Breathfinding: A Wireless Network that Monitors and Locates Breathing in a Home

This paper explores using RSS measurements on many links in a wireless network to estimate the breathing rate of a person, and the location where the breathing is occurring, in a home, while the person is sitting, laying down, standing, or sleeping. The main challenge in breathing rate estimation is that "motion interference", i.e., movements other than a person's breathing, generally cause larger changes in RSS than inhalation and exhalation. We develop a method to estimate breathing rate despite motion interference, and demonstrate its performance during multiple short (3-7 minute) tests and during a longer 66 minute test. Further, for the same experiments, we show the location of the breathing person can be estimated, to within about 2 m average error in a 56 square meter apartment. Being able to locate a breathing person who is not otherwise moving, without calibration, is important for applications in search and rescue, health care, and security

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

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

Signal Separation and Tracking Algorithm for Multi-Person Vital Signs by Using Doppler Radar

Noninvasive monitoring is an important Internet-of-Things application, which is made possible with the advances in radio-frequency based detection technologies. Existing techniques however rely on the use of antenna array and/or frequency modulated continuous wave radar to detect vital signs of multiple adjacent objects. Antenna size and limited bandwidth greatly limit the applicability. In this paper, we propose our system termed ‘DeepMining’ which is a single-antenna, narrowband Doppler radar system that can simultaneously track the respiration and heartbeat rates of multiple persons with high accuracy. DeepMining uses a number of signal observations over a period of time as input and returns the trajectory of the respiration and heartbeat rates of each person. The extraction is based on frequency separation algorithms using successive signal cancellation. The proposed system is implemented using the self-injection locking radar architecture and tested in a series of experiments, showing accuracies of 90% and 85% for two and three objects, respectively, even for closely located persons