 30 [BPM] (0.5 breaths per second) if the subject is aged 18 or more, and 20 [BPM] (0.33 breaths per second) otherwise.…”

“…Our previous studies were conducted focusing on the noncontact dengue fever detecting systems based on multiple vital signs using medical radar and thermography. For example, [4][5][6][7] developed methods that processed the recorded signal to obtain heart rate (HR) and respiratory rate (RR) by built-in algorithms, namely Autocorrelation, Fast Fourier transform (FFT) and Multiple Signal Classification (MUSIC), on computer software. However, those research studies used built-in algorithms with fixed parameters regardless of the age and sex of subjects, which could reduce the accuracy of the system.…”

“…To deal with the variability in HR [12] and RR [13,14] depending on age and gender, instead of the inflexible methods used in Refs. [4][5][6][7], filters with flexible cut-off frequencies were designed in this work. For the classification problem, two machine learning models, namely Support Vector Machine (SVM) [15] and Quadratic Discriminant Analysis (QDA) [16] were used, with a far larger dataset of 101 samples, than the 18-sample dataset in Ref.…”