Search Results (24)

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised alternative—predicting the heart rate a person would reach after completing the step test, using wearable data collected during natural daily activities. Ground truth post-exercise heart rate was obtained through the Queens College Step Test, which is a submaximal protocol widely used in fitness settings. Separately, wearable sensors recorded heart rate (HR), blood oxygen saturation, and motion data during a protocol of lifestyle tasks spanning a range of intensities. Two machine learning models were developed—a Human Activity Recognition (HAR) model that classified daily activities from inertial data with 96.93% accuracy, and a regression model that estimated post step test HR using motion features, physiological trends, and demographic context. The regression model achieved an average root mean squared error (RMSE) of 5.13 beats per minute (bpm) and a mean absolute error (MAE) of 4.37 bpm. These findings demonstrate the potential of test-free methods to estimate standardized test outcomes from daily activity data, offering an accessible pathway to infer cardiorespiratory fitness. Full article

Monitoring heart rate (HR) is vital for health management and athletic performance, and wearable technology enables scientists to obtain real-time cardiovascular insights. This study compares Machine Learning (ML) techniques, including Long Short-Term Memory (LSTM) networks, Physics-Informed Neural Networks (PINNs), and 1D Convolutional Neural Networks (1D CNNs). Then, we develop a hybrid Singular Spectrum Analysis (SSA)-Augmented ML technique to predict HR using wearable sensor data. Additionally, we investigate the impact of incorporating auxiliary physiological inputs, such as breathing rate (BR) and RR intervals, on predictive accuracy. The study utilizes the cardiorespiratory data acquired through wearable sensors while practising sports, including 126 recordings from 81 participants (53 males, 28 females) engaged in 10 different sports. Physiological signals were collected at 1 Hz using the BioHarness 3.0 (Zephyr Technology, Mangaluru, India). The dataset includes individuals with varied levels of sports experience (beginner, intermediate, and advanced), allowing for a more comprehensive evaluation of HR variability across different expertise levels. Our results demonstrate that the hybrid SSA-LSTM model reaches the lowest prediction error by effectively capturing HR dynamics. Furthermore, integrating HR, BR, and RR data significantly enhances accuracy over single or dual parameter inputs. These findings support adopting multivariate machine learning models for health monitoring, improving HR prediction accuracy for fitness and preventive healthcare. Full article

The continuous monitoring of oxygen saturation (SpO₂) and respiratory rates (RRs) are major clinical issues in many cardio-respiratory diseases and have been of tremendous importance during the COVID-19 pandemic. The early detection of hypoxemia was crucial since it precedes significant complications, and SpO₂ follow-up allowed early hospital discharge in patients needing oxygen therapy. Nevertheless, fingertip devices showed some practical limitations. In this study, we investigated the reliability of the new Multisense^® pulse oximetry system compared to a reference pulse oximeter (Vyntus CPX Pulse Oximeter) during hypoxia. In a population of sixteen healthy male subjects (mean age: 31.5 ± 7.0 years, BMI: 24.9 ± 3.6 kg/m², and 35% with darker skin tones), simultaneous SpO₂ and RR measurements were collected over 12.4 h, during which FiO₂ was progressively reduced from 21% to 10.5%. The average root mean square error (ARMS) of SpO₂ for Multisense^® placed on the back and chest was 2.94% and 2.98%, respectively, with permutation testing confirming a significant ARMS below 3.5% for both positions and no statistically significant difference in the ARMS between patch placements. Positive correlations and acceptable accuracy between devices were observed at both locations (r = 0.92, p < 0.001 and r = 0.90, p < 0.001 for back and chest placements, respectively). Bland–Altman analysis further indicated limits of agreement that support consistency across placements, with similar agreement levels noted across skin tones. Similar findings were obtained with the RR measurements. In conclusion, Multisense^® demonstrated robust accuracy in measuring SpO₂ and RRs during hypoxia in humans comparable to standard hospital-grade equipment. The effectiveness of the findings suggests that this wearable device is a valuable tool for the continuous monitoring of SpO₂ and RRs, potentially enhancing patient safety and optimizing hospital resource allocation. Nevertheless, to overcome study limitations and allow generalized use, further work on a larger population sample, including more subjects with a high phototype and desaturation below 80%, would be useful. Full article

A new method based on the Recurrence Quantification Analysis (RQA) of the heart rate (HR) offers an objective, efficient alternative to traditional methods for Aerobic Threshold (AerT) identification that have practical limitations due to the complexity of equipment and interpretation. This study aims to validate the RQA-based method’s applicability across varied demographics, exercise protocols, and health status. Data from 123 cardiopulmonary exercise tests were analyzed, and participants were categorized into four groups: athletes, young athletes, obese individuals, and cardiac patients. Each participant’s AerT was assessed using both traditional ventilatory equivalent methods and the automatic RQA-based method. Ordinary Least Products (OLP) regression analysis revealed strong correlations (r > 0.77) between the RQA-based and traditional methods in both oxygen consumption (VO₂) and HR at the AerT. Mean percentage differences in HR were below 2.5%, and the Technical Error for HR at AerT was under 8%. The study validates the RQA-based method, directly applied to HR time series, as a reliable tool for the automatic detection of the AerT, demonstrating its accuracy across diverse age groups and fitness levels. These findings suggest a versatile, cost-effective, non-invasive, and objective tool for personalized exercise prescription and health risk stratification, thereby fulfilling the study’s goal of broadening the method’s applicability. Full article

Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the need to explore non-invasive alternatives for monitoring vital signs in patients with T2D. Objective: This systematic review is the first that explores the current literature and critically evaluates the use and reporting of non-invasive wearable devices for monitoring vital signs in patients with T2D. Methods: Employing the PRISMA and PICOS guidelines, we conducted a comprehensive search to incorporate evidence from relevant studies, focusing on randomized controlled trials (RCTs), systematic reviews, and meta-analyses published since 2017. Of the 437 publications identified, seven were selected based on predetermined criteria. Results: The seven studies included in this review used various sensing technologies, such as heart rate monitors, accelerometers, and other wearable devices. Primary health outcomes included blood pressure measurements, heart rate, body fat percentage, and cardiorespiratory endurance. Non-invasive wearable devices demonstrated potential for aiding T2D management, albeit with variations in efficacy across studies. Conclusions: Based on the low number of studies with higher evidence levels (i.e., RCTs) that we were able to find and the significant differences in design between these studies, we conclude that further evidence is required to validate the application, efficacy, and real-world impact of these wearable devices. Emphasizing transparency in bias reporting and conducting in-depth research is crucial for fully understanding the implications and benefits of wearable devices in T2D management. Full article

Recent advances in commercially available integrated complex impedance spectroscopy controllers have brought rapid increases in the quality of systems available to researchers for wearable and remote patient monitoring applications. As a result, novel sensing methods and electrode configurations are increasingly viable, particularly for low-power embedded sensors and controllers for general electrochemical analysis. This study evaluates a case study of the four electrode locations suitable for wearable monitoring of respiratory and heart activity monitoring using complex impedance spectroscopy. We use tetrapolar electrode configurations with ten stimulation frequencies to characterize the relative differences in measurement sensitivity. Measurements are performed and compared for the magnitude, phase, resistive, and reactive components of the bioimpedance using two COTS-based controllers, the TI AFE4300 and MAX30009. We identify the highest percent relative changes in the magnitude of the impedance corresponding to deep breathing and heart activity across the chest (17% at 64 kHz, 0.5% at 256 kHz, respectively), on the forearm (0.098% at 16 kHz, 0.04% at 8 kHz), wrist-to-wrist across the body (0.28% at 256 kHz, 0.04% at 256 kHz, respectively), and wrist-to-finger across the body (0.35% at 4 kHz, 0.05% at 4 kHz, respectively). We demonstrate that the wrist-to-wrist and wrist-to-finger configurations are most promising and may enable new wearable bioimpedance applications. Additionally, this paper demonstrates that deep respiration and heart activity influence bioimpedance measurements in whole-body measurement configurations, with variations of nearly 1% in measured impedance due to the phase of the breathing cycle. Full article

Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes’ well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model’s accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors. Full article

In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers’ risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker’s complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy $88.7\pm 7.3\%$ for the model trained with max(HR), std(RR) and std(HR)). Full article

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body’s center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time. Full article

The demand for wearable devices to simultaneously monitor heart rate (HR) and respiratory rate (RR) values has grown due to the incidence increase in cardiovascular and respiratory diseases. The use of inertial measurement unit (IMU) sensors, embedding both accelerometers and gyroscopes, may ensure a non-intrusive and low-cost monitoring. While both accelerometers and gyroscopes have been assessed independently for both HR and RR monitoring, there lacks a comprehensive comparison between them when used simultaneously. In this study, we used both accelerometers and gyroscopes embedded in a single IMU sensor for the simultaneous monitoring of HR and RR. The following main findings emerged: (i) the accelerometer outperformed the gyroscope in terms of accuracy in both HR and RR estimation; (ii) the window length used to estimate HR and RR values influences the accuracy; and (iii) increasing the length over 25 s does not provide a relevant improvement, but accuracy improves when the subject is seated or lying down, and deteriorates in the standing posture. Our study provides a comprehensive comparison between two promising systems, highlighting their potentiality for real-time cardiorespiratory monitoring. Furthermore, we give new insights into the influence of window length and posture on the systems’ performance, which can be useful to spread this approach in clinical settings. Full article

Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient’s condition. This paper presents a ready-to-use and discreet chest band for monitoring the respiratory parameters based on the piezoresistive transduction mechanism. In detail, it relies on a strain sensor realized with a pressure-sensitive fabric (EeonTex LTT-SLPA-20K) for monitoring the chest movements induced by respiration. In addition, the band includes an Inertial Measurement Unit (IMU), which is used to remove the motion artefacts from the acquired signal, thereby improving the measurement reliability. Moreover, the band comprises a low-power conditioning and acquisition section that processes the signal from sensors, providing a reliable measurement of the respiration rate (RR), in addition to other breathing parameters, such as inhalation (TI) and exhalation (TE) times, inhalation-to-exhalation ratio (IER), and flow rate (V). The device wirelessly transmits the extracted parameters to a host device, where a custom mobile application displays them. Different test campaigns were carried out to evaluate the performance of the designed chest band in measuring the RR, by comparing the measurements provided by the chest band with those obtained by breath count. In detail, six users, of different genders, ages, and physical constitutions, were involved in the tests. The obtained results demonstrated the effectiveness of the proposed approach in detecting the RR. The achieved performance was in line with that of other RR monitoring systems based on piezoresistive textiles, but which use more powerful acquisition systems or have low wearability. In particular, the inertia-assisted piezoresistive chest band obtained a Pearson correlation coefficient with respect to the measurements based on breath count of 0.96 when the user was seated. Finally, Bland–Altman analysis demonstrated that the developed system obtained 0.68 Breaths Per Minute (BrPM) mean difference (MD), and Limits of Agreement (LoAs) of +3.20 and −1.75 BrPM when the user was seated. Full article

Wearables are valuable solutions for monitoring a variety of physiological parameters. Their application in cardiorespiratory monitoring may significantly impact global health problems and the economic burden related to cardiovascular and respiratory diseases. Here, we describe a soft biosensor capable of monitoring heart (HR) and respiratory (RR) rates simultaneously. We show that a skin-interfaced biosensor based on fiber optics (i.e., the smart patch) is capable of estimating HR and RR by detecting local ribcage strain caused by breathing and heart beating. The system addresses some of the main technical challenges that limit the wide-scale use of wearables, such as the simultaneous monitoring of HR and RR via single sensing modalities, their limited skin compliance, and low sensitivity. We demonstrate that the smart patch estimates HR and RR with high fidelity under different respiratory conditions and common daily body positions. We highlight the system potentiality of real-time cardiorespiratory monitoring in a broad range of home settings. Full article

COVID-19 containment measures hampered population cardiorespiratory fitness (which can be quantified as peak oxygen consumption ($\stackrel{.}{\mathrm{V}}$O_2peak)) and the possibility to assess it using laboratory-based techniques. Although it is useful to ascertain the $\stackrel{.}{\mathrm{V}}$O_2peak recovery after lockdowns, the community and most scientific institutions were unable to evaluate it. Wearable devices may provide the opportunity to estimate cardiorespiratory fitness outside of the laboratory, without breaking self-isolation; herein, we explore the feasibility of this approach. Fifteen healthy participants were tested every 2 weeks for 10 weeks during a reduction of containment measures after a strict lockdown. Physical activity levels were measured using the International Physical Activity Questionnaire-Short Form (IPAQ-SF). $\stackrel{.}{\mathrm{V}}$O_2peak was estimated through a previously validated test based on the speed of a 60 m sprint run, the baseline-to-peak heart rate (HR) variation, and the velocity of HR decay after the sprint, and measured through a wearable HR monitor. Participants increased physical activity from the end of lockdown (1833 [917–2594] MET-min/week; median [1st quartile–3rd quartile]) until the end of follow-up (2730 [1325–3380] MET-min/week). The estimated $\stackrel{.}{\mathrm{V}}$O_2peak increased by 0.24 ± 0.19 mL/(min*kg*week) (regression coefficient ± standard error). Based on previous knowledge on the impact of inactivity on $\stackrel{.}{\mathrm{V}}$O_2peak, our study indicates that a 10-week period of reducing the stringency of containment measures may not be sufficient to counteract the detrimental effects of the preceding lockdown. Full article

Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects (n = 10) and then conducted a feasibility study on patients (n = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status—the ratio of the resistances at 5 kHz to those at 150 kHz (K)—and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in K (p < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting. Full article