The Heart Rate Monitoring system is developed using Artificial Intelligence
(ANN) and Internet of Things technology to detect the heartbeat and SpO2 of the
patient to monitor the risk of heart attack and also make the person do regular check.ups. Health monitoring is very important to us to make sure our health is in which
condition. The proposed framework examines the sources of information gathered from
sensors fit with the patients to discover the crisis circumstance. The IoT technology is
employed; it helps anyone monitor the patient's health from anywhere. A deep learning
algorithm such as ANN is utilized to identify whether the person’s health is normal or
abnormal. In case an abnormality in health is noticed, an alert will be popped out. The
proposed framework with artificial intelligence and IoT can reduce the death occurring
due to heart rate, and other related issues can also be avoided.
The importance of wearing a mask in public places came to light when the COVID-19 pandemic has started due to the coronavirus. To strictly control the spread of the virus, wearing a mask is mandatory to avoid getting the virus through others or spreading the virus to others if we are carrying it. Since it's not possible to check each individual in public places whether he/she is wearing a mask, this paper proposed a face mask detection using Deep Learning (DL) and Convolutional Neural Network (CNN) techniques. A cloud-based approach that adopted DL is used to identify the persons violating the rules. The dataset used in the work is collected from various studies, such as Prajnasb/observations and Kaggle's Face Mask Detection Dataset that contains images of people wearing and not wearing masks. The faces in the images will be detected and cropped with the help of a trained face detector which will be used for checking whether the face in the image is wearing a mask or not. Face mask detection is done with the help of CNN. The input image is fed into the CNN and the output is binary format, whether person wearing or not wearing a mask. The work uses Max Pooling and Average Pooling layers of CNN. The outcome of the work shows that the proposed method achieves 98 % of accuracy using Max Pooling which is better than the currently available works.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.