Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

The importance of health for everyone to be able to empower everything that is owned and the environment maximally, health is closely related to healthy living. So that can be described the meaning of healthy life is to have health in life with no problems of disruption in life both physical in the form of disease in the body and non-physical that is related to the condition of the soul, heart and mind of a person in life both individually and socially. The use of expert systems with the search model with the Fuzzy Mamdani method can help to know the condition of the disease that may be suffered by each individual based on the pattern of life that he did.

Expert System for Disease Risk Based on Lifestyle with Fuzzy Mamdani

A review of enabling technologies for Internet of Medical Things (IoMT) Ecosystem

The homogeneity map method for speckle reduction in diagnostic ultrasound images

Securing e-Health application of cloud computing using hyperchaotic image encryption framework

Respiration rate is monitored in most diseases, especially ones like sleep apnea and respiratory arrest. However, most devices available for monitoring are expensive, fragile and often need trained staff for operation. For obvious reasons, constantly monitoring the patient's breathing rate is also not feasible and our proposed module tends to modify this scenario. The hardware module uses a thermistor to monitor the patient's breathing rate. This is less costly, and also involves low power consumption as well as robust performance. The device also uses cloud computing to transmit the data to the doctor's smartphone app as well as to the patient's relatives' correspondence. A buzzer attached to the system notifies nearby medics in case of abnormal breathing rates. The device is specially adapted to suit rural Indian conditions. The prototype has been substantiated for accurate monitoring of breathing rates through the standard tests.

Real time Android app based respiration rate monitor

Diagnostic Ultrasound Image Enhancement: A Multiscale Permutation Approach

This paper describes an algorithm of encrypting wave files in image format with a randomly generated orthogonal key decrypting them after being passed through a channel with white Gaussian noise. Gaussian noise is then removed and then matrix manipulations are done, to get the original audio back. In this age of information, audio encryption can be a secure way to communicate between individuals with assurance of privacy of the data.

Audio encryption and decryption algorithm in image format for secured communication

Over the past three to four decades the SAR-synthetic Aperture Radar imagery has become a beneficial and important application over the optical Satellite imagery, Because of its ability to operate in all-weather condition and capability of all time data acquisition; The main issue in this imaging technique is presence of grainy noise termed as ‘speckle’, Which will affect the overall quality of image, hence it is highly required to use effective speckle reduction techniques to suppress speckle. Various filtering techniques are available to reduce the speckle noise and to enhance the quality of the image. The main objective of this paper is to present a quantitative approach of de-specking algorithm on SAR images with adequate mathematical parametric analysis.

Quantitative approach of speckle noise reduction on synthetic Aperture radar images

In this paper an algorithm for automatic segmentation of neonatal brain MRI is proposed. The first step of the process involves skull stripping and then noise removal using anisotropic diffusion filter. The image is then segmented into three clusters namely white matter, grey matter and cerebrospinal fluid. The segmentation strategy is based on expectation maximization of Gaussian mixture model of tissues. Each pixel is assigned to the tissue cluster which showed the maximum probability of being in. The pixels with same probability likelihoods for more than one cluster are also dealt successfully. The results are evaluated using Dice similarity coefficient. A maximum Dice similarity coefficient of 0.832 is obtained for white matter on T2 images.

S. Kalaivani

Papers

Real time Android app based respiration rate monitor

Diagnostic Ultrasound Image Enhancement: A Multiscale Permutation Approach

Audio encryption and decryption algorithm in image format for secured communication

Quantitative approach of speckle noise reduction on synthetic Aperture radar images

Automatic tissue segmentation of neonatal brain MRI