Search Results (650)

Respiratory rate (RR) is a critical indicator of physiological status, yet unobtrusive and continuous RR monitoring remains challenging, particularly in wearable applications that require soft, lightweight, and low-power sensing systems. This paper presents an integrated approach that combines a textile-embedded embroidered strain-gauge sensor with Tiny Machine Learning (TinyML) to enable real-time, on-device RR estimation. The sensing platform consists of a textile-integrated meander-pattern strain gauge and a fabric-mounted analog readout circuit, which together capture thoracic expansion during breathing. Two lightweight neural network models—a convolutional neural network (CNN) operating on raw respiratory waveforms and a dense neural network (DNN) operating on wavelet features—were developed and trained using a public strain-sensor dataset and a custom dataset collected with the textile system (TexHype dataset). Both models were optimized through 8-bit quantization and deployed to an STM32L4 microcontroller, where end-to-end on-device preprocessing, filtering, segmentation, normalization, and inference were performed. The CNN achieved the highest accuracy, with a mean absolute error (MAE) of 1.23 breaths per minute (BPM) on the TexHype dataset, but exhibited substantial inference latency (5.8–6.2 s) due to its computational complexity. In contrast, the wavelet-based DNN demonstrated lower accuracy (MAE 2.21 BPM) but achieved real-time performance with inference times of 18–96 ms, and a power overhead ($\Delta P=P_{\mathrm{active}}-P_{\mathrm{idle}}$) of approximately 3.3 mW during inference. Cross-dataset testing revealed limited generalization between different strain-sensor platforms. The findings highlight key trade-offs between accuracy, latency, and energy efficiency, and illustrate the potential of combining stretchable electronics with embedded intelligence to enable next-generation wearable respiratory monitoring systems. Full article

Infant respiration is a physiological marker of health and wellbeing that can provide insight into sleep and wake patterns. Technological innovation presents opportunities to enhance measurements of physiological signals, which improves ecological validity and participant experiences. This is particularly true in the context of studying infant sleep, as it can be disrupted by changes in the environment and the physical sensation of unfamiliar or uncomfortable sensors. The goal of this study was to examine if a commercially available video baby monitor (Nanit system) can accurately estimate respiration during a nap relative to a commonly used cardiorespiratory sensor (Isansys Lifetouch sensor). Thirty-three infants (M = 9.7 months; range = 1–22 months) took a nap while wearing the Lifetouch sensor and Nanit Breathing Band. Infants slept in view of the Nanit camera. A computer vision algorithm applied to the video detected movement of the patterns on the fabric band worn around the infant’s torso to determine respiratory rates. The results showed strong consistency between the devices. More than 95% of the minute-by-minute respiration data fell within the limits of agreement, with little bias. Agreement was not influenced by age or nap duration, suggesting the Nanit Breathing Band provides a valid measure of respiration across infancy. Full article

(1) Background: Hospitalized preterm infants often require months of vital signs monitoring in the neonatal intensive care unit. Today, wired sensors are essential for survival, but are associated with numerous disadvantages including sensor dislocations, skin trauma and hygiene risks. Non-contact vital sign monitoring would therefore represent a significant improvement in the care of hospitalized neonates. (2) Objective: This study aims to lay the foundation for non-contact radar-based monitoring of the respiratory rate, which could be used in the neonatal intensive care unit. (3) Methods: We developed a radar-based vital parameter monitoring system for recording the respiratory rate of premature infants in a pediatric incubator. The novel system employs a four-channel I/Q FMCW radar with compact, application-specific antennas optimized to cover the defined area of interest on the infant’s thorax. As a proof-of-principle study, the system was tested in six anesthetized newborn piglets. (4) Results: Using the radar-based system, thorax movements were detected and the respiratory rate was calculated. We observed a high accordance between the signals of respiration detected by the novel radar sensor with the signals of the cable-bound monitor in resting piglets. (5) Conclusions: The novel radar sensor is suited for measuring respiration in the piglet model. In future, the sensor should be optimized in order to improve its robustness against disturbances body movements and in order to allow detection of heartbeat. Full article

Accurate and interpretable air quality prediction remains a critical challenge for environmental health management due to complex, nonlinear interactions among emissions, meteorology, and atmospheric chemistry. This study presents a hybrid physics informed and multimodal deep learning framework for city-scale air quality and health risk prediction. The framework combines a Gaussian plume dispersion model with a residual CNN-LSTM network that learns data driven corrections while preserving physical consistency. Multimodal open datasets, including ground based pollutant sensors, meteorological records, and satellite derived aerosol and temperature features, are jointly fused to improve spatiotemporal fidelity. An Exposure Health Index module further links predicted pollutant fields with respiratory morbidity indicators, providing a quantitative bridge between atmospheric variability and health outcomes. Using open source datasets from Riyadh, Jeddah, and Dammam, the proposed approach achieves up to 25% lower mean absolute error and $R^2$ values above 0.85 compared with physics only and purely data driven baselines. Explainability analyses using SHAP and spatial attention highlight physically plausible drivers and confirm feature relevance. The results demonstrate that physics guided residual learning can unify deterministic dispersion modeling and multimodal inference, providing a transparent, scalable, and reproducible foundation for air quality forecasting and health risk assessment. Full article

Continuous blood pressure (BP) monitoring is essential for the prevention and management of cardiovascular diseases. Traditional cuff-based methods cause discomfort during repeated measurements, and wearable sensors require direct skin contact, limiting their applicability. Radar-based contactless BP measurement has emerged as a promising alternative. However, radar pulse wave (RPW) signals are susceptible to motion artifacts, respiratory interference, and environmental clutter, posing persistent challenges to estimation accuracy and robustness. In this paper, we propose MARU-MTL, a Mamba-enhanced multi-task learning framework for continuous BP estimation using a single millimeter-wave radar sensor. To address signal quality degradation, a Variational Autoencoder-based Signal Quality Index (VAE-SQI) mechanism is proposed to automatically screen RPW segments without manual annotation. To capture long-range temporal dependencies across cardiac cycles, we integrate a Bidirectional Mamba module into the bottleneck of a U-Net backbone, enabling linear-time sequence modeling with respect to the segment length. We also introduce a multi-task learning strategy that couples BP regression with arterial blood pressure waveform reconstruction to strengthen physiological consistency. Extensive experiments on two datasets comprising 55 subjects demonstrate that MARU-MTL achieves mean absolute errors of 3.87 mmHg and 2.93 mmHg for systolic and diastolic BP, respectively, meeting commonly used AAMI error thresholds and achieving metrics comparable to BHS Grade A. Full article

Machine learning (ML) is increasingly influencing the assessment and delivery of movement and exercise, yet its role within adapted physical activity (APA) for individuals with chronic conditions has not been comprehensively synthesized. ML-based approaches have the potential to enhance functional assessment, support individualized exercise prescription, and facilitate scalable monitoring across preventive, community-based, and long-term adapted exercise settings, particularly in populations characterized by functional heterogeneity and variable responses to exercise. The aim of this narrative review is to synthesize and critically discuss current ML applications relevant to the core professional processes of APA practice. A structured narrative review was conducted using searches in PubMed/MEDLINE, Scopus, and Web of Science, complemented by targeted searches in engineering-oriented sources to capture ML methods not consistently indexed in biomedical databases. The search covered the period in which contemporary ML approaches have been increasingly applied to human movement and exercise research and was last updated in January 2026. Evidence was synthesized thematically into application-oriented domains relevant to APA practice. ML applications in APA include markerless motion and gait analysis, wearable-sensor data processing, balance and fall-risk assessment, and functional classification. Predictive and adaptive models support individualized regulation of exercise intensity, progression, and workload, including remote and hybrid delivery models. Applications span oncology, cardiometabolic, respiratory, neuromuscular conditions, and adapted sport contexts. Ethical, legal, and governance issues, such as algorithmic bias, data privacy, and professional accountability, emerge as central considerations for safe and equitable implementation. ML represents a promising decision-support layer for APA, complementing professional expertise through enhanced assessment, personalization, and monitoring. Its effective integration requires robust validation, interpretability, and responsible governance to ensure that ML augments, rather than replaces, professional judgment in APA practice. Full article

Accurate, non-contact vital-sign monitoring promises a scalable alternative to conventional sensors, yet low signal quality and long recording times have limited real-life adoption. We present a dual-modality system that combines Eulerian video magnified remote photoplethysmography (rPPG) from facial videos with optical flow-based shoulder tracking to estimate heart rate (HR) and respiratory rate (RR) from ultra-short 15 s recordings. With 200 participants, each providing 2 videos, 387 videos passed strict usability criteria, excluding flicker, blur, occlusion, and low illumination. For 15 s recordings, the HR estimates reached 98.5% accuracy within a ±10 beats per minute tolerance (MAE = 3.25, RMSE = 4.88, r = 0.93; p < 0.05) and the RR estimates achieved 98.4% accuracy within a ±5 respirations per minute tolerance (MAE = 0.69, RMSE = 0.87, r = 0.90; p < 0.05), exceeding prior studies that required 30 to 60 s recording lengths. Computational analysis on a standard home computer confirmed feasibility, with near real-time performance achievable on optimized hardware. By integrating complementary modalities and rigorous video quality control, the system overcomes low-SNR challenges, delivering high-fidelity, clinically validated vital signs monitoring. These results establish a robust, scalable, and precise framework for clinical and home care, demonstrating that accurate, contact-free HR and RR monitoring can now be achieved in seconds, making rapid, real-life vital signs assessment practical and accessible. Full article

Mitochondria are a key organelle in maintaining metabolic homeostasis. It not only generates most of the cell’s energy through oxidative phosphorylation but also acts as a complex sensor of the redox state and oxygen in the cell. This review thoroughly analyzes the interactions among mitochondrial iron metabolism, mitochondrial reactive oxygen species (mtROS), and lipid peroxidation (LPO), the triggering factors of ferroptosis, an iron-dependent form of programmed cell death. We point out research showing that intrinsic mitochondrial machinery, such as iron–sulfur (Fe-S) cluster assembly and heme metabolism, is both an important cofactor and a master regulator. If these processes are disrupted, they can lead to ferroptosis. Unlike views that focus on the cytosol, we explain that the stability of Fe-S clusters in complexes such as aconitase and respiratory Complex I is crucial for preventing electron leakage and excessive mtROS formation. The Fenton reaction and its direct effect on cardiolipin (CL) oxidation in the inner membrane of mitochondria is a central event in cardiometabolic diseases. Its peroxidation and breakdown make the organelle very unstable and lead to cell death though Ca²⁺ overload and a significantly decreased reduced/oxidized glutathione ratio. Additionally, the functions of essential iron transporters and glutathione homeostasis are examined, and their dysregulation is correlated with ferroptosis-associated progression of cardiometabolic and neurodegenerative disorders, such as obesity and Alzheimer’s disease. This review focused on the need to revisit the classic bioenergetic core of the mitochondria as a key player in the pathophysiology of metabolic and neurodegenerative diseases. Full article

Continuous respiratory monitoring plays a crucial role in both clinical and non-clinical applications, providing valuable insights into physiological health. This paper presents a sustainable, contactless respiratory monitoring framework that integrates acoustic sensing with a lightweight convolutional neural network (CNN) optimized for low-power embedded platforms. Breathing sounds are processed using wavelet-based denoising and Mel-Frequency Cepstral Coefficient (MFCC) extraction, achieving 94.8% classification accuracy with an inference latency of 0.3 s per frame. The quantized model deployed on a Sony Spresense microcontroller reduces memory usage by over 90%. By eliminating disposable sensors and minimizing energy consumption, the proposed approach delivers an eco-efficient, scalable, and real-time solution for continuous respiratory assessment. Full article

To address the challenges of traditional flexible humidity sensors, such as reliance on external power supply, complex fabrication processes, and poor adaptability to energy-limited scenarios, this study successfully developed a low-cost, easily scalable, self-powered flexible humidity sensor based on hydroxypropyl trimethyl ammonium chitosan/lithium chloride (HACC/LiCl) composite electrolyte using a screen-printing process. The device employs A4 paper as the flexible substrate, and interdigitated manganese dioxide (MnO₂) positive electrodes, zinc (Zn) negative electrodes, and HACC/LiCl composite electrolyte layers are sequentially fabricated via screen-printing, ultimately constructing a simple primary battery structure. Through a series of performance screening and optimization, 0.1 mol/L LiCl-modified HACC (HL-1) is identified as the optimal electrolyte system. The test results show that the HL-1 sensor exhibits a wide humidity detection range of 11~97% relative humidity (RH), with the output voltage displaying a good quadratic function relationship with humidity (R² = 0.996), and a peak output voltage of up to 1.2 V. The device possesses excellent cyclic stability and long-term stability, with no significant fluctuation in output voltage under different bending deformation states. This sensor demonstrates broad application prospects in fields such as respiratory monitoring and non-contact sensing, providing a feasible technical path for the development of low-cost passive humidity monitoring equipment. Full article

(1) Background: Evidence on the clinical accuracy of wireless photoplethysmography (PPG)-based vital sign monitoring is limited. This study evaluated the accuracy, technical performance, and patient comfort of a novel PPG-based earsensor for measuring oxygen saturation (SpO₂), pulse rate (PR), and respiratory rate (RR) in postoperative patients. (2) Methods: In this observational method comparison study, SpO₂, PR, and RR were simultaneously recorded using the earsensor and compared with continuous monitoring in patients admitted overnight to the post-anesthesia care unit. Outcome measures were bias, 95% limits of agreement (LoA), and average root mean square (ARMS). Technical performance was evaluated by data loss and data gap duration. Patient comfort was assessed using a questionnaire. (3) Results: Twenty-one patients contributed to 264 h of data. Bias was 1.7% for SpO₂ (ARMS 2.4%; LoA −1.8% to 5.1%), 1.2 bpm for PR (ARMS 3.9 bpm; LoA –6.1 to 8.4 bpm), and 0.3 brpm for RR (ARMS 4.4 brpm; LoA –8.4 to 8.9 brpm). Overall, data loss was 42% for SpO₂, 33% for RR, and 29% for PR; most data gaps were under 30 min. Patient-reported comfort was high (77%). (4) Conclusions: The earsensor accurately measured SpO₂ and PR. RR accuracy was outside the predefined criteria. Despite substantial data loss, patient comfort was high, supporting the potential of PPG-based sensors for unobtrusive vital sign trend monitoring in low-acuity settings. Full article

Contactless monitoring of vital signs such as heart rate (HR) and respiratory rate (RR) has gained significant attention, with vibration-based sensors like geophones showing promise for accurate, non-invasive monitoring. However, most existing systems are developed with healthy subjects and may not generalize well to extreme physiological ranges, such as those observed in infants or patients with arrhythmia. Moreover, the underlying mechanisms of cardiorespiratory vibration dynamics remain insufficiently understood, limiting clinical adoption of these systems. To address these challenges, we present a programmable cardiorespiratory testbed capable of generating realistic HR and RR signals across a wide range (HR: 40–240 bpm, RR: 8–40 bpm). Our system uses a voice coil motor that acts as the vibration source, driven by a Raspberry Pi-based control circuit. Unlike similar systems that use separate modules for heart and lung signals, our setup generates both signals using a single motor. The synthetic signals exhibit a strong correlation of 0.85 compared with data from 75 human subjects. We use this system to design signal processing-based algorithms for vital signs monitoring and demonstrate their robustness for extreme physiological ranges. The proposed system enhances the understanding of cardiorespiratory vibration dynamics while significantly reducing the time and effort required to collect real-world data. Full article

Heart and respiratory rate monitoring during sleep enables the detection of physiological irregularities through contact or contactless methods. Traditional approaches like polysomnography are accurate but costly, ergonomically limited, and often poorly accepted by patients. Smart Bedding^® is a novel, flexible bedsheet equipped with a high-resolution sensor network that records movement, pressure, sound, temperature, and humidity throughout the night. This study aimed to estimate cardiorespiratory parameters using the Smart Bedding^® IMU. Data from 30 participants sleeping on Smart Bedding^® while undergoing simultaneous polysomnography were analyzed. A robust and low-cost preprocessing pipeline was developed; estimation was performed using zero-crossing, peak detection, and Burg’s method for comparison, and validation was conducted using polysomnography as the gold-standard reference. Respiratory and heart rates were accurately estimated, achieving overall accuracies of 93.9% and 88.7% using zero-crossing and peak detection, respectively. Respiratory rate estimation showed no significant limitations across the frequency spectrum or among sleeping positions. However, heart rate estimation accuracy decreased when the frequency was below 55 BPM or when participants slept in a lateral sleep position, likely due to reduced cardiac signal power. Overall, the proposed methodology accurately tracked respiratory and cardiac patterns throughout the night, supporting Smart Bedding^® as a promising tool for future sleep tracking applications. Full article

In recent years, the rapid commercialization and widespread adoption of portable and wearable electronic devices have imposed increasingly stringent performance requirements on flexible sensors, including enhanced sensitivity, stability, response speed, comfort, and integration. This trend has driven extensive research and technological advancement in sensor material systems, among which carbon-based materials have emerged as core candidates for high-performance flexible sensors due to their exceptional electrical conductivity, mechanical flexibility, chemical stability, and highly tunable structural features. Meanwhile, new sensing mechanisms and innovative device architectures continue to emerge, demonstrating significant value in real-time health monitoring, early disease detection, and motion-state analysis, thereby expanding the functional boundaries of flexible sensors in the health-care sector. This review focuses on the application progress and future opportunities of carbon-based materials in flexible sensors, systematically summarizing the critical roles and performance-optimization strategies of carbon nanotubes, graphene, carbon fibers, carbon black, and their derivative composites in various sensing systems, including strain and pressure sensing, physiological electrical signal detection, temperature monitoring, and chemical or environmental sensing. In response to the growing demands of modern health-monitoring technologies, this review also examines the practical applications and challenges of flexible sensors—particularly those based on emerging mechanisms and novel structural designs—in areas such as heart-rate tracking, blood-pressure estimation, respiratory monitoring, sweat-component analysis, and epidermal electrophysiological signal acquisition. By synthesizing the current research landscape, technological pathways, and emerging opportunities of carbon-based materials in flexible sensors, and by evaluating the design principles and practical performance of diverse health-monitoring devices, this review aims to provide meaningful reference insights for researchers and support the continued innovation and practical deployment of next-generation flexible sensing technologies. Full article

Humidity sensing is essential in medical fields such as respiratory support, neonatal care, sterilization, and pharmaceutical storage. However, current sensors face limitations, including slow response/recovery, low sensitivity, and poor long-term stability. To address these challenges, we developed borophene-multiwalled carbon nanotube (MWCNT) heterostructures using a stepwise in situ thermal decomposition method. The resulting humidity sensor exhibits an ultrabroad detection range (11–97% RH), ultra-high sensitivity (55,000% at 97% RH), and fast response/recovery times (10.04 s/4.8 s). Through interfacial charge-transfer engineering, the system facilitates rapid electron migration, enhances Schottky barrier modulation, and provides abundant active adsorption sites for water molecules, thereby achieving comprehensive improvement in sensing performance. It also demonstrates excellent selectivity, mechanical flexibility, and operational stability. Notably, the sensor’s sensitivity at 97% RH surpasses that of sensors based on pure borophene or MWCNT by 37–462 times, highlighting the advantages of heterostructure engineering. The multifunctionality of the device suggests its potential in areas beyond conventional sensing, including non-contact voice recognition, skin humidity mapping, and real-time breath monitoring. These results lay a solid foundation for developing borophene-MWCNT heterostructures into a high-performance platform for next-generation medical diagnostics and intelligent health monitoring. Full article