Respiratory Rate Estimation Research Articles

The Effect of Walking on the Estimation of Breathing Pattern Parameters using Wearable Bioimpedance.

Wearable bioimpedance is a technique proposed to estimate breathing parameters such as respiratory rate (RR). However, its potential application lies in clinical investigation of daily-life activities like walking. This study evaluated the effect of the walking interference on the estimation of breathing parameters. 50 chronic obstructive pulmonary disease patients performed static and active measurements during thoracic bioimpedance acquisition. The static measurements included respiratory airflow for reference. The active measurements were used to estimate the walking interference from bioimpedance, and the obtained signals were added to static measurements for comparison with the reference. Afterward, we applied four different preprocessing methods to remove this walking interference and the resulting signals were used to detect the respiratory cycles and estimate breathing parameters (inspiratory time, expiratory time, duty cycle, and RR). The methods performed differently in terms of accuracy and mean average percentage error (MAPE), showing the need for specific preprocessing for active measurements. Furthermore, the MAPE values in the RR estimation were close to 3 % indicating that breathing parameters can be accurately estimated during walking. Accordingly, the present study reinforces the applicability of wearable bioimpedance for respiratory monitoring. Clinical relevance- This study exhibits the suitability of wearable bioimpedance to estimate accurate breathing param-eters during walking activities.

Motion- Based Respiratory Rate Estimation with Motion Artifact Removal Using Video of Face and Upper Body.

Respiratory rate (RR) is a significant indicator of health conditions. Remote contactless measurement of RR is gaining popularity with recent respiratory tract infection awareness. Among various methods of contactless RR measurement, a video of an individual can be used to obtain an instantaneous RR. In this paper, we introduce an RR estimation based on the subtle motion of the head or upper chest captured on an RGB camera. Motion-based respiratory monitoring allows us to acquire RR from individuals with partial face coverings, such as glasses or a face mask. However, motion-based RR estimation is vulnerable to the subject's voluntary movement. In this work, adaptive selection between face and chest regions plus a motion artifact removal technique enables us to obtain a much cleaner respiratory signal from the video recordings. The average mean absolute error (MAE) for controlled and natural breathing is 1.95 BPM using head motion only and 1.28 BPM using chest motion only. Our results demonstrate the possibility of continuous monitoring of breathing rate in real-time with any personal device equipped with an RGB camera, such as a laptop or a smartphone.

Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD.

An ability to extract detailed spirometry-like breathing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.

Estimation of Tidal Volume Using Load Cells on a Hospital Bed.

Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifiable respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respiratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respiratory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low-frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.

Development of a Novel Vital-Signs-Based Infection Screening Composite-Type Camera With Truncus Motion Removal Algorithm to Detect COVID-19 Within 10 Seconds and Its Clinical Validation.

Background: To conduct a rapid preliminary COVID-19 screening prior to polymerase chain reaction (PCR) test under clinical settings, including patient’s body moving conditions in a non-contact manner, we developed a mobile and vital-signs-based infection screening composite-type camera (VISC-Camera) with truncus motion removal algorithm (TMRA) to screen for possibly infected patients. Methods: The VISC-Camera incorporates a stereo depth camera for respiratory rate (RR) determination, a red–green–blue (RGB) camera for heart rate (HR) estimation, and a thermal camera for body temperature (BT) measurement. In addition to the body motion removal algorithm based on the region of interest (ROI) tracking for RR, HR, and BT determination, we adopted TMRA for RR estimation. TMRA is a reduction algorithm of RR count error induced by truncus non-respiratory front-back motion measured using depth-camera-determined neck movement. The VISC-Camera is designed for mobile use and is compact (22 cm × 14 cm × 4 cm), light (800 g), and can be used in continuous operation for over 100 patients with a single battery charge. The VISC-Camera discriminates infected patients from healthy people using a logistic regression algorithm using RR, HR, and BT as explanatory variables. Results are available within 10 s, including imaging and processing time. Clinical testing was conducted on 154 PCR positive COVID-19 inpatients (aged 18–81 years; M/F = 87/67) within the initial 48 h of hospitalization at the First Central Hospital of Mongolia and 147 healthy volunteers (aged 18–85 years, M/F = 70/77). All patients were on treatment with antivirals and had body temperatures <37.5°C. RR measured by visual counting, pulsimeter-determined HR, and BT determined by thermometer were used for references. Result: 10-fold cross-validation revealed 91% sensitivity and 90% specificity with an area under receiver operating characteristic curve of 0.97. The VISC-Camera-determined HR, RR, and BT correlated significantly with those measured using references (RR: r = 0.93, p < 0.001; HR: r = 0.97, p < 0.001; BT: r = 0.72, p < 0.001). Conclusion: Under clinical settings with body motion, the VISC-Camera with TMRA appears promising for the preliminary screening of potential COVID-19 infection for afebrile patients with the possibility of misdiagnosis as asymptomatic.

Estimation of respiratory rate in various environments using microphones embedded in face masks

Wearable health devices and respiratory rates (RRs) have drawn attention to the healthcare domain as it helps healthcare workers monitor patients’ health status continuously and in a non-invasive manner. However, to monitor health status outside healthcare professional settings, the reliability of this wearable device needs to be evaluated in complex environments (i.e., public street and transportation). Therefore, this study proposes a method to estimate RR from breathing sounds recorded by a microphone placed inside three types of masks: surgical, a respirator mask (Korean Filter 94), and reusable masks. The Welch periodogram method was used to estimate the power spectral density of the breathing signals to measure the RR. We evaluated the proposed method by collecting data from 10 healthy participants in four different environments: indoor (office) and outdoor (public street, public bus, and subway). The results obtained errors as low as 0% for accuracy and repeatability in most cases. This research demonstrated that the acoustic-based method could be employed as a wearable device to monitor RR continuously, even outside the hospital environment.

Camera-Augmented Non-Contact Vital Sign Monitoring in Real Time

This study develops a camera-guided frequency-modulated continuous-wave (FMCW) radar to monitor vital signs. A red-green-blue-depth (RGB-D) camera estimates the human torso landmarks and a processing unit constantly adapts the radar beams to the direction of the subjects. To constantly optimize the regions of interest for monitoring respiratory rate (RR) and heart rate (HR), a novel method, coined “singular value-based point detection (SVPD),” is designed. Vital sign extraction is then followed as the last step. Experiments are conducted for the cases of single-subject (10 subjects, 31 scenarios, and 1550 repetitions) and dual-subject monitoring (6 subjects, 6 scenarios, and 90 repetitions). Average (RR, HR) accuracies of (97.68%, 85.88%), (90.02%, 86.05%), (96.71%, 89.50%), and (97.52%, 86.71%) are achieved for the range of distances (0.5-2.5 m), azimuth angles (0°–30°), elevation angles (−30°–+30°), and incident angles (−30°–+30°), respectively. The higher chest and upper abdomen are determined as the optimal regions for RR and HR estimation respectively, with average accuracies of 98.31% and 86.93%. Finally, the capability of dual-subject monitoring at various inter-subject distances (range of 20–70 cm) is confirmed with average accuracies of 92.26% and 73.23% for RR and HR respectively.

Remote investigation and assessment of vital signs (RIA-VS)—proof of concept for contactless estimation of blood pressure, pulse, respiratory rate, and oxygen saturation in patients with suspicion of COVID-19

Background Vital signs are critical in assessing the severity and prognosis of infections, for example, COVID-19, influenza, sepsis, and pneumonia. This study aimed to evaluate a new method for rapid camera-based non-contact measurement of heart rate, blood oxygen saturation, respiratory rate, and blood pressure. Methods Consecutive adult patients attending a hospital emergency department for suspected COVID-19 infection were invited to participate. Vital signs measured with a new camera-based method were compared to the corresponding standard reference methods. The camera device observed the patient's face for 30 s from ∼1 m. Results Between 1 April and 1 October 2020, 214 subjects were included in the trial, 131 female (61%) and 83 male (39%). The mean age was 44 years (range 18–81 years). The new camera-based device’s vital signs measurements were, on average, very close to the gold standard but the random variation was larger than the reference methods. Conclusions The principle of contactless measurement of blood pressure, pulse, respiratory rate, and oxygen saturation works, which is very promising. However, technical improvements to the equipment used in this study to reduce its random variability is required before clinical implementation. This will likely be a game changer once this is sorted out. Clinical trial registration Universal Trial Number (UTN) U1111-1251-4114 and the ClinicalTrials.gov Identifier NCT04383457.

A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications.

Realtime PPG based respiration rate estimation for remote health monitoring applications

Among the many vital parameters of human body, breath rate monitoring is paramount to detect the symptoms of several respiratory diseases such as sleep apnea syndrome, chronic obstructive pulmonary disease (COPD), and asthma. Hence, this paper puts forward a novel framework to measure the respiration rate (RR) using the reflective type photoplethysmogram (PPG) signals acquired using an inexpensive and easy-to-use wearable device. Extracting the respiration induced amplitude variations (RIAV) from PPG signal using the incremental merge segmentation (IMS) algorithm, the proposed approach shows that robust RR estimation in realtime is feasible through low cost Cortex-M4 microcontroller. Using the sliding window approach augmented with adaptive thresholding technique that can deal with motion artifacts, we show that robust RR estimation is viable using reflectance type PPG, which is largely unexplored. Moreover, to handle the non-uniform nature of RIAV signal, this work employs uniform interval interpolation technique and removes the non-respiratory frequencies using finite impulse response (FIR) band-pass filter. Through the fast Fourier transform (FFT) analysis of regular interval RIAV sequence, the dominant frequency corresponding to the RR is extracted. The performance of the proposed scheme is validated not only on two publicly available PPG datasets but also on a custom designed experimental setup integrated with the smartphone. The experimental results highlight that our approach can achieve a RR estimation accuracy of 1 bpm deviation against the ground truth.

Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications

PurposeRespiratory rate can provide auxiliary information on the physiological changes within the human body, such as physical and emotional stress. In a clinical setup, the abnormal respiratory rate can be indicative of the deterioration of the patient's condition. Most of the existing algorithms for the estimation of respiratory rate using photoplethysmography (PPG) are sensitive to external noise and may require the selection of certain algorithm-specific parameters, through the trial-and-error method.MethodsThis paper proposes a new algorithm to estimate the respiratory rate using a photoplethysmography sensor signal for health monitoring. The algorithm is resistant to signal loss and can handle low-quality signals from the sensor. It combines selective windowing, preprocessing and signal conditioning, modified Welch filtering and postprocessing to achieve high accuracy and robustness to noise.ResultsThe Mean Absolute Error and the Root Mean Square Error of the proposed algorithm, with the optimal signal window size, are determined to be 2.05 breaths count per minute and 2.47 breaths count per minute, respectively, when tested on a publicly available dataset. These results present a significant improvement in accuracy over previously reported methods. The proposed algorithm achieved comparable results to the existing algorithms in the literature on the BIDMC dataset (containing data of 53 subjects, each recorded for 8 min) for other signal window sizes.ConclusionThe results endorse that integration of the proposed algorithm to a commercially available pulse oximetry device would expand its functionality from the measurement of oxygen saturation level and heart rate to the continuous measurement of the respiratory rate with good efficiency at home and in a clinical setting.

A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars.

Respiration rate (RR) and respiration patterns (RP) are considered early indicators of physiological conditions and cardiorespiratory diseases. In this study, we addressed the problem of contactless estimation of RR and classification of RP of one person or two persons in a confined space under realistic conditions. We used three impulse radio ultrawideband (IR-UWB) radars and a 3D depth camera (Kinect) to avoid any blind spot in the room and to ensure that at least one of the radars covers the monitored subjects. This article proposes a subject localization and radar selection algorithm using a Kinect camera to allow the measurement of the respiration of multiple people placed at random locations. Several different experiments were conducted to verify the algorithms proposed in this work. The mean absolute error (MAE) between the estimated RR and reference RR of one-subject and two-subjects RR estimation are 0.61±0.53 breaths/min and 0.68±0.24 breaths/min, respectively. A respiratory pattern classification algorithm combining feature-based random forest classifier and pattern discrimination algorithm was developed to classify different respiration patterns including eupnea, Cheyne-Stokes respiration, Kussmaul respiration and apnea. The overall classification accuracy of 90% was achieved on a test dataset. Finally, a real-time system showing RR and RP classification on a graphical user interface (GUI) was implemented for monitoring two subjects.

Movement Compensated Driver’s Respiratory Rate Extraction

In non-contact vital sign monitoring using radar, radar signal distorted by the surrounding unspecified factors is unsuitable for monitoring vital signs. In order to monitor vital signs accurately, it is essential to compensate for distortion of radar signals caused by surrounding environmental factors. In this paper, we propose a driver vital signal compensation method in driving situations, including the driver’s movements using a frequency-modulated continuous-wave (FMCW) radar. Driver’s movement is quantified from the radar signal and used to set a distortion signal compensation index to compensate for the signal distortion induced in the driving situation that the driver’s movement occurs. The experimental results show that the respiration rate estimated from the radar signal compensated through the proposed method is similar to the actual respiration rate than from the signal before calibration. These results confirm the possibility of using the proposed method in a non-statistic situation and effectiveness in estimating respiration rate reflecting human movement in monitoring vital signs using FMCW radar.

Non-contact physiological monitoring of post-operative patients in the intensive care unit

Prolonged non-contact camera-based monitoring in critically ill patients presents unique challenges, but may facilitate safe recovery. A study was designed to evaluate the feasibility of introducing a non-contact video camera monitoring system into an acute clinical setting. We assessed the accuracy and robustness of the video camera-derived estimates of the vital signs against the electronically-recorded reference values in both day and night environments. We demonstrated non-contact monitoring of heart rate and respiratory rate for extended periods of time in 15 post-operative patients. Across day and night, heart rate was estimated for up to 53.2% (103.0 h) of the total valid camera data with a mean absolute error (MAE) of 2.5 beats/min in comparison to two reference sensors. We obtained respiratory rate estimates for 63.1% (119.8 h) of the total valid camera data with a MAE of 2.4 breaths/min against the reference value computed from the chest impedance pneumogram. Non-contact estimates detected relevant changes in the vital-sign values between routine clinical observations. Pivotal respiratory events in a post-operative patient could be identified from the analysis of video-derived respiratory information. Continuous vital-sign monitoring supported by non-contact video camera estimates could be used to track early signs of physiological deterioration during post-operative care.

A Contactless On-Bed Radar System for Human Respiration Monitoring

Healthcare automation technologies have led to develop “smart” home with intelligent sensing systems to fully monitor vital signs. One important healthcare application of the millimeter-wave radar sensor is the respiratory rate (RR) estimation. Accurate and robust measurements of respiration motion and RR estimation in nonhospital facilities is a challenging task. We present a novel contactless on-bed radar system for respiration monitoring for “smart” home. A simplified trunk model and a mode-decomposition-based respiration reconstruction are designed for analysis of 2-D radar profile to extract nonstationary breathing motion information. Resting breathing experiments with different respiratory patterns and human poses are performed to evaluate the performance of the proposed system. The results show that the system measures respiration-induced body movement with a root-mean-squared error of 0.31 mm and a mean accuracy of 89.6%. For RR estimation, the overall accuracy is 97.1% under normal respiratory patterns and 93.4% under abnormal respiratory patterns.

A Blind Filtering Framework for Noisy Neonatal Chest Sounds

Chest sound— as the first and most commonly available vital signal for newborns— contains affluent information about their cardiac and respiratory health. However, neonatal lung sound auscultation is currently challenging and often unreliable due to the noise and interference, particularly for preterm infants. The noise often overlaps with the heart and lung contents in both time and frequency. Moreover, the frequency band of the useful components varies from one case to another, making it difficult to separate by fixed band-pass filtering. In this study, a single-channel Blind Source Separation (SCBSS) framework is proposed to separate newborns’ lung and heart sounds from noisy chest sounds recorded by a digital stethoscope. This method first decomposes the signal into a multi-resolution representation using a time-frequency transform, and then applies source separation algorithms, to find proper ad hoc frequency filters. In the simulation scenario, two different time-frequency transforms are considered; Stationary Wavelet Transform (SWT) with dyadic bases, and Continuous Wavelet Transform (CWT) with redundant bases. The transforms are followed by three different source separation methods, namely Principal Component Analysis (PCA), Periodic Component Analysis (πCA), and Second Order Blind Identification (SOBI). The yielded combinations are applied to the chest sounds recorded from ninety-one preterm and full-term newborns. The results show that compared to raw signals, fixed band-pass filtering and seven other separation methods, the heart and lung sounds extracted by the proposed methods have higher quality index and also result in more reliable heart and respiratory rate estimation.

Detecting Essential Landmarks Directly in Thermal Images for Remote Body Temperature and Respiratory Rate Measurement With a Two-Phase System

Infrared thermographs (IRTs, also called thermal cameras) have been used to remotely measure elevated body temperature (BT) and respiratory rate (RR) during infectious disease outbreaks, such as COVID-19. To facilitate the fast measurement of BT and RR using IRTs in densely populated venues, it is desirable to have IRT algorithms that can automatically identify the best facial locations in thermal images to extract these vital signs. The IEC 80601-2-59:2017 standard suggests that the regions medially adjacent to the inner canthi of the eyes are robust BT measurement sites. The nostril regions, on the other hand, are often used for RR estimation. However, it is more difficult to automatically identify inner canthi and nostrils in thermal images than in visible-light images, which are rich with exploitable features. In this paper, a unique system that can detect inner canthi and outer nostril edges directly in thermal images in two phases is introduced. In Phase I, original thermal images were processed in four different ways to enhance facial features to facilitate inner canthus and nostril detection. In Phase II, landmarks of the inner canthi and outer nostril edges were detected in two steps: (1) face detection using the Single Shot Multibox Detector (SSD) and (2) facial landmark detection to locate the inner canthi and outer nostril edges. The face detection, facial landmark detection, and overall system accuracies were evaluated using the intersection over union, normalized Euclidean distance, and success detection rate metrics on a set of 36 thermal images collected from 12 subjects using three different IRTs. Additional validation was performed on a subset of 40 random thermal images from the publicly available Tufts Face Database. The results revealed that the processed images—referred to as ICLIP images—yielded the highest landmark localization accuracy from the four types of processed thermal images, verifying that the system can automatically and accurately estimate the inner canthus and nostril locations in thermal images. The proposed system can be applied in IRT algorithms to provide reliable temperature measurements and RR estimates during infectious disease outbreaks.

Noncontact Multiphysiological Signals Estimation via Visible and Infrared Facial Features Fusion

Heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) are vital physiological signals that can reveal the physiological and psychological states of human beings. Recent research studies have demonstrated that these physiological signals can be estimated in a non-contact way from visible or infrared facial videos based on remote Photoplethysmography (rPPG). However, most existing methods only use only one data modality and output a single type of physiological signal at a time. To overcome these restrictions, we propose a multi-task framework named the SMP-Net based on multimodal fusion to realize the non-contact multi-physiological signals estimation. First, a joint attention feature fusion (JAFF) module is designed to encode and fuse features from visible and infrared videos. The JAFF module considers different modality-wise, spatial, and channel-wise information on features comprehensively. Then, a task-oriented feature refinement (TOFR) module is developed to extract task-oriented features by refining shared features to improve the estimation performance. Finally, the proposed SMP-Net is validated on the MMVS, VIPL-HR, and UBFC-rPPG datasets and outperforms state-of-the-art methods. Our proposed SMP-Net achieves 1.12 bpm mean absolute error (MAE) for HR estimation, 0.58 ρ for rPPG estimation, and 2.08 MAE for RR estimation on the MMVS dataset. On the VIPL-HR and UBFC-rPPG datasets, the MAE of HR estimation is 2.03 and 0.59 bpm, respectively. The proposed SMP-Net is of great significance for continuous non-contact estimation of multi-physiological signals.

Estimation of Respiratory Nasal Pressure and Flow Rate Signals Using Different Respiratory Sound Features

BackgroundRespiratory sounds are associated with the flow rate, nasal flow pressure, and physical characteristics of airways. In this study, we aimed to develop the flow rate and nasal flow pressure estimation models for the clinical application, and find out the optimal feature set for estimation to achieve the optimal model performance. MethodsRespiratory sounds and flow rate were acquired from nine healthy volunteers. Respiratory sounds and nasal flow pressure were acquired from twenty-three healthy volunteers. Four types of respiratory sound features were extracted for flow rate and nasal flow pressure estimation using different estimation models. Effects of estimations using these features were evaluated using Bland-Altman analysis, estimation error, and respiratory sound feature calculation time. Besides, expiratory and inspiratory phases divided estimation errors were compared with united estimation errors. ResultsThe personalized logarithm model was selected as the optimal flow rate estimation model. Respiratory nasal flow pressure estimation based on this model was also performed. For the four different respiratory sound features, there is no statistically significant difference in flow rate and pressure estimation errors. LogEnvelope was, therefore, chosen as the optimal feature because of the lowest computational cost. In addition, for any type of respiratory sound feature, no statistically significant difference was observed between divided and united estimation errors (flow rate and pressure). ConclusionRespiratory flow rate and nasal flow pressure can be estimated accurately using respiratory sound features. Expiratory and inspiratory phases united estimation using respiratory sounds is a more reasonable estimation method than divided estimation. LogEnvelope can be used for this united respiratory flow rate and nasal flow pressure estimation with minimum computational cost and acceptable estimation error.

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning.

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human’s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors’ knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.