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Modern microwave Doppler radar-based physiological sensing is playing an important role in healthcare applications and during the last decade, there has been a significant advancement in this non-contact respiration sensing technology. The advantages of contactless, unobtrusive respiration monitoring have drawn interest in various medical applications such as sleep apnea, sudden infant death syndromes (SIDS), remote respiratory monitoring of burn victims, and COVID patients. This paper provides a perspective on recent advances in biomedical and healthcare applications of Doppler radar that can detect the tiny movement of the chest surfaces to extract heartbeat and respiration and its associated different vital signs parameters (tidal volume, heart rate variability (HRV), and so on) of the human subject. Additionally, it also highlights the challenges, and opportunities of this remote physiological sensing technology and several future research directions will be laid out to deploy this sensor technology in our day-to-day life.

Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset. [ABSTRACT FROM AUTHOR]

Breathing Rate (BR) is a key physiological parameter measured in a wide range of clinical settings. However, it is still widely measured manually. In this paper, a novel framework is proposed to estimate the BR from an electrocardiogram (ECG), a photoplethysmogram (PPG), or a blood pressure (BP) signal. The framework uses Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) methods to extract respiratory signals, taking advantage of both time and frequency domain information. An Extended Kalman Filter (EKF), incorporating a Signal Quality Index (SQI), enabled our method to achieve acceptable performance even for significantly distorted periods of the signals. Using state vector fusion, the output signals are combined and finally the BR is estimated. The framework was tested on two publicly available clinical databases: the MIT-BIH Polysomnographic and BIDMC databases. Performance was evaluated using the mean absolute percentage error (MAPE). The results indicated high accuracy: MAPEs on the two databases of 3.9% and 3.6% for ECG signals, 6.0% for PPG, and 5.0% for BP signals. The results also indicated high robustness to noise down to $0dB$ . Therefore, this framework may have utility for BR monitoring in high noise settings.

Recent advances in wearable technologies integrating multi-modal sensors have enabled the in-field monitoring of several physiological metrics. In sport applications, wearable devices have been widely used to improve performance while minimizing the risk of injuries and illness. The objective of this project is to estimate breathing rate (BR) from respiratory sinus arrhythmia (RSA) using heart rate (HR) recorded with a chest belt during physical activities, yielding additional physiological insight without the need of an additional sensor. Thirty-one healthy adults performed a run at increasing speed until exhaustion on an instrumented treadmill. RR intervals were measured using the Polar H10 HR monitoring system attached to a chest belt. A metabolic measurement system was used as a reference to evaluate the accuracy of the BR estimation. The evaluation of the algorithms consisted of exploring two pre-processing methods (band-pass filters and relative RR intervals transformation) with different instantaneous frequency tracking algorithms (short-term Fourier transform, single frequency tracking, harmonic frequency tracking and peak detection). The two most accurate BR estimations were achieved by combining band-pass filters with short-term Fourier transform, and relative RR intervals transformation with harmonic frequency tracking, showing 5.5% and 7.6% errors, respectively. These two methods were found to provide reasonably accurate BR estimation over a wide range of breathing frequency. Future challenges consist in applying/validating our approaches during in-field endurance running in the context of fatigue assessment.

It is challenging to extract signs of life under low signal to noise ratio (SNR) using a contactless radar in complex environments. Via analyzing the characteristics of signs of life, a feasible localization and detection technique is investigated by employing the through-wall ultra-wide band (UWB) radar. Based on the time-frequency matrix from the wavelet transform (WT) technique performed on the skewness and kurtosis of the received pulses, the distance from the radar to human beings is acquired. Human breathing rate (BR) is measured only using the pulses in the effective area. Different experiments are conducted to test the presented detection technique compared with several traditional methods. [ABSTRACT FROM AUTHOR]

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice. [ABSTRACT FROM AUTHOR]

We present an adaptive biofeedback game for teaching self-regulation of stress. Our approach consists of monitoring the user's physiology during gameplay and adapting the game using a positive feedback loop that rewards relaxing behaviors and penalizes states of high arousal. We evaluate the approach using a casual game under three biofeedback modalities: electrodermal activity, heart rate variability, and breathing rate. The three biosignals can be measured noninvasively with wearable sensors, and represent different degrees of voluntary control and selectivity toward arousal. We conducted an experiment trial with 25 participants to compare the three modalities against a standard treatment (deep breathing) and a control condition (the game without biofeedback). Our results indicate that breathing-based game biofeedback is more effective in inducing relaxation during treatment than the other four groups. Participants in this group also showed greater retention of the relaxation skills (without biofeedback) during a subsequent stressor. [ABSTRACT FROM AUTHOR]

Summary: Background & aims: Energy expenditure (EE) during sitting is widely assumed to be higher than that while lying down, but supporting evidence is equivocal. Despite this, resting EE in the sitting position is often used as a proxy for basal metabolic rate. Here we investigate whether EE differs in the comfortable seated position compared to supine (lying) position. Methods: EE and respiratory quotient (RQ) were measured (by ventilated hood indirect calorimetry) in 19 healthy subjects (9 men, 10 women) after an overnight fast. Supine measurements were made using a comfortable clinical tilting table and sitting measurements made using an adjustable, ergonomic car seat adapted for the hood system. After about 30 min of rest in either position, metabolic monitoring was conducted until stabilization of EE for at least 15 min in each posture. Results: EE in the sitting position was not significantly different compared to supine (<2% difference). By contrast, heart rate was higher by 7 beats/min (p < 0.05). RQ was slightly but significantly decreased during sitting compared to lying (p < 0.05), with no change in breathing rate. Conclusions: This study suggests that the ventilated hood calorimetry system for assessment of REE after an overnight fast in a comfortable sitting position can be used as a good proxy of the basal metabolic rate. It also underscores the applicability of the ventilated hood system to measurements of resting EE in the sitting posture which, compared to supine posture, may be more acceptable/convenient to the subject/patient participating in postprandial metabolic studies lasting several hours. [Copyright &y& Elsevier]

This paper shows the design of a fiber-based sensor for living activities in human body and the results of a laboratory evaluation carried out on it. The authors have developed a device that allows for monitoring the vibrations of human body evoked by living activities—breathing and cardiac rhythm. The device consists of a Bragg grating inscribed into a single mode optical fiber and operating on a wavelength of around 1550 nm. The fiber Bragg grating (FBG) is mounted inside a pneumatic cushion to be placed between the backrest of the seat and the back of the monitored person. Deformations of the cushion, involving deformations of the FBG, are proportional to the vibrations of the body leaning on the cushion. Laboratory studies have shown that the sensor allows for obtaining dynamic strains on the sensing FBG in the range of 50–124 μ strain caused by breathing and approximately 8.3 μstrain induced by heartbeat, which are fully measurable by today's FBG interrogation systems. The maximum relative measurement error of the presented sensor is 12%. The sensor's simple design enables it to be easily implemented in pilot's and driver's seats for monitoring the physiological condition of pilots and drivers. [ABSTRACT FROM PUBLISHER]

Recent advances in wearable technologies integrating multi-modal sensors have enabled the in-field monitoring of several physiological metrics. In sport applications, wearable devices have been widely used to improve performance while minimizing the risk of injuries and illness. The objective of this project is to estimate breathing rate (BR) from respiratory sinus arrhythmia (RSA) using heart rate (HR) recorded with a chest belt during physical activities, yielding additional physiological insight without the need of an additional sensor. Thirty-one healthy adults performed a run at increasing speed until exhaustion on an instrumented treadmill. RR intervals were measured using the Polar H10 HR monitoring system attached to a chest belt. A metabolic measurement system was used as a reference to evaluate the accuracy of the BR estimation. The evaluation of the algorithms consisted of exploring two pre-processing methods (band-pass filters and relative RR intervals transformation) with different instantaneous frequency tracking algorithms (short-term Fourier transform, single frequency tracking, harmonic frequency tracking and peak detection). The two most accurate BR estimations were achieved by combining band-pass filters with short-term Fourier transform, and relative RR intervals transformation with harmonic frequency tracking, showing 5.5% and 7.6% errors, respectively. These two methods were found to provide reasonably accurate BR estimation over a wide range of breathing frequency. Future challenges consist in applying/validating our approaches during in-field endurance running in the context of fatigue assessment. [ABSTRACT FROM AUTHOR]