The rapid increase in healthcare demand has seen novel developments in health monitoring technologies, such as the body area networks (BAN) paradigm. BAN technology envisions a network of continuously operating sensors, which measure critical physical and physiological parameters e.g., mobility, heart rate, and glucose levels. Wireless connectivity in BAN technology is key to its success as it grants portability and flexibility to the user. While radio frequency (RF) wireless technology has been successfully deployed in most BAN implementations, they consume a lot of battery power, are susceptible to electromagnetic interference and have security issues. Intrabody communication (IBC) is an alternative wireless communication technology which uses the human body as the signal propagation medium. IBC has characteristics that could naturally address the issues with RF for BAN technology. This survey examines the on-going research in this area and highlights IBC core fundamentals, current mathematical models of the human body, IBC transceiver designs, and the remaining research challenges to be addressed. IBC has exciting prospects for making BAN technologies more practical in the future.

Intrabody Communication (IBC) is a technique that uses the human body as a transmission medium for electrical signals to connect wearable electronic sensors and devices. Understanding the human body as the transmission medium in IBC paves way for practical implementation of IBC in body sensor networks. In this study, we propose a model for galvanic coupling-type IBC based on a simplified equivalent circuit representation of the human upper arm. We propose a new way to calculate the electrode-skin contact impedance. Based on the model and human experimental results, we discuss important characteristics of galvanic coupling-type IBC, namely, the effect of tissues, anthropometry of subjects, and electrode configuration on signal propagation. We found that the dielectric properties of the muscle primarily characterize the received signal when receiver electrodes are located close to transmitter electrodes. When receiver and transmitter electrodes are far apart, the skin dielectric property affects the received signal.

Intrabody communication (IBC) is a wireless communication technology using the human body to develop body area networks (BANs) for remote and ubiquitous monitoring. IBC uses living tissues as a transmission medium, achieving power‐saving and miniaturized transceivers, making communications more robust against external interference and attacks on the privacy of transmitted data. Due to these advantages, IBC has been included as a third physical layer in the IEEE 802.15.6 standard for wireless body area networks (WBANs) designated as Human Body Communication (HBC). Further research is needed to compare both methods depending on the characteristics of IBC application. Challenges remain for an optimal deployment of IBC technology, such as the effect of long‐term use in the human body, communication optimization through more realistic models, the influence of both anthropometric characteristics and the subject’s movement on the transmission performance, standardization of communications, and development of small‐size and energy‐efficient prototypes with increased data rate. The purpose of this work is to provide an in‐depth overview of recent advances and future challenges in human body/intrabody communication for wireless communications and mobile computing.

This study presents a segmentation method for the blood vessels and provides a method for disease diagnosis in individuals based on retinal images. Blood vessel segmentation in images of the retina is very challenging in medical analysis and diagnosis. It is an essential tool for a wide range of medical diagnoses. After segmentation and binary image improvement operations, the resulting binary images are processed and the features in the blood vessels are used as feature vectors to categorize retinal images and diagnose the type of disease available. To carry out the segmentation task and disease diagnosis, we used a deep learning approach involving a convolutional neural network (CNN) and U-Net++ architecture. A multi-stage method is used in this study to better diagnose the disease using retinal images. Our proposed method includes improving the color image of the retina, applying the Gabor filter to produce images derived from the green channel, segmenting the green channel by receiving images produced from the Gabor filter using U-Net++, extracting HOG and LBP features from binary images, and finally disease diagnosis using a one-dimensional convolutional neural network. The DRIVE and MESSIDOR image banks have been used to segment the image, determine the areas related to blood vessels in the retinal image, and evaluate the proposed method for retinal disease diagnosis. The achieved results for accuracy, sensitivity, specificity, and F1-score are 98.9, 94.1, 98.8, 85.26, and, 98.14, respectively, in the DRIVE dataset and the obtained results for accuracy, sensitivity, and specificity are 98.6, 99, 98, respectively, in MESSIDOR dataset. Hence, the presented system outperforms the manual approach applied by skilled ophthalmologists.