Search Results (564)

Contactless vital sign monitoring has emerged as a transformative healthcare technology, enabling the assessment of vital signs without physical contact with the human body. This review comprehensively reviews the rapidly evolving landscape of this field, with particular emphasis on multi-modal sensing approaches and multi-task learning paradigms. We systematically categorize and analyze existing technologies based on sensing modalities (vision-based, radar-based, thermal imaging, and ambient sensing), integration strategies, and application domains. The paper examines how artificial intelligence has revolutionized this domain, transitioning from early single-modality, single-parameter approaches to sophisticated systems that combine complementary sensing technologies and simultaneously extract multiple vital sign parameters. We discuss the theoretical foundations and practical implementations of multi-modal fusion, analyzing signal-level, feature-level, decision-level, and deep learning approaches to sensor integration. Similarly, we explore multi-task learning frameworks that leverage the inherent relationships between vital sign parameters to enhance measurement accuracy and efficiency. The review also critically addresses persisting technical challenges, clinical limitations, and ethical considerations, including environmental robustness, cross-subject variability, sensor fusion complexities, and privacy concerns. Finally, we outline promising future directions, from emerging sensing technologies and advanced fusion architectures to novel application domains and privacy-preserving methodologies. This review provides a holistic perspective on contactless vital sign monitoring, serving as a reference for researchers and practitioners in this rapidly advancing field. Full article

Defensive pessimism, an important emotion regulation and motivation strategy, has increasingly attracted scholarly attention in psychology. Recently, sensor-based methods have begun to supplement or replace traditional questionnaire surveys in personality research. However, current approaches for collecting vital signs data face several challenges, including limited monitoring durations, significant data deviations, and susceptibility to external interference. This paper proposes a novel approach using a NiCr/NiSi alloy film temperature sensor, which has a K-type structure and flexible piezoelectric pressure sensor to identify and predict defensive pessimism personality traits. Experimental results indicate that the Seebeck coefficients for K-, T-, and E-type thermocouples are approximately 41 μV/°C, 39 μV/°C, and 57 μV/°C, respectively, which align closely with national standards and exhibit good consistency across multiple experimental groups. Moreover, radial artery frequency experiments demonstrate a strong linear relationship between pulse rate and the intensity of external stimuli, where stronger stimuli correspond to faster pulse rates. Simulation experiments further reveal a high correlation between radial artery pulse frequency and skin temperature, and a regression model based on the physiological sensor data shows a good fit (p < 0.05). These findings verify the feasibility of using temperature and flexible piezoelectric pressure sensors to identify and predict defensive pessimism personality characteristics. Full article

Monitoring Peripheral Oxygen Saturation (SpO₂) is an important vital sign both in Intensive Care Units (ICUs), during surgery and convalescence, and as part of remote medical consultations after of the COVID-19 pandemic. This has made the development of new SpO₂-measurement tools an area of active research and opportunity. In this paper, we present a new Deep Learning (DL) combined strategy to estimate SpO₂ without contact, using pre-magnified facial videos to reveal subtle color changes related to blood flow and with no calibration per subject required. We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. We tested the method on 18 healthy subjects, where facial videos of the subjects, including the automatic detection of the reference from a contact pulse oximeter device, were acquired. As performance metrics for the SpO₂-estimation proposal, we calculated the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other parameters from the Bland–Altman (BA) analysis with respect to the reference. Therefore, a significant improvement was observed by adding the ensemble technique with respect to the only optimization, obtaining 14.32% in RMSE (reduction from 0.6204 to 0.5315) and 13.23% in MAE (reduction from 0.4323 to 0.3751). On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD $\pm 1.96\sigma$ = −0.00175 ± 1.04. Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation. Full article

The top oil temperature of a transformer is a vital sign reflecting its operational condition. The accurate prediction of this parameter is essential for evaluating insulation performance and extending equipment lifespan. At present, the prediction of oil temperature is mainly based on single-feature prediction. However, it overlooks the influence of other features. This has a negative effect on the prediction accuracy. Furthermore, the training dataset is often made up of data from a single transformer. This leads to the poor generalization of the prediction. To tackle these challenges, this paper leverages large-scale data analysis and processing techniques, and presents a transformer top oil temperature prediction model that combines multiple models. The Convolutional Neural Network was applied in this method to extract spatial features from multiple input variables. Subsequently, a Long Short-Term Memory network was employed to capture dynamic patterns in the time series. Meanwhile, a Transformer encoder enhanced feature interaction and global perception. The spatial characteristics extracted by the CNN and the temporal characteristics extracted by LSTM were further integrated to create a more comprehensive representation. The established model was optimized using the Whale Optimization Algorithm to improve prediction accuracy. The results of the experiment indicate that the maximum RMSE and MAPE of this method on the summer and winter datasets were 0.5884 and 0.79%, respectively, demonstrating superior prediction accuracy. Compared with other models, the proposed model improved prediction performance by 13.74%, 36.66%, and 43.36%, respectively, indicating high generalization capability and accuracy. This provides theoretical support for condition monitoring and fault warning of power equipment. Full article

Background and clinical significance: Acute reduction in vigilance is a frequent reason for emergency department admissions, especially among the elderly. While intracranial causes or infections with fluid depletion are often responsible, there remain cases where imaging, laboratory tests, and clinical examination fail to provide a clear diagnosis. Case presentation: A 91-year-old woman was presented to the emergency department with recurrent episodes of somnolence to deep coma. On admission, her vital signs were stable, and cerebral CT imaging revealed no intracranial pathology. Laboratory analyses, including blood gas measurements, were unremarkable. Empirical treatment for possible intoxications with benzodiazepines or opioids using flumazenil and naloxone had no effect. An Addison’s crisis was considered but excluded following methylprednisolone administration without improvement in consciousness. Eventually, an isolated elevation of serum ammonia was identified as the cause of the reduced vigilance. Further investigation linked the hyperammonemia to abnormal intestinal bacterial colonization, likely due to a prior ureteroenterostomy. There was no evidence of liver dysfunction, thus classifying the condition as non-hepatic hyperammonemia. Therapy was initiated with rifaximin, supported by aggressive laxative regimens. Ammonia levels and vital parameters were closely monitored. The patient’s condition improved gradually, with serum ammonia levels returning to normal and cognitive function fully restored. Conclusions: This case highlights an uncommon cause of coma due to non-hepatic hyperammonemia in the absence of liver disease, emphasizing the diagnostic challenge when standard evaluations are inconclusive. It underscores the need for broad differential thinking in emergency settings and the importance of considering rare metabolic disturbances as potential causes of altered mental status. Full article

Due to the increasing number of vehicles and the aging population, the vulnerability to sudden medical emergencies among drivers is a growing problem. Events such as heart attack, stroke, and loss of consciousness can occur without warning and endanger everyone on the road. Modern vehicles, equipped with electronic systems, can support real-time driver’s health monitoring through early detection technologies. The existing Driver Monitoring Systems (DMS) in our cars assess behavioral states such as drowsiness and distraction. In the future, DMS will include biometric sensors to monitor vital signs such as heart rate and respiration. By finding predictors of sudden illnesses (SI), such a system will provide valuable time for the driver to react before the strike of a medical event. In this paper, we present our vision for DMS operation with physiological monitoring capabilities. A brief overview of sensor’s types and their locations in the vehicle interior used in the research studies for monitoring the corresponding physiological parameters is presented. A comparative analysis of the advantages and disadvantages of the sensing methods used for physiological monitoring of the driver in real driving scenarios is made. Full article

This study evaluates the accuracy of a low-cost wearable system for the continuous monitoring of vital signs, including heart rate, blood oxygen saturation (Sp${\mathrm{O}}_2$), blood pressure trend (BPT), and body temperature. The prototype was built using the nRF52840 microcontroller, which integrates photoplethysmography and infrared sensors. The heart rate and Sp${\mathrm{O}}_2$ data were collected under three body positions (Rest, Sitting, and Standing), while all measurements were performed using both anatomical configurations: BPT-Finger and BPT-Earlobe. Results were compared against validated commercial devices: UT-100 for heart rate and Sp${\mathrm{O}}_2$, G-TECH LA800 for blood pressure, and G-TECH THGTSC3 for body temperature. Ten participants were monitored over a ten-day period. Bland–Altman analysis revealed clinically acceptable agreement thresholds of ±5 mmHg for blood pressure, ±5–10 bpm for heart rate, ±4% for Sp${\mathrm{O}}_2$, and ±0.5 °C for temperature. Both wearable configurations demonstrated clinically acceptable agreement across all vital signs. The BPT-Earlobe configuration exhibited superior stability and lower variability in the Rest and Sitting positions, likely due to reduced motion artifacts. Conversely, the BPT-Finger configuration showed higher Sp${\mathrm{O}}_2$ accuracy in the Standing position, with narrower limits of agreement. These findings highlight the importance of sensor placement in maintaining measurement consistency across physiological conditions. With an estimated cost of only ~USD 130—compared to ~USD 590 for the commercial alternatives—the proposed system presents a cost-effective, scalable, and accessible solution for decentralized health monitoring, particularly in underserved or remote environments. Full article

The awareness of the importance of monitoring human vital signs has increased recently due to the outbreak of the COVID-19 pandemic. Non-invasive heart rate monitoring devices, in particular, have become some of the most popular tools for health monitoring. However, heart rate data alone are not enough to reflect the health of one’s cardiovascular function or arterial health. This growing interest has spurred research into developing high-fidelity non-invasive pulse waveform sensors. These sensors can provide valuable information such as data on blood pressure, arterial stiffness, and vascular aging from the pulse waveform. Among these sensors, optical fiber sensors (OFSs) stand out due to their remarkable properties, including resistance to electromagnetic interference, capability in monitoring multiple vital signals simultaneously, and biocompatibility. This paper reviews the latest advancements in using OFSs to measure human vital signs, with a focus on pulse waveform analysis. The various working mechanisms of OFSs and their performances in measuring the pulse waveform are discussed. In addition, we also address the challenges faced by OFSs in pulse waveform monitoring and explore the opportunities for future development. This technology shows great potential for both clinical and personal non-invasive pulse waveform monitoring applications. 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

Continuous monitoring of vital signs could improve patient care in veterinary hospitals by identifying changes earlier and reducing patient stress from repeated handling. This study aimed to assess the agreement between a wireless harness device and manual measurement of heart rate, respiratory rate, and body temperature in hospitalized dogs. Nineteen client-owned dogs wore the harness throughout hospitalization and paired manual and harness measurements were collected every 4–8 h. Linear regression and Bland–Altman analysis were used to assess agreement. The device demonstrated strong correlation with manual measurements for heart rate and respiratory rate; however, the limits of agreement (LoA) exceeded predefined clinical thresholds, indicating high variability in individual readings. Temperature measurements showed a mean difference of 1.34 °F (manual minus harness), indicating underestimation by the harness. The LoA for temperature also exceeded predefined clinical thresholds, particularly in dogs with long fur. Fur length significantly influenced respiratory rate and temperature measurements, but not heart rate. Chest conformation also impacted respiratory rate and temperature accuracy. Heart rate was the most consistent parameter across all body types. Overall, the device tracked trends in heart rate and respiratory rate, supporting its potential as a supplemental monitoring tool. However, measurements should be confirmed manually prior to clinical decision-making. Full article

Older adults make up about 12% of the public sector, primary care, and hospital use and represent a large proportion of the users of healthcare services. Older people are also more vulnerable to serious injury from unexpected falls due to tripping, slipping, or illness. This underscores the immediate necessity of stable and cost-effective e-health technologies in maintaining independent living. Artificial intelligence (AI) and machine learning (ML) offer promising solutions for early fall prediction and continuous health monitoring. This paper introduces a novel cooperative AI model that forecasts the risk of future falls in the elderly based on behavioral and health abnormalities. Two AI models’ predictions are combined to produce accurate predictions: The ${AI}_1$ model is based on vital signs using Fuzzy Logic, and the ${AI}_2$ model is based on Activities of Daily Living (ADLs) using a Deep Belief Network (DBN). A meta-model then combines the outputs to generate a total fall risk prediction. The results show 85.71% sensitivity, 100% specificity, and 90.00% prediction accuracy when compared to the Morse Falls Scale (MFS). This emphasizes how deep learning-based cooperative systems can improve well-being for older adults living alone, facilitate more precise fall risk assessment, and improve preventive care. Full article

(1) Background: Detecting long-lie incidents—where individuals remain immobile after a fall—is essential for timely intervention and preventing severe health consequences. However, most existing systems focus only on fall detection, neglect post-fall monitoring, and raise privacy concerns, especially in real-time, non-invasive applications; (2) Methods: This study proposes a lightweight, privacy-preserving, long-lie detection system utilizing thermal imaging and a soft-voting ensemble classifier. A low-resolution thermal camera captured simulated falls and activities of daily living (ADL) performed by ten healthy participants. Human pose keypoints were extracted using MediaPipe, followed by the computation of five handcrafted postural features. The top three classifiers—automatically selected based on cross-validation performance—formed the soft-voting ensemble. Long-lie conditions were identified through post-fall immobility monitoring over a defined period, using rule-based logic on posture stability and duration; (3) Results: The ensemble model achieved high classification performance with accuracy, precision, recall, and an F1 score of 0.98. Real-time deployment on a Raspberry Pi 5 demonstrated the system is capable of accurately detecting long-lie incidents based on continuous monitoring over 15 min, with minimal posture variation; (4) Conclusion: The proposed system introduces a novel approach to long-lie detection by integrating privacy-aware sensing, interpretable posture-based features, and efficient edge computing. It demonstrates strong potential for deployment in homecare settings. Future work includes validation with older adults and integration of vital sign monitoring for comprehensive assessment. Full article

We introduce a novel system for automatic assessment of newborn and preterm infant behavior—including activity levels, behavioral states, and sleep–wake cycles—in clinical settings for streamlining care and minimizing healthcare professionals’ workload. While vital signs are routinely monitored, the previously mentioned assessments require labor-intensive direct observation. Research so far has already introduced non- and minimally invasive solutions. However, we developed a system that automatizes the preceding evaluations in a non-contact way using deep learning algorithms. In this work, we provide a Gated Recurrent Unit (GRU)-stack-based solution that works on a dynamic feature set generated by computer vision methods from the cameras’ video feed and patient monitor to classify the activity phases of infants adapted from the NIDCAP (Newborn Individualized Developmental Care Program) scale. We also show how pulse rate variability (PRV) data could improve the performance of the classification. The network was trained and evaluated on our own database of 108 h collected at the Neonatal Intensive Care Unit, Dept. of Neonatology of Pediatrics, Semmelweis University, Budapest, Hungary. 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

Millimeter-wave (mmWave) sensing has emerged as a promising technology for non-contact health monitoring, offering high spatial resolution, material sensitivity, and integration potential with wireless platforms. While prior work has focused on specific applications or signal processing methods, a unified understanding of how mmWave signals map to clinically relevant biomarkers remains lacking. This survey presents a full-stack review of mmWave-based medical sensing systems, encompassing signal acquisition, physical feature extraction, modeling strategies, and potential medical and healthcare uses. We introduce a taxonomy that decouples low-level mmWave signal features—such as motion, material property, and structure—from high-level biomedical biomarkers, including respiration pattern, heart rate, tissue hydration, and gait. We then classify and contrast the modeling approaches—ranging from physics-driven analytical models to machine learning techniques—that enable this mapping. Furthermore, we analyze representative studies across vital signs monitoring, cardiovascular assessment, wound evaluation, and neuro-motor disorders. By bridging wireless sensing and medical interpretation, this work offers a structured reference for designing next-generation mmWave health monitoring systems. We conclude by discussing open challenges, including model interpretability, clinical validation, and multimodal integration. Full article