Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 17; p. 6625
• Main Authors: Salem, Mahmoud, Elkaseer, Ahmed, El-Maddah, Islam A. M., Youssef, Khaled Y., Scholz, Steffen G., Mohamed, Hoda K.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2022; MDPI
• Subjects: Algorithms; Cloud computing; Communication; Data acquisition; Data acquisition systems; Embedded systems; Energy harvesting; Global positioning systems; GPS; Health care; healthcare system; Heart rate; Internet of Things; internet of things (IoT); Life expectancy; machine-to-machine (M2M) communication; Medical diagnosis; Monitoring; non-invasive data acquisition; Older people; Patients; Physicians; Power management; Radio frequency identification; Review; Sensors; Software; Telemedicine; vital data; vital signs
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22176625

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.