Search Results (296)

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

Continuous monitoring of respiratory patterns is essential for disease diagnosis and daily health care. Contact medical devices enable reliable respiratory monitoring, but can cause discomfort and are limited in some settings. Radar offers a noncontact respiration measurement method for continuous, real-time, high-precision monitoring. However, it is difficult for a single radar to characterize the coordination of chest and abdominal movements during measured breathing. Moreover, motion interference during prolonged measurements can seriously affect accuracy. This study proposes a dual radar system with customized narrow-beam antennas and signals to measure the chest and abdomen separately, and an adaptive dynamic time warping (DTW) algorithm is used to effectively suppress motion interference. The system is capable of reconstructing respiratory waveforms of the chest and abdomen, and robustly extracting various respiratory parameters via motion interference. Experiments on 35 healthy subjects, 2 patients with chronic obstructive pulmonary disease (COPD), and 1 patient with heart failure showed a high correlation between radar and respiratory belt signals, with correlation coefficients of 0.92 for both the chest and abdomen, a root mean square error of 0.80 bpm for the respiratory rate, and a mean absolute error of 3.4° for the thoracoabdominal phase angle. This system provides a noncontact method for prolonged respiratory monitoring, measurement of chest and abdominal asynchrony and apnea detection, showing promise for applications in respiratory disorder detection and home monitoring. Full article

This study included 221 patients with COVID-19 who were admitted to the emergency department of Avicenne Hospital in Rabat between August 2020 and August 2021. Patients were divided into three groups according to their D-dimer levels (<1, 1–2, and >2 µg/mL). Adjusted and unadjusted logistic regression analyses were performed to assess the association between elevated D-dimer levels and in-hospital mortality. Pearson’s correlation analysis was performed to explore the relationship between D-dimer levels and various biological and clinical parameters. The results revealed a statistically significant difference in the mean (SD) age among the three groups (p = 0.006). Analysis showed a statistically significant difference in the means (SD) of oxygen saturation, duration of hospital stay, and breathing rate among the three independent groups of COVID-19 patients. Patients with elevated D-dimer levels (greater than 2 µg/mL) experienced worse outcomes than those in the other groups, with severity, transfer to intensive care, and in-hospital mortality of 55 (40.7%), 35 (16%), and 24 (11%) patients, respectively, with p-values of 0.048, 0.002, and 0.002, respectively. Patients in the D-dimer > 2 µg/mL group had significantly higher C-reactive protein (CRP), lactate dehydrogenase, urea, cardiac troponin, B-type natriuretic peptide, and ferritin levels than those in the other two groups. The p-value was significant among the three groups (p = 0.044, p = 0.001, and p < 0.001). Age and elevated D-dimer levels (greater than 2 µg/mL) were associated with mortality in patients diagnosed with COVID-19. Correlation analysis indicated that D-dimer in COVID-19 patients is associated with worsening respiratory, hepatic, cardiac, and coagulation parameters, suggesting their utility as an integrative marker of disease severity. D-dimer levels > 2 µg/mL were identified as an independent risk factor for COVID-19 in-hospital mortality. Measuring and monitoring D-dimer levels can assist clinicians in taking timely actions and predicting the prognosis of patients with COVID-19. Full article

Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices. Full article

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute. Full article

We have developed an adjustable optical fiber Bragg grating (FBG) sensing unit for monitoring nasal breathing. The FBG sensing unit can accommodate individuals with varying facial dimensions by adjusting the connecting holes of the ear hangers. We employed two FBG configurations: an encapsulated FBG within a silicon tube (FBG₁) and a bare FBG (FBG₂). Calibration experiments show the temperature sensitivities of 6.77 pm/°C and 6.18 pm/°C, respectively, as well as the pressure sensitivities of 2.05 pm/N and 1.18 pm/N, respectively. We conducted breathe monitoring tests on male and female volunteers under the resting and the motion states. For the male volunteer, the breathing frequency is 13.48 breaths per minute during the rest state and increases to 23.91 breaths per minute during the motion state. For the female volunteer, the breathing frequency is 14.12 breaths per minute during rest and rises to 24.59 breaths per minute during motion. Experimental results show that the FBG sensing unit can effectively distinguish breathing rate for the same person in different states. In addition, we employed a random forest algorithm to assess the importance of two sensors in breathing monitoring applications. The findings indicate that FBG₁ outperforms FBG₂ in monitoring performance, highlighting that pressure plays a positive impact in enhancing the accuracy of breathing monitoring. Full article

The identification of vital signs is becoming increasingly important in various applications, including healthcare monitoring, security, smart homes, and locating entrapped persons after disastrous events, most of which are achieved using continuous-wave radars and ultra-wideband systems. Operating frequency and transmission power are important factors to consider when conducting earthquake search and rescue (SAR) operations in urban regions. Poor communication infrastructure can also impede SAR operations. This study proposes a method for vital sign detection using an integrated sensing and communication (ISAC) system where a unified orthogonal frequency division multiplexing (OFDM) signal was adopted, and it is capable of sensing life signs and carrying out communication simultaneously. An ISAC demonstration system based on software-defined radios (SDRs) was initiated to detect respiratory and heartbeat rates while maintaining communication capability in a typical office environment. The specially designed OFDM signals were transmitted, reflected from a human subject, received, and processed to estimate the micro-Doppler effect induced by the breathing and heartbeat of the human in the environment. According to the results, vital signs, including respiration and heartbeat rates, have been accurately detected by post-processing the reflected OFDM signals with a 1 MHz bandwidth, confirmed with conventional contact-based detection approaches. The potential of dual-function capability of OFDM signals for sensing purposes has been verified. The principle and method developed can be applied in wider ISAC systems for search and rescue purposes while maintaining communication links. Full article

Exercise metrics are critical for assessing health, but real-time heart rate and respiration measurements remain challenging. We propose a physiological monitoring system that uses an in-ear microphone to extract heart rate and respiration from faint ear canal signals. An improved non-negative matrix factorization (NMF) algorithm combines with a short-time Fourier transform (STFT) to separate physiological components, while an inverse Fourier transform (IFT) reconstructs the signal. The earplug effect enhances the low-frequency components, thereby improving the signal quality and noise immunity. Heart rate is derived from short-term energy and zero-crossing rate, while a BiLSTM-based model can refine the breathing phases and calculate indicators such as respiratory rate. Experiments have shown that the average accuracy can reach 91% under various conditions, exceeding 90% in different environments and under different weights, thus ensuring the system’s robustness. Full article

Background: Previous research indicates that gastrointestinal discomfort from milk consumption may be attributable to A1 β-casein, rather than lactose intolerance alone. A2 milk (free of A1 β-casein) consumption may result in fewer symptoms compared to conventional milk containing both A1/A2 β-casein. Objective: In this five-week, double-blind, double-crossover study, we assessed the physiological responses to doses escalating in volume of lactose-free conventional milk (Lactaid), A2 milk, and lactose-free A2 milk in fluid milk-avoiding participants. Methods: Each milk type was consumed over three separate weeks with three increasing doses across five days per week, >one week washout. Gastrointestinal symptoms, blood glucose, and breath gases were monitored for twenty-four, two-, and three-hours post-consumption, respectively. Sensory evaluation was completed for each sample. Results: Fifty-three participants consented and were randomized, with forty-eight participants completing the study. Overall, symptoms were minimal. On Days 1 and 3, lower ratings of bloating and flatulence were observed in A2 compared to lactose-free A2. Breath hydrogen responses reflected lactose content, but were higher in lactose-free A2 than Lactaid on Day 5. Thirty-three participants were deemed lactose-intolerant and had higher fasting and average breath hydrogen for all samples. The only symptom corresponding to the increase in breath hydrogen among these participants was flatulence after A2 consumption. Surprisingly, flatulence was apparently higher for lactose-tolerant individuals when consuming Lactaid compared to A2. Conclusions: These findings suggest that adults who avoid conventional fluid milk consumption may experience minimal GI discomfort from lactose-free and/or A1-free milks. Full article

Early detection of cancer is crucial for improving patient outcomes. Traditional modalities such as mammography and low-dose computed tomography are effective but exhibit inherent limitations, including radiation exposure and accessibility challenges. This review explores innovative, non-invasive cancer screening methods, focusing on liquid biopsy and volatile organic compound (VOC)-based detection platforms. Liquid biopsy analyzes circulating tumor DNA and other biomarkers in bodily fluids, offering potential for early detection and monitoring of treatment response. VOC-based detection leverages unique metabolic signatures emitted by cancer cells, detectable in exhaled breath or other bodily emissions, providing a rapid and patient-friendly screening option. We provide a comprehensive overview of these advanced multi-cancer detection techniques to enhance diagnostic accuracy, accessibility, and patient adherence, and ultimately enhance survival rates and patient outcomes. Full article

In this study, we investigate the relationship between breathing patterns and cardiac autonomic nervous activity during spontaneous breathing. Electrocardiograms and respiratory signals were simultaneously monitored in six subjects for 5 min while in a seated position. The inhalation/exhalation ratio (i/e) was calculated, and its variance was compared with the heart rate variability index. The results showed that inhalation time tended to be longer than exhalation time, with the inhalation-to-exhalation ratio ranging from 1.074 to 1.423. Additionally, one subject exhibited an unusually slow respiratory cycle. The inhalation/exhalation ratio was partly associated with changes in the low-frequency to high-frequency ratio (LF/HF) of heart rate variability, indicating individual differences. These findings suggest that while breathing patterns play a role in autonomic nervous system regulation and may have applications in stress and respiratory health management, there are limitations to these associations. Full article

A new modulation scheme for frequency-modulated continuous-wave (FMCW) radars with millimeter-level target motion detection capability is presented. The proposed radar scheme is free from the synchronization constraint and exhibits low sensitivity to internal parasitic mutual coupling, thus significantly reducing its design complexity without worsening its performance in terms of accuracy and operating ranges. Alternatively to canonical FMCW radars, which exploit chirp signals with triangular or sawtooth-like frequency variation, a radar based on a sinusoidal frequency modulation, which does not require specific synchronization procedures to achieve accurate motion detection even at a short distance from the radar, was developed. Both numerical and experimental results, performed with a 24 GHz radar, have shown the suitability of the proposed modulation scheme for monitoring very small target movements, consistent with those typically exhibited by the human thorax during basic vital activities (heartbeat and respiration). This makes the proposed radar scheme a suitable solution for contactless heart and breath rate monitoring. Full article

In-vehicle physiological sensing is emerging as a vital approach to enhancing driver monitoring and overall automotive safety. This pilot study explores the feasibility of a pressure-based system, repurposing commonplace occupant classification electronics to capture respiration signals during real-world driving. Data were collected from a driver-seat-embedded, fluid-filled pressure bladder sensor during normal on-road driving. The sensor output was processed using simple filtering techniques to isolate low-amplitude respiratory signals from substantial background noise and motion artifacts. The experimental results indicate that the system reliably detects the respiration rate despite the dynamic environment, achieving a mean absolute error of 1.5 breaths per minute with a standard deviation of 1.87 breaths per minute (9.2% of the mean true respiration rate), thereby bridging the gap between controlled laboratory tests and real-world automotive deployment. These findings support the potential integration of unobtrusive physiological monitoring into driver state monitoring systems, which can aid in the early detection of fatigue and impairment, enhance post-crash triage through timely vital sign transmission, and extend to monitoring other vehicle occupants. This study contributes to the development of robust and cost-effective in-cabin sensor systems that have the potential to improve road safety and health monitoring in automotive settings. Full article

Background/Objectives: Premature infants frequently experience feeding difficulties due to the disrupted coordination of sucking, swallowing, and breathing, increasing the risk of airway compromise. In adults with dysphagia, cold liquids can enhance swallowing by stimulating sensory receptors in the pharyngeal mucosa. We previously demonstrated that short-duration feeding with cold liquid significantly reduces dysphagia in preterm infants; however, the impact of an entire feeding with cold milk remains unexplored. This study aimed to evaluate the safety of cold milk feedings in preterm infants with uncoordinated feeding patterns and their impact on their feeding performance. Methods: Preterm infants with uncoordinated feeding patterns (n = 26) were randomized to be fed milk at either room temperature (RT) or cold temperature (CT) using an experimental, randomized crossover design. We monitored axillary and gastric content temperatures, mesenteric blood flow, and feeding performance. Results: There were no significant differences in mesenteric blood flow Doppler measurements or axillary body temperatures between the CT and RT feeding conditions. However, a reduction in gastric content temperatures of 3.6 °F and 2.7 °F was observed at one and thirty minutes following CT feeding, respectively. No evidence of cold stress, increased episodes of apnea or bradycardia, gastric residuals, or emesis was noted in infants during or after the CT feeding condition. Feeding performance outcomes did not differ significantly regarding milk transfer rate (p = 0.781) or proficiency (p = 0.425). However, the quality score on the Infant-Driven Feeding Scale (IDFS) showed a significant improvement following CT feeding (p = 0.001). Conclusions: Cold milk feeding can be a safe therapeutic option for preterm infants. This underscores the potential for further comprehensive investigations to evaluate cold milk feeding as an effective therapeutic strategy for managing feeding and swallowing difficulties in preterm infants. The study was registered at clinicaltrials.org under #NCT04421482. Full article

Background: Continuous vital sign monitoring using wearable sensors has gained traction for the early detection of patient deterioration, particularly with the advent of virtual wards. Objective: The objective was to evaluate the reliability of a wearable sensor for monitoring heart rate (HR), respiratory rate (RR), and temperature in acutely unwell hospital patients and to identify the optimal time window for alert generation. Methods: A prospective cohort study recruited 500 patients in a single hospital. Sensor readings were compared to standard intermittent nurse observations using Bland–Altman plots to assess the limits of agreement. Results: HR demonstrated good agreement with nurse observations (intraclass correlation coefficient [ICC] = 0.66, r = 0.86, p < 0.001), with a mean difference of 3.63 bpm (95% LoA: −10.87 to 18.14 bpm). RR exhibited weaker agreement (ICC = 0.20, r = 0.18, p < 0.001), with a mean difference of −2.72 breaths per minute (95% LoA: −10.91 to 5.47 bpm). Temperature showed poor to fair agreement (ICC = 0.30, r = 0.39, p < 0.001), with a mean difference of −0.57 °C (95% LoA: −1.72 to 0.58 °C). A 10 min averaging window was identified as optimal, balancing data retention and real-time alerting. Conclusions: Wearable sensors demonstrate potential for reliable continuous monitoring of vital signs, supporting their future integration into real-world clinical practice for improved patient safety. Full article