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Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up. [ABSTRACT FROM AUTHOR]

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of −0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring. [ABSTRACT FROM AUTHOR]

In this paper, we present the design and fabrication of a novel chip-on-tip catheter, which uses a microcamera and optical fibres to capture in vivo images in a beating porcine heart thanks to a saline flush to clear the blood field. Here, we demonstrate the medical utility and mechanical robustness of this catheter platform system, which could be used for other optical diagnostic techniques, surgical guidance, and clinical navigation. We also discuss some of the challenges and system requirements associated with developing a miniature prototype for such a study and present assembly instructions. Methods of clearing the blood field are discussed, including an integrated flush channel at the distal end. This permits the capture of images of the endocardial walls. The device was navigated under fluoroscopic guiding, through a guiding catheter to various locations of the heart, where images were successfully acquired. Images were captured at the intra-atrial septum, in the left atrium after a trans-septal cross procedure, and in the left ventricle, which are, to the best of our knowledge, the first images captured in an in vivo beating heart using endoscopic techniques. [ABSTRACT FROM AUTHOR]

Mental distress-induced imbalances in autonomic nervous system activities adversely affect the electrical stability of the cardiac system, with heart rate variability (HRV) identified as a related indicator. Traditional HRV measurements use electrocardiography (ECG), but impulse radio ultra-wideband (IR-UWB) radar has shown potential in HRV measurement, although it is rarely applied to psychological studies. This study aimed to assess early high levels of mental distress using HRV indices obtained using radar through modified signal processing tailored to reduce phase noise and improve positional accuracy. We conducted 120 evaluations on 15 office workers from a software startup, with each 5 min evaluation using both radar and ECG. Visual analog scale (VAS) scores were collected to assess mental distress, with evaluations scoring 7.5 or higher classified as high-mental distress group, while the remainder formed the control group. Evaluations indicating high levels of mental distress showed significantly lower HRV compared to the control group, with radar-derived indices correlating strongly with ECG results. The radar-based analysis demonstrated a significant ability to differentiate high mental distress, supported by receiver operating characteristic (ROC) analysis. These findings suggest that IR-UWB radar could be a supportive tool for distinguishing high levels of mental stress, offering clinicians complementary diagnostic insights. [ABSTRACT FROM AUTHOR]

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases. [ABSTRACT FROM AUTHOR]

Global stress is widespread in today's post-pandemic world of political and economic uncertainty. Vibroacoustic technology is a vibrotactile intervention with multiple uses, but its impact on stress lacks interpretation. This research assessed if the vibroacoustic technology of a Vibroacoustic Sound Massage (VSM) can reduce psychological, physiological, and cognitive stress. The Perceived Stress Scale (PSS-10) and electrocardiogram (ECG) and electroencephalogram (EEG) biosignals were used to quantify results. Participants were divided into Low-Stress and High-Stress groups. The ECG results show VSM increased parasympathetic activity for all participants, with the Low-Stress group being more affected. The EEG results indicate increased concentration, reduced arousal, and increased relaxation, with participant well-being non-significantly affected, though variability in this metric was homogenised after VSM. Together, these results validate VSM as an effective support tool for stress management; however, further research is required. [ABSTRACT FROM AUTHOR]

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads. [ABSTRACT FROM AUTHOR]

Low-cost, portable devices capable of accurate physiological measurements are attractive tools for coaches, athletes, and practitioners. The purpose of this study was primarily to establish the validity and reliability of Movesense HR+ ECG measurements compared to the criterion three-lead ECG, and secondarily, to test the industry leader Garmin HRM. Twenty-one healthy adults participated in running and cycling incremental test protocols to exhaustion, both with rest before and after. Movesense HR+ demonstrated consistent and accurate R-peak detection, with an overall sensitivity of 99.7% and precision of 99.6% compared to the criterion; Garmin HRM sensitivity and precision were 84.7% and 87.7%, respectively. Bland–Altman analysis compared to the criterion indicated mean differences (SD) in RR' intervals of 0.23 (22.3) ms for Movesense HR+ at rest and 0.38 (18.7) ms during the incremental test. The mean difference for Garmin HRM-Pro at rest was −8.5 (111.5) ms and 27.7 (128.7) ms for the incremental test. The incremental test correlation was very strong (r = 0.98) between Movesense HR+ and criterion, and moderate (r = 0.66) for Garmin HRM-Pro. This study developed a robust peak detection algorithm and data collection protocol for Movesense HR+ and established its validity and reliability for ECG measurement. [ABSTRACT FROM AUTHOR]

Assessing the olfactory preferences of consumers is an important aspect of fragrance product development and marketing. With the advancement of wearable device technologies, physiological signals hold great potential for evaluating olfactory preferences. However, there is currently a lack of relevant studies and specific explanatory procedures for preference assessment methods that are based on physiological signals. In response to this gap, a synchronous data acquisition system was established using the ErgoLAB multi-channel physiology instrument and olfactory experience tester. Thirty-three participants were recruited for the olfactory preference experiments, and three types of autonomic response data (skin conductance, respiration, and heart rate) were collected. The results of both individual and overall analyses indicated that olfactory preferences can lead to changes in skin conductance (SC), respiration (RESP), and heart rate (HR). The trends of change in both RESP and HR showed significant differences (with the HR being more easily distinguishable), while the SC did not exhibit significant differences across different olfactory perception preferences. Additionally, gender differences did not result in significant variations. Therefore, HR is more suitable for evaluating olfactory perception preferences, followed by RESP, while SC shows the least effect. Moreover, a logistic regression model with a high accuracy (84.1%) in predicting olfactory perception preferences was developed using the changes in the RESP and HR features. This study has significant implications for advancing the assessment of consumer olfactory preferences. [ABSTRACT FROM AUTHOR]

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system. [ABSTRACT FROM AUTHOR]

Oxygen uptake ( V ˙ O 2 ) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate ( H R ) is a prominent physiological indicator correlated with V ˙ O 2 and is often used for indirect V ˙ O 2 prediction. This study investigates the impact of H R placement on V ˙ O 2 prediction accuracy by analyzing H R data combined with the respiratory rate ( R E S P ) and minute ventilation ( V ˙ E ) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at various intensities. Data on V ˙ O 2 , R E S P , V ˙ E , and H R were collected and used to develop a neural network model for V ˙ O 2 prediction. The influence of H R position on prediction accuracy was assessed via Bland–Altman plots, and model performance was evaluated by mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE). Our findings indicate that H R combined with R E S P and V ˙ E ( V ˙ O 2 H R + R E S P + V ˙ E ) produces the most accurate V ˙ O 2 predictions (MAE: 165 mL/min, R2: 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of V ˙ O 2 prediction decreases, particularly within high-intensity exercise. The substitution of H R with different anatomical sites significantly impacts V ˙ O 2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering H R placement in V ˙ O 2 prediction models, with R E S P and V ˙ E serving as effective compensatory factors. These findings contribute to refining indirect V ˙ O 2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements. [ABSTRACT FROM AUTHOR]

The rising incidence of type 2 diabetes underscores the need for technological innovations aimed at enhancing diabetes management by aiding individuals in monitoring their dietary intake. This has resulted in the development of technologies capable of tracking the timing and content of an individual's meals. However, the ability to use non-invasive wearables to estimate or classify the carbohydrate content of the food an individual has just consumed remains a relatively unexplored area. This study investigates carbohydrate content classification using postprandial heart rate responses from non-invasive wearables. We designed and developed timeStampr, an iOS application for collecting timestamps essential for data labeling and establishing ground truth. We then conducted a pilot study in controlled, yet naturalistic settings. Data were collected from 23 participants using an Empatica E4 device worn on the upper arm, while each participant consumed either low-carbohydrate or carbohydrate-rich foods. Due to sensor irregularities with dark skin tones and non-compliance with the study's health criteria, we excluded data from three participants. Finally, we configured and trained a Light Gradient Boosting Machine (LGBM) model for carbohydrate content classification. Our classifiers demonstrated robust performance, with the carbohydrate content classification model consistently achieving at least 84% in accuracy, precision, recall, and AUCROC within a 60 s window. The results of this study demonstrate the potential of postprandial heart rate responses from non-invasive wearables in carbohydrate content classification. [ABSTRACT FROM AUTHOR]

Heart Rate Variability (HRV) refers to the capability of the heart rhythm to vary at different times, typically reflecting the regulation of the heart by the autonomic nervous system. In recent years, with advancements in Electrocardiogram (ECG) signal processing technology, HRV features reflect various aspects of cardiac activity, such as variability in heart rate, cardiac health status, and responses. We extracted key features of HRV and used them to develop and evaluate an automatic recognition model for cardiac diseases. Consequently, we proposed the HRV Heart Disease Recognition (HHDR) method, employing the Spectral Magnitude Quantification (SMQ) technique for feature extraction. Firstly, the HRV signals are extracted through electrocardiogram signal processing. Then, by analyzing parts of the HRV signal within various frequency ranges, the SMQ method extracts rich features of partial information. Finally, the Random Forest (RF) classification computational method is employed to classify the extracted information, achieving efficient and accurate cardiac disease recognition. Experimental results indicate that this method surpasses current technologies in recognizing cardiac diseases, with an average accuracy rate of 95.1% for normal/diseased classification, and an average accuracy of 84.8% in classifying five different disease categories. Thus, the proposed HHDR method effectively utilizes the local information of HRV signals for efficient and accurate cardiac disease recognition, providing strong support for cardiac disease research in the medical field. [ABSTRACT FROM AUTHOR]

Smartwatches are one of the most relevant fitness trends of the past two decades, and they collect increasing amounts of health and movement data. The accuracy of these data may be questionable and requires further investigation. Therefore, the aim of the present study is to validate smartwatches for use in triathlon training. Ten different smartwatches were tested for accuracy in measuring heart rates, distances (via global navigation satellite systems, GNSSs), swim stroke rates and the number of swim laps in a 50 m Olympic-size pool. The optical heart rate measurement function of each smartwatch was compared to that of a chest strap. Thirty participants (15 females, 15 males) ran five 3 min intervals on a motorised treadmill to evaluate the accuracy of the heart rate measurements. Moreover, for each smartwatch, running and cycling distance tracking was tested over six runs of 4000 m on a 400 m tartan stadium track, six hilly outdoor runs over 3.4 km, and four repetitions of a 36.8 km road bike course, respectively. Three swimming protocols ranging from 200 m to 400 m were performed in triplicate in a 50 m Olympic-size pool, evaluating the tracked distance and the detected number of strokes. The mean absolute percentage errors (MAPEs) for the average heart rate measurements varied between 3.1% and 8.3%, with the coefficient of determination ranging from 0.22 to 0.79. MAPE results ranged from 0.8% to 12.1% for the 4000 m run on the 400 m track, from 0.2% to 7.5% for the 3.4 km outdoor run, and from 0.0% to 4.2% for the 36.8 km bike ride. For the swimming tests, in contrast, the deviations from the true distance varied greatly, starting at a 0.0% MAPE for the 400 m freestyle and reaching 91.7% for the 200 m medley with style changes every 25 m. In summary, for some of the smartwatches, the measurement results deviated substantially from the true values. Measurements taken while road cycling over longer distances with only a few curves were in relative terms more accurate than those taken during outdoor runs and even more accurate than those taken on the 400 m track. In the swimming exercises, the accuracy of the measured distances was severely deteriorated by the medley changes among the majority of the smartwatches. Altogether, the results of this study should help in assessing the accuracy and thus the suitability of smartwatches for general triathlon training. [ABSTRACT FROM AUTHOR]

Non-invasive continuous health monitoring has become feasible with the advancement of biosensors. While monitoring certain biomarkers such as heart rate or skin temperature are now at a certain maturity, monitoring molecular biomarkers is still challenging. Progress has been shown in sampling, measurement, and interpretation of data toward non-invasive molecular sensors that can be integrated into daily wearable items. Toward this goal, this paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. In controlled experiments, we have shown the variation of glucose concentration in water can be wirelessly detected by tracking the resonant frequency of the embroidered sensors. The current sensors operate at 1.8 GHz to 2 GHz and respond to the change in glucose concentration with a sensitivity of 0.17 MHz/(mg/dL). The embroidered IDT-based sensors with wireless sensing will be a new measurement modality for molecular wearable sensors. The establishment of a wireless sensing mechanism for embroidered IDT-based sensors will be followed by an investigation of sweat for molecular detection. This will require adding functionalities for sampling and interpretation of acquired data. We envisage the embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture. [ABSTRACT FROM AUTHOR]

The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA-a1) of heart rate variability (HRV) has been shown to be a sensitive marker for assessing global organismic demands. The wide dynamic range within the exercise intensity spectrum and the relationship to established physiologic threshold boundaries potentially allow in-field use and also open opportunities to provide real-time feedback. The present study expands the idea of using everyday workout data from the AI Endurance app to obtain the relationship between cycling power and DFA-a1. Collected data were imported between September 2021 and August 2023 with an initial pool of 3123 workouts across 21 male users. The aim of this analysis was to further apply a new method of implementing workout group data considering representative values of DFA-a1 segmentation compared to single workout data and including all data points to enhance the validity of the internal-to-external load relationship. The present data demonstrate a universal relationship between cycling power and DFA-a1 from everyday workout data that potentially allows accessible and regular tracking of intensity zone demarcation information. The analysis highlights the superior efficacy of the representative-based approach of included data in most cases. Validation data of the performance level and the up-to-date relationship are still pending. [ABSTRACT FROM AUTHOR]

Long-term patient monitoring is required for detection of episodes of atrial fibrillation, one of the most widespread cardiac pathologies. Today, the most used non-invasive technique is Holter electrocardiographic (ECG) monitoring, which can often prove ineffective because of the short duration of recordings (e.g., one day). Other techniques such as photo-plethysmography are adopted by smartwatches for much longer duration monitoring, but this has the disadvantage of offering only intermittent measurements. This study proposes an Internet of Things (IoT) sensor that can provide a very long period of continuous monitoring. The sensor consists of an ECG-integrated Analog Front End (MAX30003), a microcontroller (STM32F401RE), and an IoT narrowband module (STEVAL-STMODLTE). The instantaneous heart rate is extracted from the ECG recording in real time. At intervals of two minutes, the sequence of inter-beat intervals is transmitted to an IoT cloud platform (ThingSpeak). Settled atrial fibrillation event recognition software runs on the cloud and generates alerts when it recognizes such arrhythmia. Performances of the proposed sensor were evaluated by generating analog ECG signals from a public dataset of ECG signals with atrial fibrillation episodes, the MIT-BIH Atrial Fibrillation Database, each recording lasting approximately 10 h. Software implementing the Lorentz algorithm, one of the best detectors of atrial fibrillation, was implemented on the cloud platform. The accuracy, sensitivity, and specificity in recognizing atrial fibrillation episodes of the proposed system was calculated by comparison with a cardiologist's reference data. Across all patients, the proposed method achieved an accuracy of 0.88, a sensitivity 0.71, and a specificity 0.99. The results obtained suggest that the developed system can continuously record and transmit heart rhythms effectively and efficiently and, in addition, offers considerable performance in recognizing atrial fibrillation episodes in real time. [ABSTRACT FROM AUTHOR]

Introduction: Concussion is known to cause transient autonomic and cerebrovascular dysregulation that generally recovers; however, few studies have focused on individuals with an extensive concussion history. Method: The case was a 26-year-old male with a history of 10 concussions, diagnosed for bipolar type II disorder, mild attention-deficit hyperactivity disorder, and a history of migraines/headaches. The case was medicated with Valproic Acid and Escitalopram. Sensor-based baseline data were collected within six months of his injury and on days 1–5, 10, and 14 post-injury. Symptom reporting, heart rate variability (HRV), neurovascular coupling (NVC), and dynamic cerebral autoregulation (dCA) assessments were completed using numerous biomedical devices (i.e., transcranial Doppler ultrasound, 3-lead electrocardiography, finger photoplethysmography). Results: Total symptom and symptom severity scores were higher for the first-week post-injury, with physical and emotional symptoms being the most impacted. The NVC response showed lowered activation in the first three days post-injury, while autonomic (HRV) and autoregulation (dCA) were impaired across all testing visits occurring in the first 14 days following his concussion. Conclusions: Despite symptom resolution, the case demonstrated ongoing autonomic and autoregulatory dysfunction. Larger samples examining individuals with an extensive history of concussion are warranted to understand the chronic physiological changes that occur following cumulative concussions through biosensing devices. [ABSTRACT FROM AUTHOR]

Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping. [ABSTRACT FROM AUTHOR]

Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann–Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers' mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection. [ABSTRACT FROM AUTHOR]

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method. [ABSTRACT FROM AUTHOR]

(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the levels of anxiety and depression among college students. Additionally, the study assesses the accuracy of a multilayer perceptron-based HRV analysis in predicting these emotional states. (2) Methods: A total of 845 healthy college students, aged between 18 and 22, participated in the study. Participants completed self-assessment scales for anxiety and depression (SAS and PHQ-9). HRV data were collected during exercise and for a 5-min period post-exercise. The multilayer perceptron neural network model, which included several branches with identical configurations, was employed for data processing. (3) Results: Through a 5-fold cross-validation approach, the average accuracy of HRV in predicting anxiety levels was 89.3% for no anxiety, 83.6% for mild anxiety, and 74.9% for moderate to severe anxiety. For depression levels, the average accuracy was 90.1% for no depression, 84.2% for mild depression, and 82.1% for moderate to severe depression. The predictive R-squared values for anxiety and depression scores were 0.62 and 0.41, respectively. (4) Conclusions: The study demonstrated that HRV during exercise and recovery in college students can effectively predict levels of anxiety and depression. However, the accuracy of score prediction requires further improvement. HRV related to exercise can serve as a non-invasive biomarker for assessing psychological health. [ABSTRACT FROM AUTHOR]

Manual wheelchair users (MWUs) are prone to a sedentary life that can negatively affect their physical and cardiovascular health, making regular assessment important to identify appropriate interventions and lifestyle modifications. One mean of assessing MWUs' physical health is the 6 min push test (6MPT), where the user propels themselves as far as they can in six minutes. However, reliance on observer input introduces subjectivity, while limited quantitative data inhibit comprehensive assessment. Incorporating sensors into the 6MPT can address these limitations. Here, ten MWUs performed the 6MPT with additional sensors: two inertial measurement units (IMUs)—one on the wheelchair and one on the wrist together with a heart rate wristwatch. The conventional measurements of distance and laps were recorded by the observer, and the IMU data were used to calculate laps, distance, speed, and cadence. The results demonstrated that the IMU can provide the metrics of the traditional 6MPT with strong significant correlations between calculated laps and observer lap counts (r = 0.947, p < 0.001) and distances (r = 0.970, p < 0.001). Moreover, heart rate during the final minute was significantly correlated with calculated distance (r = 0.762, p = 0.017). Enhanced 6MPT assessment can provide objective, quantitative, and comprehensive data for clinicians to effectively inform interventions in rehabilitation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Low physical activity (PA) measured by accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used, and some devices also include an accelerometer. The objective of this study was to evaluate whether PA or HRV measured from long-term ECG monitors was associated with cognitive function among older adults. A total of 1590 ARIC participants had free-living PA and HRV measured over 14 days using the Zio® XT Patch [aged 72–94 years, 58% female, 32% Black]. Cognitive function was measured by cognitive factor scores and adjudicated dementia or mild cognitive impairment (MCI) status. Adjusted linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Each 1-unit increase in the total amount of PA was associated with higher global cognition (β = 0.30, 95% CI: 0.16–0.44) and executive function scores (β = 0.38, 95% CI: 0.22–0.53) and lower odds of MCI (OR = 0.38, 95% CI: 0.22–0.67) or dementia (OR = 0.25, 95% CI: 0.08–0.74). HRV (i.e., SDNN and rMSSD) was not associated with cognitive function. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) is related to cardiac vagal control and emotional regulation and an index for cardiac vagal control and cardiac autonomic activity. This study aimed to develop the Taiwan HRV normative database covering individuals aged 20 to 70 years and to assess its diagnosing validity in patients with major depressive disorder (MDD). A total of 311 healthy participants were in the HRV normative database and divided into five groups in 10-year age groups, and then the means and standard deviations of the HRV indices were calculated. We recruited 272 patients with MDD for cross-validation, compared their HRV indices with the normative database, and then converted them to Z-scores to explore the deviation of HRV in MDD patients from healthy groups. The results found a gradual decline in HRV indices with advancing age in the HC group, and females in the HC group exhibit higher cardiac vagal control and parasympathetic activity than males. Conversely, patients in the MDD group demonstrate lower HRV indices than those in the HC group, with their symptoms of depression and anxiety showing a negative correlation with HRV indices. The Taiwan HRV normative database has good psychometric characteristics of cross-validation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the recording of movement kinematics and interaction forces. Such measurements are commonly employed in clinics with the aim of assessing patients' pathologies, but so far some of them have found full exploitation mainly for research purposes. In fact, even though the data they allow to gather may shed light on physiopathology and mechanisms underlying motor recovery in rehabilitation, their practical use in the clinical environment is mainly devoted to research studies, with a very reduced impact on clinical practice. This is especially the case for muscle synergies, a well-known method for the evaluation of motor control in neuroscience based on multichannel EMG recordings. In this paper, considering neuromotor rehabilitation as one of the most important scenarios for exploiting novel methods to assess motor control, the main challenges and future perspectives for the standard clinical adoption of muscle synergy analysis are reported and critically discussed. [ABSTRACT FROM AUTHOR]

The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates' determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman's coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman's coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables. [ABSTRACT FROM AUTHOR]

Beach variants of popular sports like soccer and handball have grown in participation over the last decade. However, the characterization of the workload demands in beach sports remains limited compared to their indoor equivalents. This systematic review aimed to: (1) characterize internal and external loads during beach invasion sports match-play; (2) identify technologies and metrics used for monitoring; (3) compare the demands of indoor sports; and (4) explore differences by competition level, age, sex, and beach sport. Fifteen studies ultimately met the inclusion criteria. The locomotive volumes averaged 929 ± 269 m (average) and 16.5 ± 3.3 km/h (peak) alongside 368 ± 103 accelerations and 8 ± 4 jumps per session. The impacts approached 700 per session. The heart rates reached 166–192 beats per minute (maximal) eliciting 60–95% intensity. The player load was 12.5 ± 2.9 to 125 ± 30 units. Males showed 10–15% higher external but equivalent internal loads versus females. Earlier studies relied solely on a time–motion analysis, while recent works integrate electronic performance and tracking systems, enabling a more holistic quantification. However, substantial metric intensity zone variability persists. Beach sports entail intermittent high-intensity activity with a lower-intensity recovery. Unstable surface likely explains the heightened internal strain despite moderately lower running volumes than indoor sports. The continued integration of technology together with the standardization of workload intensity zones is needed to inform a beach-specific training prescription. [ABSTRACT FROM AUTHOR]

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches. [ABSTRACT FROM AUTHOR]

We assessed the feasibility of implementing a virtually guided Neuromuscular Electrical Stimulation (NMES) protocol over the tibialis anterior (TA) muscle while collecting heart rate (HR), Numeric Pain Rating Scale (NPRS), and quality of contraction (QoC) data. We investigated if HR, NPRS, and QoC differ ON and OFF the TA motor point and explored potential relationships between heart rate variability (HRV) and the NPRS. Twelve healthy adults participated in this cross-sectional study. Three NMES trials were delivered ON and OFF the TA motor point. HR, QoC, and NPRS data were collected. There was no significant difference in HRV ON and OFF the motor point (p > 0.05). The NPRS was significantly greater OFF the motor point (p < 0.05). The QoC was significantly different between motor point configurations (p < 0.05). There was no correlation between the NPRS and HRV (p > 0.05, r = −0.129). We recommend non-electrical methods of measuring muscle activity for future studies. The NPRS and QoC can be administered virtually. Time-domain HRV measures could increase the validity of the protocol. The variables should be explored further virtually to enhance the protocol before eventual ICU studies. [ABSTRACT FROM AUTHOR]

Nocturnal enuresis (NE) is involuntary bedwetting during sleep, typically appearing in young children. Despite the potential benefits of the long-term home monitoring of NE patients for research and treatment enhancement, this area remains underexplored. To address this, we propose NEcare, an in-home monitoring system that utilizes wearable devices and machine learning techniques. NEcare collects sensor data from an electrocardiogram, body impedance (BI), a three-axis accelerometer, and a three-axis gyroscope to examine bladder volume (BV), heart rate (HR), and periodic limb movements in sleep (PLMS). Additionally, it analyzes the collected NE patient data and supports NE moment estimation using heuristic rules and deep learning techniques. To demonstrate the feasibility of in-home monitoring for NE patients using our wearable system, we used our datasets from 30 in-hospital patients and 4 in-home patients. The results show that NEcare captures expected trends associated with NE occurrences, including BV increase, HR increase, and PLMS appearance. In addition, we studied the machine learning-based NE moment estimation, which could help relieve the burdens of NE patients and their families. Finally, we address the limitations and outline future research directions for the development of wearable systems for NE patients [ABSTRACT FROM AUTHOR]

Basketball involves frequent high-intensity movements requiring optimal aerobic power. Altitude training can enhance physiological adaptations, but research examining its effects in basketball is limited. This study aimed to characterize the internal/external workload of professional basketball players during preseason and evaluate the effects of altitude and playing position. Twelve top-tier professional male basketball players (Liga Endesa, ACB; guards: n = 3, forwards: n = 5, and centers: n = 4) participated in a crossover study design composed of two training camps with nine sessions over 6 days under two different conditions: high altitude (2320 m) and sea level (10 m). Internal loads (heart rate, %HRMAX) and external loads (total distances covered across speed thresholds, accelerations/decelerations, impacts, and jumps) were quantified via wearable tracking and heart rate telemetry. Repeated-measures MANOVA tested the altitude x playing position effects. Altitude increased the total distance (+10%), lower-speed running distances (+10–39%), accelerations/decelerations (+25–30%), average heart rate (+6%), time in higher-intensity HR zones (+23–63%), and jumps (+13%) across all positions (p < 0.05). Positional differences existed, with guards accruing more high-speed running and centers exhibiting greater cardiovascular demands (p < 0.05). In conclusion, a 6-day altitude block effectively overloads training, providing a stimulus to enhance fitness capacities when structured appropriately. Monitoring workloads and individualizing training by playing position are important when implementing altitude training, given the varied responses. [ABSTRACT FROM AUTHOR]

Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. To account for limitations associated with small datasets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, and model evaluation. Our findings highlight that the random forest model achieved the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 86.3%) compared to neutral states (F1-score: 65.8%). Additionally, the model demonstrated generalizability when tested on independent secondary datasets, showcasing its ability to distinguish between stress and relaxation states. While our performance metrics might be lower than some previous studies, this likely reflects our focus on robust methodologies to enhance the generalizability and interpretability of ML models, which are crucial for real-world applications with limited datasets. [ABSTRACT FROM AUTHOR]

In the realm of conditionally automated driving, understanding the crucial transition phase after a takeover is paramount. This study delves into the concept of post-takeover stabilization by analyzing data recorded in two driving simulator experiments. By analyzing both driving and physiological signals, we investigate the time required for the driver to regain full control and adapt to the dynamic driving task following automation. Our findings show that the stabilization time varies between measured parameters. While the drivers achieved driving-related stabilization (winding, speed) in eight to ten seconds, physiological parameters (heart rate, phasic skin conductance) exhibited a prolonged response. By elucidating the temporal and cognitive dynamics underlying the stabilization process, our results pave the way for the development of more effective and user-friendly automated driving systems, ultimately enhancing safety and driving experience on the roads. [ABSTRACT FROM AUTHOR]

The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects' diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean. [ABSTRACT FROM AUTHOR]

Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human–robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing. [ABSTRACT FROM AUTHOR]

This retrospective study aimed to analyze the return to running of non-professional runners after experiencing asymptomatic or mild COVID-19. Participants aged 18–55 years who maintained a training load of ≥10 km/week for at least three months prior to diagnosis and utilized Garmin/Polar apps were included. From these devices, parameters such as pace, distance, total running time, cadence, and heart rate were collected at three intervals: pre-COVID, immediately post-COVID, and three months after diagnosis. The Wilcoxon signed rank test was used for analysis (significance was set at ≤0.05). Twenty-one participants (57.1% male; mean age 35.0 ± 9.8 years) were included. The results revealed a significant decrease in running duration and distance two weeks after diagnosis, without significant changes in other parameters. Three months after infection, no differences were observed compared to pre-infection data, indicating a return to the pre-disease training load. These findings underscore the transient impact of COVID-19 on training performance among non-professional runners with mild or asymptomatic symptoms, highlighting the importance of tailored strategies for resuming running after infection. [ABSTRACT FROM AUTHOR]

Introduction: Amyotrophic lateral sclerosis (ALS) produces alterations in the autonomic nervous system (ANS), which explains the cardiac manifestations observed in patients. The assessment of heart rate variability (HRV) is what best reflects the activity of the ANS on heart rate. The Polar H7 Bluetooth® device proves to be a non-invasive and much faster technology than existing alternatives for this purpose. Objective: The goal of this study is to determine HRV using Polar H7 Bluetooth technology in ALS patients, comparing the obtained measurements with values from healthy individuals. Method: The sample consisted of 124 participants: 68 diagnosed with ALS and 56 healthy individuals. Using Polar H7 Bluetooth technology and the ELITE HRV application, various HRV measurements were determined for all participants, specifically the HRV index, RMSSD, RMSSD LN, SDNN index, PNN50, LF, HF, LF/HF ratio, HR average, and HF peak frequency. Results: Statistically significant differences were observed between ALS patients and healthy individuals in the HRV index, RMSSD, RMSSD LN, SDNN index, PNN50, HF, and LF, where healthy individuals exhibited higher scores. For the HR average, the ALS group showed a higher value. Values were similar when comparing men and women with ALS, with only a higher HF peak frequency observed in women. Conclusion: The Polar H7 Bluetooth® device is effective in determining heart rate variability alterations in ALS, being a promising prognostic tool for the disease. [ABSTRACT FROM AUTHOR]

This work proposes a highly sensitive sandwich heterostructure multimode optical fiber microbend sensor for heart rate (HR), respiratory rate (RR), and ballistocardiography (BCG) monitoring, which is fabricated by combining a sandwich heterostructure multimode fiber Mach–Zehnder interferometer (SHMF-MZI) with a microbend deformer. The parameters of the SHMF-MZI sensor and the microbend deformer were analyzed and optimized in detail, and then the new encapsulated method of the wearable device was put forward. The proposed wearable sensor could greatly enhance the response to the HR signal. The performances for HR, RR, and BCG monitoring were as good as those of the medically approved commercial monitors. The sensor has the advantages of high sensitivity, easy fabrication, and good stability, providing the potential for application in the field of daily supervision and health monitoring. [ABSTRACT FROM AUTHOR]

In organisational contexts, professionals are required to decide dynamically and prioritise unexpected external inputs deriving from multiple sources. In the present study, we applied a multimethodological neuroscientific approach to investigate the ability to resist and control ecological distractors during decision-making and to explore whether a specific behavioural, neurophysiological (i.e., delta, theta, alpha and beta EEG band), or autonomic (i.e., heart rate—HR, and skin conductance response—SCR) pattern is correlated with specific personality profiles, collected with the 10-item Big Five Inventory. Twenty-four participants performed a novel Resistance to Ecological Distractors (RED) task aimed at exploring the ability to resist and control distractors and the level of coherence and awareness of behaviour (metacognition ability), while neurophysiological and autonomic measures were collected. The behavioural results highlighted that effectiveness in performance did not require self-control and metacognition behaviour and that being proficient in metacognition can have an impact on performance. Moreover, it was shown that the ability to resist ecological distractors is related to a specific autonomic profile (HR and SCR decrease) and that the neurophysiological and autonomic activations during task execution correlate with specific personality profiles. The agreeableness profile was negatively correlated with the EEG theta band and positively with the EEG beta band, the conscientiousness profile was negatively correlated with the EEG alpha band, and the extroversion profile was positively correlated with the EEG beta band. Taken together, these findings describe and disentangle the hidden relationship that lies beneath individuals' decision to inhibit or activate intentionally a specific behaviour, such as responding, or not, to an external stimulus, in ecological conditions. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative non-contact heart rate monitoring method based on Wi-Fi Channel State Information (CSI). This approach integrates both amplitude and phase information of the CSI signal through rotational projection, aiming to optimize the accuracy of heart rate estimation in home environments. We develop a frequency domain subcarrier selection algorithm based on Heartbeat to subcomponent ratio (HSR) and design a complete set of signal filtering and subcarrier selection processes to further enhance the accuracy of heart rate estimation. Heart rate estimation is conducted by combining the peak frequencies of multiple subcarriers. Extensive experimental validations demonstrate that our method exhibits exceptional performance under various environmental conditions. The experimental results show that our subcarrier selection method for heart rate estimation achieves an average accuracy of 96.8%, with a median error of only 0.8 bpm, representing an approximately 20% performance improvement over existing technologies. [ABSTRACT FROM AUTHOR]

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field. [ABSTRACT FROM AUTHOR]

Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20–100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland–Altman plots and coefficient of variation (CoV). A sampling frequency of ~100–200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40–50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1–8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates. [ABSTRACT FROM AUTHOR]

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value. [ABSTRACT FROM AUTHOR]

We recently reported enhanced parasympathetic activation at rest throughout pregnancy associated with regular yoga practice. The present study presents a secondary analysis of data collected within a prospective cohort study of 33 pregnant women practicing yoga once weekly throughout pregnancy and 36 controls not involved in formal pregnancy exercise programs. The objective was to assess the impact of prenatal yoga on the autonomic nervous system stress response. Healthy pregnant women with singleton pregnancies were recruited in the first trimester. There was no significant difference in the maternal body mass index (BMI) between the yoga group and the controls (24.06 ± 3.55 vs. 23.74 ± 3.43 kg/m2, p = 0.693). Women practicing yoga were older (28.6 ± 3.9 vs. 31.3 ± 3.5 years, p = 0.005) and more often nulliparous (26 (79%) vs. 18 (50%), p = 0.001). We studied heart rate variability (HRV) parameters in the time domain (SDNN, standard deviation of regular R-R intervals, and RMSSD, square root of mean squared differences of successive R-R intervals) and frequency domain (ln(LF/HF), natural logarithm of low-frequency to high-frequency power), as well as synchronization indices of heart rate, blood pressure and respiration during and immediately following acute psychological stress of a standardized mental challenge test. Measurements were performed once per trimester before and after yoga or a 30 min moderate-intensity walk. Statistical comparison was performed using three-way analyses of variance (p < 0.05 significant). Time domain HRV parameters during and following mental challenge in the yoga group were significantly higher compared to the controls regardless of the trimester (F = 7.22, p = 0.009 for SDNN and F = 9.57, p = 0.003 for RMSSD, respectively). We observed no significant differences in the yoga group vs. the controls in terms of ln(LF/HF) and synchronization indices. Regular prenatal yoga practice was associated with a significantly reduced sympathetic response to mental challenge and quicker recovery after acute psychological stress. These effects persisted throughout pregnancy with regular practice. [ABSTRACT FROM AUTHOR]

Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject's thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1–S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland–Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation. [ABSTRACT FROM AUTHOR]

Working on a moving platform can significantly impede human performance. Previous studies on moving vehicles have often focused on the overall impact on general task performance, whereas our study's emphasis is on precise hand movements, exploring the interaction between body motion and the escalation of task difficulty. We recruited 28 participants to engage in reciprocal aiming tasks, following Paul Fitts's setting, under both in-motion and stationary conditions. The task index of difficulty (ID) was manipulated by varying the width of the targets and the distance between the targets. We measured participants' movement time (MT), performance errors, and monitored their eye movements using an eye-tracking device, heart rate (HR), and respiration rate (RR) during the tasks. The measured parameters were compared across two experimental conditions and three ID levels. Compared to the stationary conditions, the in-motion conditions degraded human aiming performance, resulting in significantly prolonged MT, increased errors, and longer durations of eye fixations and saccades. Furthermore, HR and RR increased under the in-motion conditions. Linear relationships between MT and ID exhibited steeper slopes under the in-motion conditions compared to the stationary conditions. This study builds a foundation for us to explore the control mechanisms of individuals working in dynamic and demanding environments, such as pilots in airplanes and paramedics in ambulances. [ABSTRACT FROM AUTHOR]

Postpartum depression (PPD) is a serious mental health issue among women after childbirth, and screening systems that incorporate questionnaires have been utilized to screen for PPD. These questionnaires are sensitive but less specific, and the additional use of objective measures could be helpful. The present study aimed to verify the usefulness of a measure of autonomic function, heart rate variability (HRV), which has been reported to be dysregulated in people with depression. Among 935 women who had experienced childbirth and completed the Edinburgh Postnatal Depression Scale (EPDS), HRV was measured in EPDS-positive women (n = 45) 1 to 4 weeks after childbirth using a wearable device. The measurement was based on a three-behavioral-state paradigm with a 5 min duration, consisting of rest (Rest), task load (Task), and rest-after-task (After) states, and the low-frequency power (LF), the high-frequency power (HF), and their ratio (LF/HF) were calculated. Among the women included in this study, 12 were diagnosed with PPD and 33 were diagnosed with adjustment disorder (AJD). Women with PPD showed a lack of adequate HRV regulation in response to the task load, accompanying a high LF/HF score in the Rest state. On the other hand, women with AJD exhibited high HF and reduced LF/HF during the After state. A linear discriminant analysis using HRV indices and heart rate (HR) revealed that both the differentiation of PPD and AJD patients from the controls and that of PPD patients from AJD patients were possible. The sensitivity and specificity for PPD vs. AJD were 75.0% and 90.9%, respectively. Using this paradigm, an HRV measurement revealed the characteristic autonomic profiles of PPD and AJD, suggesting that it may serve as a point-of-care sensing tool in PPD screening systems. [ABSTRACT FROM AUTHOR]