Search Results (178)

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

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

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

Background and Objectives: Chronic wounds pose a significant healthcare burden due to their prolonged healing times and susceptibility to infection. Electric field (EF)-enabled smart bandages offer a promising solution by combining therapeutic stimulation with real-time physiological monitoring. Materials and Methods: This study assessed a smart bandage integrating spiral stainless steel electrodes delivering a 200 millivolts per millimeter (mV/mm) EF for 5 h daily over 14 days to full-thickness excisional wounds in 100 Sprague–Dawley rats. Vital signs including heart rate (BPM), oxygen saturation (SpO₂), and temperature were monitored continuously. Machine learning models were trained on these data to predict wound healing status. Results: By Day 7, EF-treated wounds demonstrated significantly faster healing, achieving an average wound closure rate of 82.0% ± 2.1% compared to 70.75% ± 2.3% in the control group (p < 0.05). By Day 14, wounds in the experimental group had significantly reduced to 0.01 ± 0.005 cm², while the control group retained a wound size of 0.24 ± 0.03 cm² (p < 0.05). Histological analysis revealed enhanced neovascularization, collagen alignment, and epithelial regeneration in the EF group. Physiological data showed no systemic inflammatory response. Predictive modeling using XGBoost and Random Forest achieved >98% accuracy, with SHAP (SHapley Additive exPlanations) analysis identifying EF exposure and treatment duration as key predictors. Conclusions: The findings demonstrate that EF-based smart bandages significantly enhance wound healing and enable highly accurate prediction of outcomes through machine learning models. This bioelectronic approach holds strong potential for clinical translation. Full article

In the context of Doppler radar, studies have examined the changes in the phase shift of the S₂₁ transmission coefficient related to minute movements of the human chest as a response to breathing or heartbeat. Detecting human vital signs remains a challenge, especially when obstacles interfere with the attempt to detect the presence of life. The sensitivity of a measurement system’s perception of vital signs is highly dependent on the monitoring systems and antennas that are used. The current work proposes a computational approach that aims to extract an empirical law of the dependence of the phase shift of the transmission coefficient (S₂₁) on the sensitivity at reception, based upon a set of four parameters. These variables are as follows: (a) the frequency of the continuous wave utilized; (b) the antenna type and its gain/directivity; (c) the electric field strength distribution on the chest surface (and its average value); and (d) the type of material (dielectric properties) impacted by the incident wave. The investigated frequency range is (1–20) GHz, while the simulations are generated using a doublet of dipole or gain-convenient identical Yagi antennas. The chest surface is represented by a planar rectangle that moves along a path of only 3 mm, with a step of 0.3 mm, mimicking respiration movement. The antenna–target system is modeled in the computational space in each new situation considered. The statistics illustrate the multiple regression function, empirically extracted. This enables the subsequent building of a continuous-wave bio-radar Doppler system with controlled and improved sensitivity. Full article

Technological advances continually reduce the effort to digitally transform health-related activities such as rehabilitation and training. Exemplary systems use tracking and vital sign monitoring to assess physical condition and training progress. This paper presents a system for body stability training and coordination evaluation, using cost-efficient tracking and monitoring solutions. It implements the use case of app-guided back posture tracking on the ICAROS Pro training device via SteamVR Tracking 2.0, with pulse and respiration rate monitoring via Zephyr BioHarness 3.0. A longitudinal study on training effects with 20 subjects was conducted, involving a representative procedure created with a sports manager. Posture errors served as the main progress indicator, and pulse and respiration rates as co-indicators. Outcomes suggest the system’s capabilities to foster comprehension of effects and steering of exercises. Further, a secondary study presents a self-developed VR-based exergame demo for future system expansion. The Empatica EmbracePlus smartwatch was used as an alternative for vital sign acquisition. The user experiences of five subjects gathered via a survey highlight its motivating and entertaining character. For both the main and secondary studies, a thorough discussion elaborates on potentials and current limitations. The developed training system can serve as template and be adjusted for further use cases, and the exergame’s reception revealed prospective extension directions. Software components are available via GitHub. 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: 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

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

The increasing demand for personalized healthcare, particularly among individuals requiring continuous health monitoring, has driven significant advancements in sensor technology. Wearable, non-continuous monitoring, and non-contact sensors are leading this innovation, providing novel methods for monitoring vital signs and physiological data in both clinical and home settings. However, there is a lack of comprehensive comparative studies assessing the overall functionality of these technologies. This paper aims to address this gap by presenting a detailed comparative analysis of selected wearable, non-continuous monitoring, and non-contact sensors used for health monitoring. To achieve this, we conducted a comprehensive evaluation of various sensors available on the market, utilizing key indicators such as sensor performance, usability, associated platforms functionality, data management, battery efficiency, and cost-effectiveness. Our findings highlight the strengths and limitations of each sensor type, thus offering valuable insights for the selection of the most appropriate technology based on specific healthcare needs. This study has the potential to serve as a valuable resource for researchers, healthcare providers, and policymakers, contributing to a deeper understanding of existing user-centered health monitoring solutions. Full article

Invisible health monitoring is currently a topic of global interest within the scientific community. Sensorization of everyday objects can provide valuable health information without requiring any changes in people’s routines. In this work, a feasibility study of photoplethysmography (PPG) acquisition in the lower limbs for continuous and real-time monitoring of the vital signs, including heart rate (HR) and respiratory rate (RR), is presented. The proposed system uses two MAX30102 sensors to obtain PPG signals from the back of the thigh. As proof of concept, tests were conducted in 17 volunteers (age group between 22 and 40 years old, twelve females and five males), and the results were compared to those of reference sensors. A Pearson correlation coefficient of r = 0.92 and r = 0.77 and a mean difference of 1.2 bpm and 0.9 rpm for HR and RR, respectively, were obtained between the developed system and reference. System accuracies of 95.9% for HR and 91.3% for RR were achieved, showing the system viability for vital sign monitoring of the lower limbs. Full article

Continuous monitoring on the general ward leads to more and earlier interventions to prevent clinical deterioration. These clinical actions influence outcomes and may serve as an indicator of impending deterioration. This study aims to correlate clinical actions with clinical endpoints and deviating vital signs. Methods: This cohort study prospectively charted all patients undergoing continuous vital sign monitoring on a gastro-intestinal and oncological surgery, and an internal ward of an academic hospital in The Netherlands from 1 August 2018 till 31 July 2019 (METC 2018-4330, NCT04189653). Clinical actions recorded in electronic medical records were analyzed to assess correlations with patient outcomes, hospital length of stay, and alarming monitoring minutes. Results: A total of 1529 patients were included, of which 68 patients had a negative clinical endpoint. There were 2749 clinical actions recorded. Clinical actions correlated to negative clinical endpoints (ρ = 0.259; p < 0.001, OR: 3.4 to 79.5) and to the length of stay (ρ = 0.560; p < 0.001). Vital sign deviations correlated with clinical actions (ρ = 0.025–0.056; p < 0.001–p = 0.018). In the last 72 h before a clinical endpoint, for alarming minutes, this correlation with clinical actions was more pronounced (ρ = 0.340, p < 0.001). Conclusions: Predefined clinical actions performed on admitted general ward patients correlated with negative endpoints, an increased length of stay, and with deviating vital signs, especially in the period directly preceding severe deterioration. Clinical actions have potential as an intermediate measurement of deterioration. Full article