ABSTRACT
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.
Subject(s)
Delivery of Health Care , Vital Signs , HumansABSTRACT
As additive manufacturing has evolved, 3D inkjet printing (IJP) has become a promising alternative manufacturing method able to manufacture functional multi-material parts in a single process. However, issues with part quality in terms of dimensional errors and lack of precision still restrict its industrial and commercial applications. This study aims at improving the dimensional accuracy of 3D IJP parts by developing an optimization-oriented simulation tool of droplet behavior during the drop-on-demand 3D IJP process. The simulation approach takes into consideration the effect of droplet volume, droplet center-to-center distance, coverage percentage of jetted droplets, the contact angle of the ink on the solid substrate and coalescence performance of overlapping droplets, in addition to the number of printed layers. Following the development of the simulation tool using MATLAB, its feasibility was experimentally validated and the results showed a good agreement with a maximum deviation of 2.25% for horizontal features. In addition, the simulated horizontal features are compared with the results of "Inkraster" software, which also illustrates droplet behavior, however, only in 2D. For vertical features, a dial gauge indicator is used to measure the sample height, and the validation results show that the simulation tool can predicate the height of the sample with an average error of 10.89% for a large droplet diameter and 8.09% for a small diameter. The simulation results were found to be in a good agreement with the dimensions of the printed parts. The developed tool was then used to elucidate the effect of resolution of processed TIFF image and droplet diameter on the dimensional accuracy of 3D IJP parts.
