基于物联网的医疗保健系统口罩检测和医疗传感器的深度学习模型综述

Aniket Dhole, Mohit Gandhi, Shrishail Kumbhar, Harsh Singhal, S. Gore
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引用次数: 1

摘要

医疗传感器如心率、血糖和其他健康监测传感器的增长是巨大的。随着传感器在设备和医疗保健系统中的使用,在边缘设备上使用掩膜检测等图像分类模型的需求也在不断增长。该调查包括现代医疗保健设备中使用的各种技术和各种其他方法,如传感器融合和无线传感器,以收集和监测健康数据。它还包括部署在嵌入式设备(如Raspberry Pi, Nvidia Jetson)和相机(如OpenMV, ESP32Cam)以及深度学习模型(如MobileNetV1, InceptionV4和YOLO Tiny)上的多个掩模检测模型的比较,这些模型使用TensorFlow Lite进行优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review of Deep Learning Models for Mask Detection and Medical Sensors for IoT based Health Care System
The growth of medical sensors like heart rate, blood sugar, and other health monitoring sensors is huge. Along with the use of sensors in devices and healthcare systems, the use of image classification models like mask detection on edge devices is of growing demand. The survey consists of various techniques used in modern healthcare devices and various other methods like sensor fusion and wireless sensors to collect and monitor health data. And it also includes a comparison of multiple mask detection models which were deployed on embedded devices like Raspberry Pi, Nvidia Jetson and cameras like OpenMV, ESP32Cam and deep learning models like MobileNetV1, InceptionV4, and YOLO Tiny which were optimized using TensorFlow Lite.
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