Estimation of Breathing Rate with Confidence Interval Using Single-Channel CW Radar

Abstract

Breathing rate monitoring using continuous wave (CW) radar has gained much attention due to its contact-less nature and privacy-friendly characteristic. In this work, using a single-channel CW radar, a breathing rate estimation method is proposed that deals with system nonlinearity of a single-channel CW radar and realizes a reliable breathing rate estimate by including confidence intervals. To this end, time-varying dominant Doppler frequency of radar signal, in the range of breathing rate, is extracted in time-frequency domain. It is shown through simulation and mathematical modeling that the average of the dominant Doppler frequencies over time provides an estimation of breathing rate. However, this frequency is affected by noise components and random body movements over time. To address this issue, the sum of these unwanted components is extracted in time-frequency domain, and from their surrogate versions, bootstrap resamples of the measured signal are obtained. Accordingly, a 95% confidence interval is calculated for breathing rate estimation using the bootstrap approach. The proposed method is validated in three different postures including lying down, sitting, and standing, with or without random body movements. The results show that using the proposed algorithm, estimation of breathing rate is feasible using single-channel CW radar. It is also shown that even in presence of random body movements, average of absolute error of estimation for all three postures is 1.88 breath per minute, which represents 66% improvement as compared to the Fourier transform-based approach.

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

“…In 74 studies, both the heart rate and the respiratory rate were measured [2,5,10,12,. In 14 studies, only the respiratory rate was considered [3,11,13,[84][85][86][87][88][89][90][91][92][93][94], and in 26 studies, only the heart rate . Table 2 shows the number of acquisitions of the vital parameters considered in the experiments in relation to the radar type.…”

“…The four main vital parameters include the heart rate, respiratory rate, body (core) temperature and blood pressure [1]. Since they are strongly dependent on the general health status of a person and react to fever, stress and infections [2,3], among other things, they are suitable for identifying acute health problems or for monitoring patients over an extended period of time. The detection of heart and respiratory rates is usually effected via contact electrodes used for taking an electrocardiogram (ECG) and via respiratory belts or indirectly by photoplethysmography within the scope of the measurement of peripheral oxygen saturation.…”

Breathing Rate Classification Using Piezoresistive Sensor Utilizing Continuous Wavelet Transform and Lightweight CNN

“…Feature extraction has been used in [20] to assess the energy distribution of the signal which belongs to three breathing state classes along with nonlinear time series feature extraction on the reflected signal to the process and obtain an accuracy of nearly 97%. In [21], a single-channel continuous wave radar with confidence intervals has been incorporated to provide a valid breathing rate estimation. Retrieval of Doppler frequency in the range of breathing rate and averaging the dominating Doppler frequencies throughout time was used to estimate the breathing rate with the use of mathematical modeling and simulations.…”

“…Retrieval of Doppler frequency in the range of breathing rate and averaging the dominating Doppler frequencies throughout time was used to estimate the breathing rate with the use of mathematical modeling and simulations. To avoid the problem of noise interference, the bootstrap method is used which results in 95% confidence interval for breathing rate estimation [21]. In [22], a videobased respiration and activity monitoring system has been proposed to monitor a primary subject during its sleeping time.…”

Spontaneous Breathing Rate Variations Linked to Social Exclusion and Emotion Self-assessment

“…Secondly, the acquisition and signal processing devices, specifically for breathing rate (BR), are cost-effective and, recently, non-contact, facilitating the monitoring of numerous individuals (Prince et al, 2020 ), and, specifically, people for whom it is inconvenient to use wearable SC or HR sensors. Indeed, in contrast to most psychophysiological manifestations, BR can be measured by using video or radars (Nejadgholi et al, 2019 ). Thirdly, respiration can be modulated conscientiously to reduce BR in order to calm stress responses (Arch & Craske, 2006 ; Zaccaro et al, 2018 ; Zepf et al, 2021 ).…”