Abstract
The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.
Highlights
There has been a recent rapid increase in data generation from users and Internet of Things (IoT)devices such as sensors, controllers, and others
The support vector machines (SVMs)-based model achieves an average accuracy of 97.9% for acceptable ECG segments in real wearable ECG monitoring
Real-Time PMS Architecture using a broker of Message Queuing Telemetry Transport (MQTT) A Convolutional Neural Network (CNN) based deep learning technique A combination of multiple signal quality indices (SQI) and Support vector machine (SVM)- based classification
Summary
There has been a recent rapid increase in data generation from users and Internet of Things (IoT). Robust services in healthcare systems may be provided by IoT-based solutions such as remote patient control, robots, and others, especially in quarantined hospitals Data generated from these devices can be conveyed to medical systems in order to handle situations and obtain accurate results using machine learning [9]. With the increasing number of IoT environments and applications such as smart cities, smart health, smart homes, energy management, transportation, elder care, and environmental monitoring, researchers and developers are focused on both security and privacy, which have become more challenging. This technology has become more ubiquitous due to the increasing number of wearable devices. Section presents ECG deployment opportunities and challenges and Section provides a conclusion and suggestions for future work
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