Respiratory Rate Estimation Research Articles

Estimation of heart rate and respiratory rate by fore-background spatiotemporal modeling of videos

Heart rate (HR) and respiratory rate (RR) are two critical physiological parameters that can be estimated from video recordings. However, the accuracy of remote estimation of HR and RR is affected by fluctuations in ambient illumination. To address this adverse effect, we propose a fore-background spatiotemporal (FBST) method for estimating HR and RR from videos captured by consumer-grade cameras. Initially, we identify the foreground regions of interest (ROIs) on the face and chest, as well as the background ROIs in non-body areas of the videos. Subsequently, we construct the foreground and background spatiotemporal maps based on the dichromatic reflectance model. We then introduce a lightweight network equipped with adaptive spatiotemporal layers to process the spatiotemporal maps and automatically generate a feature map of the non-illumination perturbation pulses. This feature map serves as input to a ResNet-18 network to estimate the physiological rhythm. Finally, we extract pulse signals and estimate HR and RR concurrently. Experiments conducted on three public and one private dataset demonstrate the superiority of the proposed FBST method in terms of accuracy and computational efficiency. These findings provide novel insights into non-intrusive human physiological measurements using common devices.

Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device.

Objectives: Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting. We conducted a validation of the automatic RR measurement capability of AcuPebble RE100 (Acurable, London, UK) against a gold-standard capnography system and a type-III cardiorespiratory polygraphy system in two independent prospective and retrospective studies. Methods: The experiment for the prospective study was conducted at Imperial College London. Data from AcuPebble RE100 (Acurable, London, UK) and the reference capnography system (Capnostream™35, Medtronic, Minneapolis, MN, USA) were collected simultaneously from healthy volunteers. The data from a previously published study were used in the retrospective study, where the patients were recruited consecutively from a standard Obstructive Sleep Apnea (OSA) diagnostic pathway in a UK hospital. Overnight data during sleep were collected using the AcuPebble SA100 (Acurable, London, UK) sensor and a type-III cardiorespiratory polygraphy system (Embletta MPR Sleep System, Natus Medical, Pleasanton, CA, USA) at the patients' homes. Data from 15 healthy volunteers were used in the prospective study. For the retrospective study, 150 consecutive patients had been referred for OSA diagnosis and successfully completed the study. Results: The RR output of AcuPebble RE100 (Acurable, London, UK) was compared against the reference device in terms of the Root Mean Squared Deviation (RMSD), mean error, and standard deviation (SD) of the difference between the paired measurements. In both the prospective and retrospective studies, the AcuPebble RE100 algorithms provided accurate RR measurements, well within the clinically relevant margin of error, typically used by FDA-approved respiratory rate monitoring devices, with the RMSD under three breaths per minute (BPM) and mean errors of 1.83 BPM and 1.4 BPM, respectively. Conclusions: The evaluation results provide evidence that AcuPebble RE100 (Acurable, London, UK) algorithms produce reliable results and are hence suitable for overnight monitoring of RR.

Non-contact monitoring of human cardiorespiratory activity during sleep using FMCW millimeter wave radar

Millimeter wave radar can be utilized for non-contact, long-term, and accurate health monitoring. This paper presents an application for vital signs detection including respiratory rate (RR) and heart rate (HR) during sleep, as well as facilitating rapid detection and extended recording of apnea syndrome. This paper employs frequency spectrum analysis for RR estimation and develops an algorithm of template matching estimation (TME) for real-time, accurate HR estimation by comparing radar signal fragments to a template with a frequency signature. The time required for HR estimation is reduced to 1 s. In addition, a neural network-based model was developed for detecting the apnea events with the accuracy of 93.60% and calculates the duration in seconds. The system underwent testing using irregular analog signals of vital signs. In the actual scene testing, the accuracy of the RR estimation is 90.58%, and 90.25% for HR estimation.

Remote Monitoring of Sympathovagal Imbalance During Sleep and Its Implications in Cardiovascular Risk Assessment: A Systematic Review.

Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography.

Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning.

Thermal videos provide a privacy-preserving yet information-rich data source for remote health monitoring, especially for respiration rate (RR) estimation. This paper introduces an end-to-end deep learning approach to RR measurement using thermal video data. A detection transformer (DeTr) first finds the subject's facial region of interest in each thermal frame. A respiratory signal is estimated from a dynamically cropped thermal video using 3D convolutional neural networks and bi-directional long short-term memory stages. To account for the expected phase shift between the respiration measured using a respiratory effort belt vs. a facial video, a novel loss function based on negative maximum cross-correlation and absolute frequency peak difference was introduced. Thermal recordings from 22 subjects, with simultaneous gold standard respiratory effort measurements, were studied while sitting or standing, both with and without a face mask. The RR estimation results showed that our proposed method outperformed existing models, achieving an error of only 1.6 breaths per minute across the four conditions. The proposed method sets a new State-of-the-Art for RR estimation accuracy, while still permitting real-time RR estimation.

Speckle Vibrometry for Contactless Instantaneous Heart Rate and Respiration Rate Monitoring on Mechanically Ventilated Patients.

Objective: Contactless monitoring of instantaneous heart rate and respiration rate has a significant clinical relevance. This work aims to use Speckle Vibrometry (i.e., based on the secondary laser speckle effect) to contactlessly measure these two vital signs in an intensive care unit. Methods: In this work, we propose an algorithm for the estimation of instantaneous heart rate and respiration rate from mechanically ventilated patients. The algorithm uses multiple regions, principal component analysis, and dominant angle analysis. A semi-automated peak detection method is implemented to precisely label the aortic valve opening peak within the cardiac waveform. Results: Compared with electrocardiography, the present work achieves limits of agreement of [-2.19, 1.73] beats per minute of instantaneous heart rate. The measurement spot is on the chest covered with two to three layers of duvet blankets. Compared with the airway flow signal measured by the mechanical ventilator, the present work achieves limits of agreement of [-0.68, 0.46] respirations per minute of instantaneous respiration rate. Conclusions: These results showcased Speckle Vibrometry's potential in vital sign monitoring in a clinical setting. Significance: This is the first human clinical study for Speckle Vibrometry.

A novel computational signal processing framework towards multimodal vital signs extraction using neck-worn wearable devices

Pulse rate (PR) and respiratory rate (RR) are two of the most important vital signs. Monitoring them would benefit from easy-to-use technologies. Hence, wearable devices would, in principle, be ideal candidates for such systems. The neck, although highly susceptible to artifacts, presents an attractive location for a diverse pool of physiological biomarkers monitoring purposes such as airflow sensing in a non-obstructive manner. This paper presents a methodology for PR and RR estimation using photoplethysmography (PPG) and accelerometry (Acc) sensors placed on the neck. Neck PPG and Acc signals were recorded from 22 healthy participants for RR estimation, where the resting subjects performed guided breathing following a visual metronome. Neck PPG signals were obtained from 16 healthy participants who breathed through an altitude generator machine in order to acquire a wider range of PR readings while at rest. The proposed methodology was able to provide rate estimates via a combination of recursive FFT-based dominance scoring coupled with an exponentially weighted moving average (EWMA)-driven aggregation scheme. The recursion aimed at bypassing sudden intra-window amplitude deviations caused by momentary artifacts, while the EWMA-based aggregation was utilized for handling inter-window artifact-induced deviations. To further improve estimation stability and confidence, estimates were calculated in the form of rate bands taking into account the relevant clinically acceptable error margins, and results when considering rate values and rate bands are presented and discussed. The framework was able to achieve an overall pulse rate value accuracy of 93.67±7.64\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.67\\pm 7.64$$\\end{document}% within the clinically acceptable ± 5 BPM with reference to the gold-standard reference devices while providing an overall respiratory rate value accuracy within the clinically appropriate ± 3 BrPM of 94.94±3.56\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$94.94\\pm 3.56$$\\end{document}% with reference to the guiding visual metronome, and 88.4±7.63\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88.4\\pm 7.63$$\\end{document}% with respect to the gold-standard reference device. The proposed methodology achieves acceptable PR and RR estimation capabilities, even when signals are acquired from an unusual location such as the neck. This work introduces novel ideas that can lead to the development of medical device outputs for PR and RR monitoring, especially capitalizing on the advantages of the neck as a multi-modal physiological monitoring location.

PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal.

Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethysmogram (PPG) sensors can monitor breathing. However, current methods have limitations due to manual parameter tuning and pre-defined features. To address this challenge, we propose the PPG2RespNet deep-learning framework. It draws inspiration from the UNet and UNet + + models. It uses three publicly available PPG datasets (VORTAL, BIDMC, Capnobase) to autonomously and efficiently extract respiratory signals. The datasets contain PPG data from different groups, such as intensive care unit patients, pediatric patients, and healthy subjects. Unlike conventional U-Net architectures, PPG2RespNet introduces layered skip connections, establishing hierarchical and dense connections for robust signal extraction. The bottleneck layer of the model is also modified to enhance the extraction of latent features. To evaluate PPG2RespNet's performance, we assessed its ability to reconstruct respiratory signals and estimate respiration rates. The model outperformed other models in signal-to-signal synthesis, achieving exceptional Pearson correlation coefficients (PCCs) with ground truth respiratory signals: 0.94 for BIDMC, 0.95 for VORTAL, and 0.96 for Capnobase. With mean absolute errors (MAE) of 0.69, 0.58, and 0.11 for the respective datasets, the model exhibited remarkable precision in estimating respiration rates. We used regression and Bland-Altman plots to analyze the predictions of the model in comparison to the ground truth. PPG2RespNet can thus obtain high-quality respiratory signals non-invasively, making it a valuable tool for calculating respiration rates.

Methodological Recommendations for the Creation of Sensor Measurement Systems for Respiratory Rate Monitoring Based on Photoplethysmographic Signal Processing

A methodical apparatus for creating sensor measurement systems for monitoring human respiration rate is proposed. It includes a method for estimating respiratory rate based on statistical analysis of photoplethysmographic signals (human pulse wave), a method for selecting priority regions for estimating respiratory rate, and a criterion for determining the required bracelet tension during measurements. The application of the respiratory rate estimation method involves calculating the Correntropy spectral density of the pulse wave signal. A distinctive feature of the method is the use of an algorithm for selecting the priority empirical mode of the Hilbert-Huang decomposition, which is most closely related to the respiratory rate. Experimental verification of the method showed that the mean value of the absolute error for 58.8% of the sample of calculated respiratory rate values did not exceed 1 breath/min, and the 95% confidence interval for the mean absolute error of the entire sample was [0.72–2.2] breaths/min.

Signal Quality-Aware Frequency Demodulation-Based ECG-Derived Respiration Rate Estimation Method With Reduced False Alarms

Signal Quality-Aware Frequency Demodulation-Based ECG-Derived Respiration Rate Estimation Method With Reduced False Alarms

Noise-tolerant NMF-based parallel algorithm for respiratory rate estimation

The accurate estimation of respiratory rate (RR) is crucial for assessing the respiratory system’s health in humans, particularly during auscultation processes. Despite the numerous automated RR estimation approaches proposed in the literature, challenges persist in accurately estimating RR in noisy environments, typical of real-life situations. This becomes especially critical when periodic noise patterns interfere with the target signal. In this study, we present a parallel driver designed to address the challenges of RR estimation in real-world environments, combining multi-core architectures with parallel and high-performance techniques. The proposed system employs a nonnegative matrix factorization (NMF) approach to mitigate the impact of noise interference in the input signal. This NMF approach is guided by pre-trained bases of respiratory sounds and incorporates an orthogonal constraint to enhance accuracy. The proposed solution is tailored for real-time processing on low-power hardware. Experimental results across various scenarios demonstrate promising outcomes in terms of accuracy and computational efficiency.

Leveraging Attention-reinforced UWB Signals to Monitor Respiration during Sleep

The respiration state during overnight sleep is an important indicator of human health. However, existing contactless solutions for sleep respiration monitoring either perform in controlled environments and have low usability in practical scenarios or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events in patients. In this article, we propose Respnea, a non-intrusive sleep respiration monitoring system using an ultra-wideband device. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different subject states. Further, we construct a deep learning model that adopts a multi-head self-attention mechanism and learns the patterns implicit in the respiration signals to distinguish sleep respiration events at a granularity of seconds. To improve the generalization of the model, we propose a contrastive learning strategy to learn a robust representation of the respiration signals. We deploy our system in hospital and home scenarios and conduct experiments on data from healthy subjects and patients with sleep disorders. The experimental results show that Respnea achieves high temporal coverage and low errors (a median error of 0.27 bpm) in respiration rate estimation and reaches an accuracy of 94.44% on diagnosing the severity of sleep apnea-hypopnea syndrome.

SiamQuality: a ConvNet-based foundation model for photoplethysmography signals

Objective. Physiological data are often low quality and thereby compromises the effectiveness of related health monitoring. The primary goal of this study is to develop a robust foundation model that can effectively handle low-quality issue in physiological data. Approach. We introduce SiamQuality, a self-supervised learning approach using convolutional neural networks (CNNs) as the backbone. SiamQuality learns to generate similar representations for both high and low quality photoplethysmography (PPG) signals that originate from similar physiological states. We leveraged a substantial dataset of PPG signals from hospitalized intensive care patients, comprised of over 36 million 30 s PPG pairs. Main results. After pre-training the SiamQuality model, it was fine-tuned and tested on six PPG downstream tasks focusing on cardiovascular monitoring. Notably, in tasks such as respiratory rate estimation and atrial fibrillation detection, the model’s performance exceeded the state-of-the-art by 75% and 5%, respectively. The results highlight the effectiveness of our model across all evaluated tasks, demonstrating significant improvements, especially in applications for heart monitoring on wearable devices. Significance. This study underscores the potential of CNNs as a robust backbone for foundation models tailored to physiological data, emphasizing their capability to maintain performance despite variations in data quality. The success of the SiamQuality model in handling real-world, variable-quality data opens new avenues for the development of more reliable and efficient healthcare monitoring technologies.

An efficient model for extracting respiratory and blood oxygen saturation data from photoplethysmogram signals by removing motion artifacts using heuristic-aided ensemble learning model

Patients with surgical, pulmonary, and cardiac problems, continual monitoring of Oxygen Saturation of a Person (SpO2) and Respiratory Rate (RR) is essential. Similarly, the persons with cardiopulmonary health issues, RR estimation is crucial. The performance of the ventilator assistance and lung medicines are evaluated using SpO2 and RR. For the persons, those who are living alone with respiratory illnesses need a compulsory estimation of RR. In case of serious illness, the RR might face abrupt changes. The immobility of the disturbance and RR makes the RR evaluation from the PhotoPlethysmoGraphic (PPG) signals is a difficult challenge. So, an efficient RR and SpO2 estimation framework from the PPG signal using the deep learning method is developed in this paper. At first, the PPG signal is collected from standard data sources. The collected PPG signals undergo signal pre-processing. The pre-processing procedures include Motion Artifacts (MA) removal and filtering techniques. The pre-processed signals are split into distinct windows. From the split windows of the signals, the spectral features, RR, and Respiratory Peak Variance (RPV) features are extracted. The retrieved features are selected optimally with the help of Advanced Golden Tortoise Beetle Optimizer (AGTBO). The weights are chosen optimally with the same AGTBO. The optimally selected features are fused with the optimal features to get the weighted optimal features. These weighted optimal features are fed into the Ensemble Learning-based RR and SpO2 Estimation Network (ELRR-SpO2EN). The ensemble learning model is developed by combining Multilayer Perceptron (MLP), AdaBoost, and Attention-based Long Short Term Memory (A-LSTM). The performance of the developed RR and SpO2 estimation model is compared with other existing techniques. The experimental analysis results revealed that the proposed AGTBO-ELRR-SpO2EN model attained 96 % accuracy for the second dataset, which is higher than the conventional models such as MLP (90 %), Adaboost (92 %), A-LSTM (92 %), and MLP-ADA-ALSTM (94 %). Thus, it has been confirmed that the designed RR and SpO2 estimation framework from PPG signals is more efficient than the other conventional models.

Prediction of Distributed River Sediment Respiration Rates Using Community‐Generated Data and Machine Learning

AbstractRiver sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi‐scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature‐rich (i.e., 100+ possible input variables) data set. Here, we present results from a two‐tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud‐based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger‐scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger‐scale features to generate data‐driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep.

This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

Continuous Respiratory Rate Estimation Approach Based on Phase Features Using Array Radar in Home Sleeping Monitoring

Continuous Respiratory Rate Estimation Approach Based on Phase Features Using Array Radar in Home Sleeping Monitoring

Three methods for determining the respiratory waves from ECG (Part II)

This paper presents the results of a study of three methods for estimating the respiratory wave (RW) and respiratory rate (RR) using the electrocardiogram (ECG). There were applied methods from different groups: amplitude modulation ECG-Derived Respiration (EDR), frequency modulation Respiratory Sinus Arrhythmia (RSA) and Baseline Wander (BW) processing with the Savitzky–Golay filter (S–G). The theoretical aspects of the methods were presented in the Part 1 of the publication which was entitled: “Three Methods for the Determination of the Respiratory Waves from ECG Part I”. RR parameter estimation was performed for all the three methods for 12 subjects. The research concerning the influence of the parameters: Body Mass Index (BMI), Tidal Volume (TV) -, Forced Expiratory Volume in 1 second (FEV1) and – Forced Vital Capacity (FVC) on the errors of the estimated parameter RR. Moreover, all 12 signals, which were acquired with the help of a 12-lead Holter ECG were taken into consideration. The results indicate a preliminary dependence of respiratory parameters and BMI on the Respiratory Wave and, further, on the RR estimation errors. Consequently, the type of method and ECG Holter leads depend on the BMI and respiratory parameters. Studies with larger numbers of objects to definitively confirm these relationships are planned. In addition, an optimal selection of S–G filter parameters was carried out. Finally, a proprietary reference embedded system for recording RW and calculating RR was demonstrated.

Respiratory Rate Estimation on Embedded System

We present the design, implementation, and results of an algorithm for respiratory rate estimation using respiratory-induced frequency, intensity, and amplitude variation calculated from the infrared (IR) channel of the SEN-15219 board for photoplethysmography (PPG) acquisition. First, the algorithm was developed in Python (on a PC) using synthetic signals and publicly available respiration and PPG data. We also include a graphical user interface to process data from sensors and display vital signs. Later, we ported the algorithm to an MSP432P401R microcontroller to complete our wearable prototype. Results are promissory and show that respiratory rate estimation can be performed on the selected platform with our proposed Fourier Product (FP) method, which results in a Mean Absolute Error of 4.1 using 16-seconds windows of IR-PPG signals.