Abstract
This paper employs a unique sensor fusion (SF) approach to detect a COVID-19 suspect and the enhanced MobileNetV2 model is used for face mask detection on an Internet-of-Things (IoT) platform. The SF algorithm avoids incorrect predictions of the suspect. Health data are continuously monitored and recorded on the ThingSpeak cloud server. When a COVID-19 suspect is detected, an emergency email is sent to healthcare personnel with the GPS position of the suspect. A lightweight and fast deep learning model is used to recognize appropriate mask positioning; this restricts virus transmission. When tested with the real-world masked face dataset (RMFD) dataset, the enhanced MobileNetV2 neural network is optimal for Raspberry Pi. Our IoT device and deep learning model are 98.50% (compared to commercial devices) and 99.26% accurate, respectively, and the time required for face mask evaluation is 31.1 milliseconds. The proposed device is useful for remote monitoring of covid patients. Thus, the method will find medical application in the detection of COVID-19-positive patients. The device is also wearable.
Highlights
In December 2019, a pneumonia-like disease began to spread worldwide, accompanied by fever and cold-like symptoms [1,2], caused by the COVID-19 (Coronavirus disease of 2019) virus [3,4]
The MLX 90614 sensor was tested on the same individual; readings were obtained at 10-min intervals and compared to those of a commercial thermometer (Figure 7)
The MLX 90614 sensor was tested on the same individual; readings were obtained at 10-min9inofte18rvals and compared to those of a commercial thermometer (Figure 7)
Summary
In December 2019, a pneumonia-like disease began to spread worldwide, accompanied by fever and cold-like symptoms [1,2], caused by the COVID-19 (Coronavirus disease of 2019) virus [3,4]. The World Health Organization (WHO) declared COVID-19 a Public Health Emergency of International Concern on 30 January followed by declaration of pandemic on 11 March 2020. Pandemic affects people’s mental and physical health. 401 million COVID-19 cases have been detected, with 5.76 million deaths confirmed. The increasing number of COVID-19 cases and deaths have led to worldwide lockdowns, quarantines, and restrictions on human movements. Abdulkadir Atalan mentioned that lockdowns could suppress the spread of the virus. Reference [4] mentioned the effects of lockdowns on psychology, the environment, and the economy. Various studies have shown the effects of lockdowns on economics, domestic abuse, mental health, and social health [5]
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