Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware which may cause inconvenience and involve additional appointments with the patient. This study proposes a computer vision-based system using a digital camera to measure SpO2 on the basis of the imaging photoplethysmography (iPPG) signal extracted from the human’s forehead without the need for restricting the subject or physical contact. The proposed camera-based system decomposes the iPPG obtained from the red and green channels into different signals with different frequencies using a signal decomposition technique based on a complete Ensemble Empirical Mode Decomposition (EEMD) technique and Independent Component Analysis (ICA) technique to obtain the optical properties from these wavelengths and frequency channels. The proposed system is convenient, contactless, safe and cost-effective. The preliminary results for 70 videos obtained from 14 subjects of different ages and with different skin tones showed that the red and green wavelengths could be used to estimate SpO2 with good agreement and low error ratio compared to the gold standard of pulse oximetry (SA210) with a fixed measurement position.

Heart rate variability (HRV) is a reliable index and reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, this provides a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. Heart Rate (HR) is a non stationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random during certain intervals of the day. Hence t is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Therefore HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. Here we present an inexpensive AVR microcontroller based data acquisition system with computer interface. Typical data collected is presented and analyzed using Poincare plot and Rescale Range analysis technique and thus fractal dimension of the time series of HR signal is determined and details discussed.

The breast tumor is rarely detected early; early detection helps to quickly and effectively treat. A number of methods are used to detect breast tumors, with mammography being the most popular breast screening method. Mammography is the X-ray tumor in the breast that has some limitation and is quite painful. Microwave imagery provides an enticing mammography alteration by detecting breast tumor using micro strip transmitter at microwave frequency. A fractal shaped UWB MIMO circular ring transmitter has a total size of 60 to 100 mm2 and has a range of frequencies from 3.1 until 12.0 GHZ. The transmitter is designed using (CST) Studio Suite 3D EM simulation and analysis software.

Microwave imagery for identification of Breast cancer is based on the electrical contrast between fatty breast tissues. We implemented a simple fractal antenna in this paper (peano patch antenna) in the Ultra-Wideband frequency (6.744) GHZ as low as (-42.657 dB). For breast imaging on a microwave system, the option of antenna is made of an antenna array consisting of 18 antennas. For better detection of tumors, the antenna is positioned in a circular design so that it can be faced directly to the phantom of the breast. This choice is made by positioning the array antennas on the breast skin to test the magnetic, electrical fields and current density in healthy tissue of breast phantom built and simulated in the studio simulator CST Microwave.

In the last decade the healthcare monitoring schemes have drawn significant considerations of the researchers. With over a decennary concerning muscular lookup or improvement, wireless sensor network science has been rising as much an achievable solution in imitation of numerous revolutionary applications. In that paper, we outline a Wi-Fi sensor network blueprint as we bear advanced the usage of open-source hardware platforms, Arduino yet Raspberry Pi. The scheme is affordable yet very scalable each of phrases of the kind over sensors or the range concerning sensor nodes, who makes it pleasant applicable because of a large choice about functions associated to environmental monitoring. Whole plan construction then the diagram concerning hardware or software elements are offered into specifics in this paper. Some sample outgiving or excuse results are also to show the usefulness over the dictation toughness Moreover working with the intelligent backend scheme construction this scheme can too offer instant physician information in situation if emergency situation occurs without doctors near the side. The outcome of the study offers a present medical care through whole hospital, and the recently invented tag may bring an important change to typical health care process chiefly in patient care.