Search Results (102)

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

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. 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

(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

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

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

Flexible pressure sensors play a significant role in wearable electronics, human–machine interfaces, and health monitoring, and improving their performance has always been a major focus of research. Various microstructures have been proposed to enhance sensitivity, particularly when tilted. However, unidirectional tilting may create a shift in contact surfaces, reducing accuracy in pressure detection. To address these limitations, this study introduces a capacitive pressure sensor with a staggered tilted column microstructure, inspired by the elaborate network of epidermis and dermis layers within human skin. The simulation and experiment results reveal that the developed sensor has high sensitivity and responds rapidly to applied forces, making it suitable for real-time applications. Demonstrations of gesture recognition and physiological monitoring highlight its practical potential. These findings underscore the effectiveness of the staggered microstructure in improving sensor performance and its applicability in next-generation flexible sensors. Full article

This study evaluated the application of the BCG Sense1 wearable device for monitoring the heart rate (HR) and the respiratory rate (RR) in dogs, comparing its performance to the gold standard ECG under awake and anesthetized conditions. Data were collected from twelve dogs, with six awake beagles and six anesthetized client-owned dogs. Bland–Altman analysis and linear regression revealed strong correlations between BCG and ECG under both awake and anesthetized conditions (HR: r = 0.97, R² = 0.94; RR: r = 0.78, R² = 0.61, and p < 0.001). While slight irregularities were noted in respiratory rate measurements in both groups, potentially affecting the concordance between methods, BCG maintained a significant correlation with ECG under anesthesia (HR: r = 0.96, R² = 0.92; RR: r = 0.85, R² = 0.72, and p < 0.01). The wearable BCG-Sense 1 sensor enables continuous monitoring over 24 h, while ECG serves as the gold standard reference. These findings prove that BCG can be a good alternative to ECG for the monitoring of vital signs in clinical, perioperative, intraoperative, and postoperative settings. The strong correlation between the BCG and ECG signals in awake and anesthetized states highlights the prospects of BCG technology as a revolutionary method in veterinary medicine. As a non-invasive and real-time monitoring system, the BCG Sense1 device strengthens clinical diagnosis and reduces physiological variations induced by stress. Full article

Millimeter-wave radar is emerging as a key sensor technology not only for autonomous driving but also for various industrial applications, such as vital sign monitoring and structural displacement sensing using millimeter-wave FMCW radar, which must detect extremely small displacements on the sub-micron scale. Accurate displacement measurements fundamentally rely on obtaining precise intermediate frequency (IF) phase data over slow time (i.e., chirp-to-chirp intervals or pulse repetition time) generated by the radar sensor system. In this study, we developed a millimeter-wave FMCW radar sensor for displacement sensing using a 77–81 GHz radar transceiver MMIC (Monolithic Microwave Integrated Circuit) and evaluated its accuracy and precision through a series of experiments. First, we assessed the MMIC’s phase performance under static conditions using a rigid RF waveguide, and second, we measured a vibrating target using an industrial vibration shaker as a reference. The experiments demonstrated a maximum accuracy error of +0.359 degrees (1.907 μm displacement) and a maximum 3-sigma precision of ±0.358 degrees (±1.180 μm displacement), validating the feasibility of using millimeter-wave radar to measure very small displacements. Full article

Monitoring for early symptoms is a critical step in preventing pneumoconiosis. The early signs of pneumoconiosis can be characterized by dyspnea, tachypnea, and cough. While traditional sensor-based methods are promising, they necessitate the wearing of devices and confine human physical movements. On the other hand, camera-based methods have issues related to illumination, obstruction, and privacy. Recently, wireless sensing has attracted a significant amount of research attention. Among wireless signals, acoustic signals possess unique advantages for fine-grained sensing due to their low propagation speed in the air and low hardware requirement. In this paper, we propose a system called $P^{3}Warning$ to realize low-cost warnings for potential pneumoconiosis patients in a contactless manner. For the first time, the designed system utilizes the inaudible acoustic signal to monitor early symptoms of pneumoconiosis (i.e., abnormal respiration and cough), leveraging a pair of commercial speaker and microphone. We introduce and address unique technical challenges, such as formulating a delay elimination method to synchronize transceiver signals and providing a search-based strategy to amplify signal variation for accurate and long-distance vital sign sensing. Ultimately, we apply an innovative signal decomposition technique to reconstruct the respiration waveform and extract features for cough detection. Comprehensive experiments were conducted to evaluate $P^{3}Warning$. Experiment results show that it can achieve a robust performance with a median error of 0.39 bpm for abnormal respiration pattern monitoring and an accuracy of 95% for cough detection in total, and support the furthest sensing range of up to 4 m. Full article

Emerging radar sensing technology is revolutionizing cardiovascular monitoring by eliminating direct skin contact. This approach captures vital signs through electromagnetic wave reflections, enabling contactless blood pressure (BP) tracking while maintaining user comfort and privacy. We present a hierarchical neural framework that synergizes spatial and temporal feature learning for radar-driven, contactless BP monitoring. By employing advanced preprocessing techniques, the system captures subtle chest wall vibrations and their second-order derivatives, feeding dual-channel inputs into a hierarchical neural network. Specifically, Stage 1 deploys convolutional depth-adjustable lightweight residual blocks to extract spatial features from micro-motion characteristics, while Stage 2 employs a transformer architecture to establish correlations between these spatial features and BP periodic dynamic variations. Drawing on the intrinsic link between systolic (SBP) and diastolic (DBP) blood pressures, early estimates from Stage 2 are used to expand the feature set for the second-stage network, boosting its predictive power. Validation achieved clinically acceptable errors (SBP: −1.09 ± 5.15 mmHg, DBP: −0.26 ± 4.35 mmHg). Notably, this high degree of accuracy, combined with the ability to estimate BP at 2 s intervals, closely approximates real-time, beat-to-beat monitoring, representing a pivotal breakthrough in non-contact BP monitoring. Full article

Purpose: To investigate community-acquired pressure injuries (CAPIs) in older people by utilizing big data. Design: Retrospective data curation and analysis of inpatient data from two general medical centers between 1 January 2016 and 31 December 2018. Methods: Nursing assessments from 44,449 electronic medical records of patients admitted to internal medicine departments were retrieved, organized, coded by data engineers, and analyzed by data scientists. Potential explanatory patient characteristics tested were gender, age, admission indices, nursing assessments including CAPIs, CAPI type and location, vital signs, and the results of lab tests within the first 36 h of admission. Findings: Most CAPIs were located in the buttocks (56.9%), followed by the sacrum (11.8%), ankle (10.8%), trochanter (5.1%), and leg (3.9%). Tissue associated with CAPIs was described as necrotic, serotic, bloody, granolithic, epithelial, and infected. There were 31% of first-degree CAPIs, 41% second-degree, and 18% third-degree. Previously unacknowledged patient characteristics associated with CAPIs are as follows: age, oxygen use, intestinal function, the touch senses of heat and pain, albumin, RDW (red cell distribution width), and systolic blood pressure. Conclusions: The novel indicators for CAPIs underscore the importance of data-driven approaches in detecting and preventing CAPIs in community care. These markers can detect and prevent pressure ulcers in the community, particularly among the elderly. Relevance for Clinical Practice: Nursing management is called upon to integrate information about novel patient characteristics associated with CAPI into clinical practice. Assimilating the insights from this hospital nursing-led study into community nursing will enhance the safety and quality of care for the elderly. Full article

With the compelling popularity of integrated sensing and communication (ISAC), Wi-Fi sensing has drawn increasing attention in recent years. Starting from 2010, Wi-Fi channel state information (CSI)-based wireless sensing has enabled various exciting applications such as indoor localization, target imaging, activity recognition, and vital sign monitoring. In this paper, we retrospect the latest achievements of Wi-Fi sensing using commodity-off-the-shelf (COTS) devices from the past 5 years in detail. Specifically, this paper first presents the background of the CSI signal and related sensing models. Then, recent studies are categorized from two perspectives, i.e., according to their application scenario diversity and the corresponding sensing methodology difference, respectively. Next, this paper points out the challenges faced by Wi-Fi sensing, including domain dependency and sensing range limitation. Finally, three imperative research directions are highlighted, which are critical for realizing more ubiquitous and practical Wi-Fi sensing in real-life applications. Full article

Mobile health technologies enable continuous, quantitative assessment of mobility and gait in real-world environments, facilitating early diagnoses of gait disorders, disease progression monitoring, and prediction of adverse events like falls. Traditionally, mobile gait assessment predominantly relied on body-fixed sensors positioned at the feet or lower trunk. Here, we investigate the potential of an algorithm utilizing an ear-worn motion sensor for spatiotemporal segmentation of gait patterns. We collected 3D acceleration profiles from the ear-worn sensor during varied walking speeds in 53 healthy adults. Temporal convolutional networks were trained to detect stepping sequences and predict spatial relations between steps. The resulting algorithm, mEar, accurately detects initial and final ground contacts (F1 score of 99% and 91%, respectively). It enables the determination of temporal and spatial gait cycle characteristics (among others, stride time and stride length) with good to excellent validity at a precision sufficient to monitor clinically relevant changes in walking speed, stride-to-stride variability, and side asymmetry. This study highlights the ear as a viable site for monitoring gait and proposes its potential integration with in-ear vital-sign monitoring. Such integration offers a practical approach to comprehensive health monitoring and telemedical applications, by integrating multiple sensors in a single anatomical location. Full article