Search Results (92)

Danionella, a transparent freshwater species belonging to the Cyprinidae family, has emerged as a valuable model organism in biological and medical research due to its optical transparency. The cardiovascular system of Danionella larvae provides a unique opportunity for non-invasive heart rate monitoring in aquatic animals. Traditional approaches for evaluating larval heart rate often require manual or semi-automated definition of the cardiac region in video recordings. In this study, we developed a simplified heart rate monitoring system that estimates heartbeat activity by analyzing interframe luminance differences in video sequences of Danionella larvae. Our system successfully measured heart rates in the range of 150–155 beats per minute (bpm), consistent with previous findings reporting rates between 140 and 200 bpm. The non-invasive nature of this method offers significant advantages for high-throughput screening and long-term physiological monitoring. Furthermore, this system has potential applications in evaluating environmental stressors, supporting survival and health assessments, and guiding habitat management strategies to ensure stable populations of adult fish in both natural and laboratory settings. Full article

Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of intelligent healthcare systems. This research presents a comparative analysis of sleep position recognition using two distinct approaches: image-based deep learning and accelerometer-based classification. There are five classes: prone, supine, right side, left side, and wake up. For the image-based method, the Visual Geometry Group 16 (VGG16) convolutional neural network was fine-tuned with data augmentation strategies including rotation, reflection, scaling, and translation to enhance model generalization. The image-based model achieved an overall accuracy of 93.49%, with perfect precision and recall for “right side” and “wakeup” positions, but slightly lower performance for “left side” and “supine” classes. In contrast, the accelerometer-based method employed a feedforward neural network trained on features extracted from segmented accelerometer data, such as signal sum, standard deviation, maximum, and spike count. This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. The results demonstrate that while image-based models are effective, accelerometer-based classification offers higher precision and robustness, particularly in real-time and privacy-sensitive scenarios. Further comparisons of the system characteristics, data size, and training time are also carried out to offer crucial insights for selecting the appropriate technology in clinical, in-home, or embedded healthcare monitoring applications. 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

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

The T-Rehab System delivers a semi-immersive tele-rehabilitation experience by integrating Affective Computing (AC) through facial expression analysis and contactless heartbeat monitoring. T-Rehab closely monitors patients’ mental health as they engage in a personalized, semi-immersive Virtual Reality (VR) game on a desktop PC, using a webcam with MediaPipe to track their hand movements for interactive exercises, allowing the system to tailor treatment content for increased engagement and comfort. T-Rehab’s evaluation comprises two assessments: system performance and cognitive walkthroughs. The first evaluation focuses on system performance, assessing the tested game, middleware, and facial emotion monitoring to ensure hardware compatibility and effective support for AC, gaming, and tele-rehabilitation. The second evaluation uses cognitive walkthroughs to examine usability, identifying potential issues in emotion detection and tele-rehabilitation. Together, these evaluations provide insights into T-Rehab’s functionality, usability, and impact in supporting both physical rehabilitation and emotional well-being. The thorough integration of technology inside T-Rehab ensures a holistic approach to tele-rehabilitation, allowing patients to participate comfortably and efficiently from anywhere. This technique not only improves physical therapy outcomes but also promotes mental resilience, marking an important step advance in tele-rehabilitation practices. Full article

Impaired balance and mental stress are significant health concerns, particularly among older adults. This study investigated the relationship between the heart rate variability and fall risk during daily activities among individuals over 40 years old. This aimed to explore the potential of the heart rate variability as an indicator of stress and balance loss. Data were collected from 14 healthy participants who wore a Polar H10 heart rate monitor and performed Berg Balance Scale activities as part of an assessment of functional balance. Machine learning techniques applied to the collected data included two phases: unsupervised clustering and supervised classification. K-means clustering identified three distinct physiological states based on HRV features, such as the high-frequency band power and the root mean square of successive differences between normal heartbeats, suggesting patterns that may reflect stress levels. In the second phase, integrating the cluster labels obtained from the first phase together with HRV features into machine learning models for fall risk classification, we found that Gradient Boosting performed the best, achieving an accuracy of 95.45%, a precision of 93.10% and a recall of 85.71%. This study demonstrates the feasibility of using the heart rate variability and machine learning to monitor physiological responses associated with stress and fall risks. By highlighting this potential biomarker of autonomic health, the findings contribute to developing real-time monitoring systems that could support fall prevention efforts in everyday settings for older adults. Full article

Arrhythmia detection in electrocardiogram (ECG) signals is essential for monitoring cardiovascular health. Current automated arrhythmia classification methods frequently encounter difficulties in detecting multiple cardiac abnormalities, particularly when dealing with imbalanced datasets. This paper proposes a novel deep learning approach for the detection and classification of arrhythmias in ECG signals using a Hybrid Residual Network (Hybrid ResNet). Our method employs a Hybrid Residual Network architecture that integrates standard convolution, depthwise separable convolution, and residual connections to enhance the feature extraction efficiency and classification accuracy. To guarantee superior input signals, we preprocess the ECG signals by removing baseline drift with a high-pass Butterworth filter, denoising via discrete wavelet transform, and segmenting heartbeat cycles through R-peak detection. Additionally, we rectify the class imbalance in the MIT-BIH Arrhythmia Database by applying the Synthetic Minority Oversampling Technique (SMOTE), therefore enhancing the model’s ability to detect infrequent arrhythmia types. The suggested system achieves a classification accuracy of 99.09% on the MIT-BIH dataset, surpassing conventional convolutional neural networks and other state-of-the-art methodologies. Compared to existing approaches, our strategy exhibits superior effectiveness and robustness in managing diverse irregular heartbeats and arrhythmias. Full article

With the rapid rise in technological advancements, health can be tracked and monitored in multiple ways. Tracking and monitoring healthcare gives the option to give precise interventions to people, enabling them to focus more on healthier lifestyles by minimising health issues concerning long screen time. Artificial Intelligence (AI) techniques like the Large Language Model (LLM) technology enable intelligent smart assistants to be used on mobile devices and in other cases. The proposed system uses the power of IoT and LLMs to create a virtual personal assistant for long-time screen users by monitoring their health parameters, with various sensors for the real-time monitoring of seating posture, heartbeat, stress levels, and the motion tracking of eye movements, etc., to constantly track, give necessary advice, and make sure that their vitals are as expected and within the safety parameters. The intelligent system combines the power of AI and Natural Language Processing (NLP) to build a virtual assistant embedded into the screens of mobile devices, laptops, desktops, and other screen devices, which employees across various workspaces use. The intelligent screen, with the integration of multiple sensors, tracks and monitors the users’ vitals along with various other necessary health parameters, and alerts them to take breaks, have water, and refresh, ensuring that the users stay healthy while using the system for work. These systems also suggest necessary exercises for the eyes, head, and other body parts. The proposed smart system is supported by user recognition to identify the current user and suggest advisory actions accordingly. The system also adapts and ensures that the users enjoy proper relaxation and focus when using the system, providing a flexible and personalised experience. The intelligent screen system monitors and improves the health of employees who have to work for a long time, thereby enhancing the productivity and concentration of employees in various organisations. Full article

The COVID-19 epidemic has raised awareness of exactly how crucial it is to continuously observe issues and diagnose respiratory problems early. Although the respiratory system is the primary objective of the disease’s acute phase, subsequent complications of SARS-CoV-2 infection might trigger enduring respiratory problems and symptoms, according to new research. These signs and symptoms, which collectively inflict considerable strain on healthcare systems and people’s quality of life, comprise, but are not restricted to, congestion, shortage of breath, tightness in the chest, and a decrease in lung function. Wearable technology offers a promising remedy to this persistent issue by offering continuous respiratory parameter monitoring, facilitating the early control and intervention of post-COVID-19 issues with respiration. In an effort to enhance patient outcomes and reduce expenses related to healthcare, this paper examines the possibility of using wearable technology to provide remote surveillance and the early diagnosis of respiratory problems in individuals suffering from COVID-19. In this work, a fog computing-based cost-effective smart health monitoring device is proposed for infectious disease applications. Further, the proposed device consists of three different biosensor modules, namely a MAX90614 infrared temperature sensor, a MAX30100 pulse oximeter, and a microphone sensor. All these sensor modules are connected to a fog computing device, namely a Raspberry PI microcontroller. Also, three different sensor modules were integrated with the Raspberry PI microcontroller and individuals’ physiological parameters, such as oxygen saturation (SPO2), heartbeat rate, and cough sounds, were recorded by the computing device. Additionally, a convolutional neural network (CNN)-based deep learning algorithm was coded inside the Raspberry PI and was trained with normal and COVID-19 cough sounds from the KAGGLE database. This work appears to be of high clinical significance since the developed fog computing-based smart health monitoring device is capable of identifying the presence of infectious disease through individual physiological parameters. Full article

Background: Heart rate variability (HRV) is the fluctuation in the time intervals between adjacent heartbeats. HRV is a measure of neurocardiac function that is produced by dynamic autonomic nervous system (ANS) processes and is a simple measure that estimates cardiac autonomic modulation. Methods: The study included 108 patients admitted to the Coronary Intensive Care Unit with acute myocardial infarction (AMI) who did not undergo primary percutaneous transluminal coronary angioplasty (PTCA) or systemic thrombolysis and followed conservative management. All patients underwent detailed clinical, biological, and paraclinical assessments, including evaluation of HRV parameters and echocardiographic measurements. The analysis of RR variability in both time and frequency domains indicates that the negative prognosis of patients with AMI is associated with an overall imbalance in the neuro-vegetative system. The HRV parameters were acquired using continuous 24 h electrocardiogram (ECG) monitoring at a baseline, after 1 month, and 6 months. Results: Our analysis reveals correlations between alterations in HRV parameters and the increased risk of adverse events and mortality after AMI. The study found a significant improvement in HRV parameters over time, indicating better autonomic regulation post-AMI. The standard deviation of all RR intervals (SDNN) increased significantly from baseline (median 75.3 ms, IQR 48.2–100) to 1 month (median 87 ms, IQR 55.7–111) and further to 6 months (median 94.2 ms, IQR 67.6–118) (p < 0.001 for both comparisons). The root mean square of successive difference of RR (RMSSD) also showed significant increases at each time point, from baseline (median 27 ms, IQR 22–33) to 1 month (median 30.5 ms, IQR 27–38) and from 1 month to 6 months (median 35 ms, IQR 30–42) (p < 0.001 for all comparisons), indicating enhanced parasympathetic activity. Moreover, changes in HRV parameters have been associated with impaired left ventricle ejection fraction (LVEF) and global longitudinal strain (GLS), indicating a relationship between autonomic dysfunction and myocardial deformation. GLS values improved from a baseline median of −11% (IQR 5%) to −13% (IQR 4%) at 6 months (p < 0.001), reflecting better myocardial function. Conclusions: HRV parameters and cardiac performance analysis, especially using GLS, offer a solid framework for evaluating recovery and predicting adverse outcomes post-MI. Full article

(1) Objective: This study aims to lay a foundation for noncontact intensive care monitoring of premature babies. (2) Methods: Arterial oxygen saturation and heart rate were measured using a monochrome camera and time-division multiplex controlled lighting at three different wavelengths (660 nm, 810 nm and 940 nm) on a piglet model. (3) Results: Using this camera system and our newly designed algorithm for further analysis, the detection of a heartbeat and the calculation of oxygen saturation were evaluated. In motionless individuals, heartbeat and respiration were separated clearly during light breathing and with only minor intervention. In this case, the mean difference between noncontact and contact saturation measurements was 0.7% (RMSE = 3.8%, MAE = 2.93%). (4) Conclusions: The new sensor was proven effective under ideal animal experimental conditions. The results allow a systematic improvement for the further development of contactless vital sign monitoring systems. The results presented here are a major step towards the development of an incubator with noncontact sensor systems for use in the neonatal intensive care unit. Full article

This paper presents a real-time and non-contact dual-mode embedded impulse-radio (IR) ultra-wideband (UWB) radar system designed for microwave imaging and vital sign applications. The system is fully customized and composed of three main components, an RF front-end transmission block, an analog signal processing (ASP) block, and a digital processing block, which are integrated in an embedded system. The ASP block enables dual-path receiving for image construction and vital sign detection, while the digital part deals with the inverse scattering and direct current (DC) offset issues. The self-calibration technique is also incorporated into the algorithm to adjust the DC level of each antenna for DC offset compensation. The experimental results demonstrate that the IR-UWB radar, based on the proposed algorithm, successfully detected the 2D image profile of the object as confirmed by numerical derivation. In addition, the radar can wirelessly monitor vital sign behavior such as respiration and heartbeat information. Full article

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy. Full article

Glucose is a simple sugar molecule. The chemical formula of this sugar molecule is C₆H₁₂O₆. This means that the glucose molecule contains six carbon atoms (C), twelve hydrogen atoms (H), and six oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally, after eating or drinking, our bodies break down the sugars in food and use them to obtain energy for our cells. To execute this process, our pancreas produces insulin. Insulin “pulls” sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas cannot produce enough insulin. As a result, the level of glucose in their blood rises. This can lead to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack, damage to blood vessels, etc. In this study, a non-invasive and therefore easily usable method for monitoring blood glucose was developed. With the experiment carried out, it was possible to measure glucose levels continuously, thus eliminating the disadvantages of invasive systems. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in blood; these sensors have a wavelength of 940 nm. The sensor was placed on a small black parallelepiped-shaped box on the tip of the finger and the output of the optical sensor was then connected to a microcontroller at the analogue input. Another sensor used, but only to provide more medical information, was the heartbeat sensor, inserted into an armband (along with the microprocessor). After processing and linear regression analysis, the glucose level was predicted, and data were sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods (commercial products). The hardware consisted of a microcontroller, a near-IR optical sensor, a heartbeat sensor, and a Bluetooth module. Another objective of this experiment using low-cost and low-power hardware was to not carry out complex processing of data from the sensors. Our practical laboratory experiment resulted in an error of 2.86 per cent when compared to a commercial product, with a hardware cost of EUR 8 and a consumption of 50 mA. Full article

Thyroid hormones have a pivotal role in controlling metabolic processes, cardiovascular function, and autonomic nervous system activity. Hypothyroidism, a prevalent endocrine illness marked by inadequate production of thyroid hormone, has been linked to different cardiovascular abnormalities, including alterations in heart rate variability (HRV). The study included 110 patients with hypothyroid disorder. Participants underwent clinical assessments, including thyroid function tests and HRV analysis. HRV, a measure of the variation in time intervals between heartbeats, serves as an indicator of autonomic nervous system activity and cardiovascular health. The HRV values were acquired using continuous 24-h electrocardiogram (ECG) monitoring in individuals with hypothyroidism, as well as after a treatment period of 3 months. All patients exhibited cardiovascular symptoms like palpitations or fatigue but showed no discernible cardiac pathology or other conditions associated with cardiac disease. The findings of our study demonstrate associations between hypothyroidism and alterations in heart rate variability (HRV) parameters. These results illustrate the possible influence of thyroid dysfunction on the regulation of cardiac autonomic function. Full article