Economical and Wearable Pulse Oximeter using IoT

Shinde, Rupali Kiran; Alam, Md. Shahmur; Choi, Min; Kim, Nam

doi:10.1109/iccse51940.2021.9569303

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…In a serial communication system, Raspberry Pi 4 plays the role of a host and an Arduino the role of a slave. The MLX 90614 sensor detects body temperature; the SparkFun sensor detects the blood oxygen level and heartbeat [ 30 , 31 , 32 , 33 ]. The GPS signal is detected by an LM80 sensor connected to a USB port.…”

Section: Methodsmentioning

confidence: 99%

Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%