An inclusive survey of contactless wireless sensing: a technology used for remotely monitoring vital signs has the potential to combating COVID-19

With the Coronavirus pandemic showing no signs of abating, companies and governments around the world are spending millions of dollars to develop contactless sensor technologies that minimize the need for physical interactions between the patient and healthcare providers. As a result, healthcare research studies are rapidly progressing towards discovering innovative contactless technologies, especially for infants and elderly people who are suffering from chronic diseases that require continuous, real-time control, and monitoring. The fusion between sensing technology and wireless communication has emerged as a strong research candidate choice because wearing sensor devices is not desirable by patients as they cause anxiety and discomfort. Furthermore, physical contact exacerbates the spread of contagious diseases which may lead to catastrophic consequences. For this reason, research has gone towards sensor-less or contactless technology, through sending wireless signals, then analyzing and processing the reflected signals using special techniques such as frequency modulated continuous wave (FMCW) or channel state information (CSI). Therefore, it becomes easy to monitor and measure the subject’s vital signs remotely without physical contact or asking them to wear sensor devices. In this paper, we overview and explore state-of-the-art research in the field of contactless sensor technology in medicine, where we explain, summarize, and classify a plethora of contactless sensor technologies and techniques with the highest impact on contactless healthcare. Moreover, we overview the enabling hardware technologies as well as discuss the main challenges faced by these systems.This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under grand 119E39

Wearable Biosensor: How to improve the efficacy in data transmission in respiratory monitoring system?

Respiratory rate measurement is important under different types of health issues. The need for technological developments for measuring respiratory rate has become imperative for healthcare professionals. The paper presents an approach to respiratory monitoring, with the aim to improve the accuracy and efficacy of the data monitored. We use multiple types of sensors on various locations on the body to continuously transmit real-time data, which is  rocessed to calculate the respiration rate. Variations in the respiration rate will help us identify the current health condition of the patient also for diagnosis and further medical treatment. The software tools such as Keil μVision IDE, Mbed Studio IDE, Energia IDE are used to compile and build the system architecture and display information. EasyEDA is used to provide pin map details and complete architecture information

Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

A dual-mode Ultra-Wideband wireless platform for remote patient monitoring systems

The combination of two factors demands the need to find a solution that guarantees the well-being of the people suffering from chronic diseases. On the one hand, the increase of the life expectancy leads to an older world's population. On the other hand, the aged are more likely to suffer from chronic diseases and/or injuries. This thesis deals with the design of an Ultra-Wideband-based node of a Remote Patient Monitoring (RPM) network. This node must be able to measure, collect and transmit some medical parameters of a patient. Existing RPM networks use two different hardware platforms: one for measuring and another one for transmitting. This leads to high cost and high power consumption. Since a RPM network is typically composed by hundreds or thousands of nodes, a new platform with lower cost and power consumption is vital to make such a system work. This thesis explores the viability to achieve the dual-mode operation: Radar Mode (RM) to obtain a certain parameter and Data Transmission Mode (DTM) to send it to another node. A platform using Impulse-Radio Ultra-Wideband (IR-UWB) has been proposed to accomplish this goal. The simulations done verified its feasibility. Moreover, the physical experiments carried out validated the transmitter. Nevertheless, due to time and hardware limitations, the receiver has not been experimentally validated yet

Respiratory Rate Estimation Using WiFi Channel State Information - A Machine Learning Approach

Respiratory rate (RR) is an important vital sign for diagnosing and treating a number of medical conditions. Current respiration monitoring systems require that a special device is continuously attached to the human body. However, contactless respiration monitoring systems have recently been developed to overcome this inconvenience. Research has shown that channel state information (CSI) measured by WiFi devices can be used for estimating RR. Although pattern-based respiration detection has been used to extract RR from periodic changes in CSI, systems based on this method do not perform well when channel conditions are not favorable. This thesis highlights newly introduced learning-based approaches used for RR estimation. Off-the-shelf WiFi devices were used to collect fine-grained wireless CSI data, which was then used to train and evaluate machine learning models. Results show that classification algorithms, including KNN, SVM, Random Forest, Logistic Regression and MLP, achieve over 96% accuracy when predicting RR. Regression models were compared to an existing pattern-based system, demonstrating that the majority of regression models have better performance when estimating RR. For instance, Logistic Regression’s Root Mean Square Error (RMSE) is 0.35, while pattern-based system’s RMSE is 2.7. It is important to note that classification and regression models cannot be generalized, nor can they accurately predict respiratory rate using the data collected from a new and previously unseen subject. To improve and make the models more generalizable, data used to train the models must be collected from a larger number of subjects

Contactless Electrocardiogram Monitoring with Millimeter Wave Radar

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring

Positioning and Sensing System Based on Impulse Radio Ultra-Wideband Technology

Impulse Radio Ultra-Wideband (IR-UWB) is a wireless carrier communication technology using nanosecond non-sinusoidal narrow pulses to transmit data. Therefore, the IR-UWB signal has a high resolution in the time domain and is suitable for high-precision positioning or sensing systems in IIoT scenarios. This thesis designs and implements a high-precision positioning system and a contactless sensing system based on the high temporal resolution characteristics of IR-UWB technology. The feasibility of the two applications in the IIoT is evaluated, which provides a reference for human-machine-thing positioning and human-machine interaction sensing technology in large smart factories. By analyzing the commonly used positioning algorithms in IR-UWB systems, this thesis designs an IRUWB relative positioning system based on the time of flight algorithm. The system uses the IR-UWB transceiver modules to obtain the distance data and calculates the relative position between the two individuals through the proposed relative positioning algorithm. An improved algorithm is proposed to simplify the system hardware, reducing the three serial port modules used in the positioning system to one. Based on the time of flight algorithm, this thesis also implements a contactless gesture sensing system with IR-UWB. The IR-UWB signal is sparsified by downsampling, and then the feature information of the signal is obtained by level-crossing sampling. Finally, a spiking neural network is used as the recognition algorithm to classify hand gestures