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(1) Background: This study utilized a 24-week intervention incorporating heart rate sensors for real-time monitoring of intervention training, aiming to comprehensively assess the effects of Tai Chi on vascular endothelial function, atherosclerosis progression, and lipid metabolism. The insights gained may inform personalized non-pharmacological interventions to enhance the management of cardiovascular health in this population to provide sustainable benefits and improve quality of life. (2) Methods: Forty postmenopausal middle-aged and elderly women were randomly assigned to an exercise or control group. The exercise group underwent a 24-week Tai Chi (BaFa WuBu) training intervention with real-time heart rate monitoring using Polar sensors. Pre- and post-intervention assessments included body composition, blood pressure, vascularity, and blood parameters measured with the Inbody 720, Vascular Endothelial Function Detector, and Arteriosclerosis. Data were analyzed using SPSS 26.0 and mixed-design ANOVA to assess the effects of time, group, and their interactions on study outcomes. (3) Results: After training through 24 weeks of Tai Chi (BaFa WuBu) intervention, compared with the control group, systolic blood pressure in the exercise group was significantly lower (p < 0.05), and the difference between left and right arm pulse pressure, left and right ankle mean arterial pressure, left and right side baPWV, left and right side ABI, TC, TG, LDL, and blood pressure viscosity were all very significantly lower (p < 0.01), and the diastolic blood pressure was significantly higher (p < 0.05). Compared with baseline values in the exercise group, systolic blood pressure, right and left arm pulse pressure difference, right and left ankle mean arterial pressure, right and left side baPWV, right and left side ABI, TC, TG, LDL, and blood pressure viscosity decreased very significantly (p < 0.01) and diastolic blood pressure and FMD increased very significantly (p < 0.01) in the exercise group after the intervention. (4) Conclusions: In our study, a 24-week Tai Chi (BaFa WuBu) program significantly improved vascular health in middle-aged and older postmenopausal women. This simplified Tai Chi form is gentle and effective, ideal for older adults. Regular practice led to reduced vascular obstruction, improved lipid metabolism, and enhanced vascular endothelial function, crucial for preventing vascular diseases. The real-time heart rate sensors used were pivotal, enabling precise monitoring and adjustment of exercise intensity, thereby enhancing the study's scientific rigor and supporting Tai Chi (BaFa WuBu) as a beneficial therapeutic exercise. [ABSTRACT FROM AUTHOR]

The noninvasive measurement and sensing of vital bio signs, such as respiration and cardiopulmonary parameters, has become an essential part of the evaluation of a patient's physiological condition. The demand for new technologies that facilitate remote and noninvasive techniques for such measurements continues to grow. While previous research has made strides in the continuous monitoring of vital bio signs using lasers, this paper introduces a novel technique for remote noncontact measurements based on radio frequencies. Unlike laser-based methods, this innovative approach offers the advantage of penetrating through walls and tissues, enabling the measurement of respiration and heart rate. Our method, diverging from traditional radar systems, introduces a unique sensing concept that enables the detection of micro-movements in all directions, including those parallel to the antenna surface. The main goal of this work is to present a novel, simple, and cost-effective measurement tool capable of indicating changes in a subject's condition. By leveraging the unique properties of radio frequencies, this technique allows for the noninvasive monitoring of vital bio signs without the need for physical contact or invasive procedures. Moreover, the ability to penetrate barriers such as walls and tissues opens new possibilities for remote monitoring in various settings, including home healthcare, hospital environments, and even search and rescue operations. In order to validate the effectiveness of this technique, a series of experiments were conducted using a prototype device. The results demonstrated the feasibility of accurately measuring respiration patterns and heart rate remotely, showcasing the potential for real-time monitoring of a patient's physiological parameters. Furthermore, the simplicity and low-cost nature of the proposed measurement tool make it accessible to a wide range of users, including healthcare professionals, caregivers, and individuals seeking to monitor their own health. [ABSTRACT FROM AUTHOR]

During peak height velocity, adjusting training intensity is crucial for optimizing performance and minimizing injury risk. This cross-sectional study compares external and internal intensities in different training tasks (analytical tasks, small-sided games, and training matches) and analyzes their effect on the maturation age of young players. Fifty-five U-15 and U-16 boys from two soccer clubs in southwestern Spain were monitored using inertial movement units and heart rate monitors to report training intensities. Anthropometric data and birthdates were collected to estimate maturation age. The Friedman test and Durbin–Conover post hoc test identified specific differences between groups, and Spearman's rank correlation coefficients assessed variable impacts. Training matches showed significantly higher distance covered, maximum and average speed, and average heart rate compared to small-sided games and analytical tasks. High-intensity actions and sprints were significantly higher (p < 0.0001) during training matches compared to analytical tasks and during small-sided games compared to analytical tasks. Player load per minute was significantly highest (p < 0.05) during training matches, followed by small-sided games, and lowest in analytical tasks. Positive correlations between maturational age and high-intensity actions, accelerations, and decelerations indicated higher intensity (p < 0.05) in more mature players. A negative correlation between player load per minute and maturational age suggested more efficient intensity management in mature players. These findings highlight the importance of considering biological maturation and training task variability in youth athletes' development. [ABSTRACT FROM AUTHOR]

The monitoring of hemodynamic parameters, such as heart rate and blood pressure, provides valuable indications of overall cardiovascular health. It is preferable that such monitoring is non-invasive and in real time via an affordable, compact and small-scale device for maximum convenience. Numerous literature sources have exploited derivations of these parameters from photoplethysmogram (PPG) and electrical bioimpedance (EBI) signal measurements through the use of calculation algorithms of varying complexity. Compared to electrocardiogram (ECG), these measurement techniques have a merit of well-established practices of designing a wearable device that could conveniently be put on a wrist. The current paper provides a comprehensive review on the use of PPG and EBI measurement techniques in the context of hemodynamic parameter monitoring using a wearable device. A special emphasis is placed on the most basic hemodynamic parameter—heart rate—describing different algorithms of heart rate detection and monitoring. The last section provides an overview of commercially available and in-home wearable device technologies based on PPG and EBI measurements, their design challenges, and future prospects. [ABSTRACT FROM AUTHOR]

Enhancing road safety by monitoring a driver's physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, the system can detect early signs of drowsiness and sudden medical emergencies, such as heart attacks, thereby preventing accidents. This is particularly vital for fully self-driving (FSD) systems, as it ensures control is not transferred to an impaired driver. The proposed system employs a 60 GHz frequency-modulated continuous wave (FMCW) radar placed behind the driver's seat. This article mainly describes how advanced signal processing methods, including the Huber–Kalman filtering algorithm, are applied to mitigate the impact of respiration on heart rate detection. Additionally, the autocorrelation algorithm enables fast detection of vital signs. Intensive experiments demonstrate the system's effectiveness in accurately monitoring HRV, highlighting its potential to enhance safety and reliability in both traditional and autonomous driving environments. [ABSTRACT FROM AUTHOR]

The rising prevalence of mental illness is straining global mental health systems, particularly affecting older adults who often face deteriorating physical health and decreased autonomy and quality of life. Early detection and targeted rehabilitation are crucial in mitigating these challenges. Mindfulness acceptance and commitment therapy (ACT) holds promise for enhancing motivation and well-being among the elderly, although delivering such psychological interventions is hindered by limited access to services, prompting exploration of remote delivery options like mobile applications. In this paper, we introduce the BrainHeart App (v.1.1.8), a mobile application tailored to improve physical and mental well-being in seniors. The app features a 10-day ACT program and other sections promoting healthy lifestyle. In a pilot study involving twenty participants, individuals engaged in daily mental exercises for 10 days using the app. Clinical evaluations, including assessments of psychological flexibility, overall cognitive profile, mindfulness disposition, cognitive fusion, and heart rate collected with Polar H10, were conducted at baseline (T0) and one month post-intervention (T1). Analysis revealed significant improvements in almost all neuropsychological scores, with high usability reported (system usability scale average score: 82.3 ± 9.31). Additionally, a negative correlation was found between usability and experiential avoidance (r = −0.51; p = 0.026), and a notable difference in heart rate was observed between baseline and post-intervention (F-value = 3.06; p-value = 0.09). These findings suggest that mindfulness-ACT exercises delivered via the BrainHeart App can enhance the well-being of elderly individuals, highlighting the potential of remote interventions in addressing mental health needs in this population. [ABSTRACT FROM AUTHOR]

Cerebral palsy is a disorder of central motor and postural development, resulting in limited mobility. Cerebral palsy is often accompanied by cognitive impairment and abnormal behavior, significantly impacting individuals and society. Time, energy, and economic investment in the rehabilitation process is substantial, yet the rehabilitation outcomes often remain unsatisfactory. Additionally, some patients have limited sensory perception during rehabilitation training, making it challenging to effectively regulate exercise intensity. Traditional evaluation methods are mostly based on recovery performance, lack guidance at the neurophysiological level, and have an unequal distribution of medical rehabilitation resources, which pose great challenges to the rehabilitation of patients. Based on the issues mentioned above, this paper proposes a real-time cerebral signal monitoring system based on wearable devices. This system can monitor and store blood oxygen, heart rate, myoelectric, and EEG signals during cerebral palsy rehabilitation, and it can track and monitor signals during the rehabilitation treatment process. The system includes two parts: hardware design and software design. The hardware design includes a data signal acquisition module, a main control chip (ESP32), a muscle electrical sensor module, a brain electrical sensor module, a blood/heart rate acquisition module, etc. It is primarily for real-time signal data acquisition, processing, and uploading to the cloud server. The software design includes functions such as data receiving, data processing, data storage, network configuration, and remote communication and enables the visual monitoring of data signals. The system can achieve real-time monitoring of electromyography, electroencephalography, and blood oxygen levels, as well as the heart rate of patients with cerebral palsy, and adjust rehabilitation training in real-time during the rehabilitation process. At the same time, based on the real-time storage of the original electromyography and electroencephalography data, it can provide auxiliary guidance for later rehabilitation evaluation and effective data support for the entire rehabilitation treatment process. [ABSTRACT FROM AUTHOR]

Long-term patient monitoring is required for detection of episodes of atrial fibrillation, one of the most widespread cardiac pathologies. Today, the most used non-invasive technique is Holter electrocardiographic (ECG) monitoring, which can often prove ineffective because of the short duration of recordings (e.g., one day). Other techniques such as photo-plethysmography are adopted by smartwatches for much longer duration monitoring, but this has the disadvantage of offering only intermittent measurements. This study proposes an Internet of Things (IoT) sensor that can provide a very long period of continuous monitoring. The sensor consists of an ECG-integrated Analog Front End (MAX30003), a microcontroller (STM32F401RE), and an IoT narrowband module (STEVAL-STMODLTE). The instantaneous heart rate is extracted from the ECG recording in real time. At intervals of two minutes, the sequence of inter-beat intervals is transmitted to an IoT cloud platform (ThingSpeak). Settled atrial fibrillation event recognition software runs on the cloud and generates alerts when it recognizes such arrhythmia. Performances of the proposed sensor were evaluated by generating analog ECG signals from a public dataset of ECG signals with atrial fibrillation episodes, the MIT-BIH Atrial Fibrillation Database, each recording lasting approximately 10 h. Software implementing the Lorentz algorithm, one of the best detectors of atrial fibrillation, was implemented on the cloud platform. The accuracy, sensitivity, and specificity in recognizing atrial fibrillation episodes of the proposed system was calculated by comparison with a cardiologist's reference data. Across all patients, the proposed method achieved an accuracy of 0.88, a sensitivity 0.71, and a specificity 0.99. The results obtained suggest that the developed system can continuously record and transmit heart rhythms effectively and efficiently and, in addition, offers considerable performance in recognizing atrial fibrillation episodes in real time. [ABSTRACT FROM AUTHOR]

In the long-term performance development of rhythmic gymnasts aged 16–17, athletes enter a high-performance training phase, marked by increased training loads and preparation for international competitions. This study aimed to (1) provide an overview of methods used to capture external and internal training/competition loads in elite rhythmic gymnasts, and (2) identify measurements of external and internal training/competition loads and their responses during monitored periods. Conducted according to PRISMA guidelines, the systematic review included 6 studies out of the 815 initially identified. The most common methods for calculating external training load were hours or minutes per week. Internal measures varied and included objective methods such as heart rate monitoring and biochemical, hormonal, and hematological assessments from saliva and blood samples. Among subjective methods, session-RPE was most frequently used, along with other questionnaires examining recovery, well-being, sleep, and competition anxiety. The analyzed studies integrated diverse external and internal training load variables, delving into their impact on athlete's biochemical parameters, recovery, and well-being. Pre-competitive and competitive training periods were the focal points of measuring loads. The complex training structure of rhythmic gymnastics can complicate the calculation of training loads. Therefore, more studies are needed to explore the dose-response relationships between training load and training adaptations, fatigue, and recovery. [ABSTRACT FROM AUTHOR]

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method. [ABSTRACT FROM AUTHOR]

Manual wheelchair users (MWUs) are prone to a sedentary life that can negatively affect their physical and cardiovascular health, making regular assessment important to identify appropriate interventions and lifestyle modifications. One mean of assessing MWUs' physical health is the 6 min push test (6MPT), where the user propels themselves as far as they can in six minutes. However, reliance on observer input introduces subjectivity, while limited quantitative data inhibit comprehensive assessment. Incorporating sensors into the 6MPT can address these limitations. Here, ten MWUs performed the 6MPT with additional sensors: two inertial measurement units (IMUs)—one on the wheelchair and one on the wrist together with a heart rate wristwatch. The conventional measurements of distance and laps were recorded by the observer, and the IMU data were used to calculate laps, distance, speed, and cadence. The results demonstrated that the IMU can provide the metrics of the traditional 6MPT with strong significant correlations between calculated laps and observer lap counts (r = 0.947, p < 0.001) and distances (r = 0.970, p < 0.001). Moreover, heart rate during the final minute was significantly correlated with calculated distance (r = 0.762, p = 0.017). Enhanced 6MPT assessment can provide objective, quantitative, and comprehensive data for clinicians to effectively inform interventions in rehabilitation. [ABSTRACT FROM AUTHOR]

Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user's vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%. [ABSTRACT FROM AUTHOR]

The persistence of symptoms following COVID-19 infection represents a significant challenge in healthcare management. During the outbreak, tele-rehabilitation emerged as a new tool to support healthcare structures in providing rehabilitation services. This study assessed the effectiveness and the feasibility of a 3-week home-based motor and respiratory rehabilitation program for individuals with long COVID-19 after traditional rehabilitation. Twenty-three patients completed the program and underwent functional tests at different time points (i.e., baseline, at discharge from in-hospital rehabilitation and after tele-rehabilitation). Motor function was evaluated using the instrumented Six-Minutes Walking Test (i6MWT), with monitored heart rate and oxygen saturation. Additionally, respiratory function was measured via forced vital capacity (FVC) and maximal voluntary ventilation (MVV) tests. Significant improvements (p < 0.05) in motor and respiratory function were observed throughout the intervention, including an 18.3% increase in walked distance from the baseline. The findings suggest that the proposed home-based tele-rehabilitation shows potential in enhancing motor and respiratory function in patients with long COVID. Despite limitations such as the small sample size, lack of control group and the preliminary nature of the outcomes observed, the overall findings seem to support the feasibility of the proposed tele-rehabilitation program in managing long COVID symptoms and promoting functional recovery. Nevertheless, further research is needed to validate these findings and explore tele-rehabilitation's potential in broader and different patient populations [ABSTRACT FROM AUTHOR]

This study introduces a novel approach for analyzing vital signals using continuous-wave (CW) radar, employing an integrated neural network model to overcome the limitations associated with traditional step-by-step signal processing methods. Conventional methods for vital signal monitoring, such as electrocardiograms (ECGs) and sphygmomanometers, require direct contact and impose constraints on specific scenarios. Conversely, our study primarily focused on non-contact measurement techniques, particularly those using CW radar, which is known for its simplicity but faces challenges such as noise interference and complex signal processing. To address these issues, we propose a temporal convolutional network (TCN)-based framework that seamlessly integrates noise removal, demodulation, and fast Fourier transform (FFT) processes into a single neural network. This integration minimizes cumulative errors and processing time, which are common drawbacks of conventional methods. The TCN was trained using a dataset comprising preprocessed in-phase and quadrature (I/Q) signals from the CW radar and corresponding heart rates measured via ECG. The performance of the proposed method was evaluated based on the L1 loss and accuracy against the moving average of the estimated heart rates. The results indicate that the proposed approach has the potential for efficient and accurate non-contact vital signal analysis, opening new avenues in health monitoring and medical research. Additionally, the integration of CW radar and neural networks in our framework offers a robust and scalable solution, enhancing the practicality of non-contact health monitoring systems in diverse environments. This technology can be leveraged in healthcare robots to provide continuous and unobtrusive monitoring of patients' vital signs, enabling timely interventions and improving overall patient care. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring. [ABSTRACT FROM AUTHOR]

Wheelchair basketball (WB) is a sport modality adapted for people with disabilities who present functional classification (FC) according to their mobility, from 1.0 for players with lesser functional capacity up to 4.5 for great mobility and trunk control players. This study aimed to analyze and compare the external load (EL) and the internal load (IL) of the WB players according to their FC in 5 × 5 simulation game tasks. The main objective of this task was to develop a simulated game in which two teams of five players faced each other to resemble the physical demands of an official match. The development of these tasks allowed for the players to learn the different systems of play, and to improve tactical skills. To conduct the study, 12 (male) national professional WB players (years: 30.7 ± 4.82 and WB experience: 5 ± 1.43) participated in the study and were grouped according to FC. WIMU PROTM devices monitored the EL manufactured by RealTrack Systems in Almería, Spain, and to measure the player's IL, GarminTM Heart Rate (HR) bands were used (GarminTM in Olathe, KS, USA). The EL variables were divided as kinematic (distance, explosive distance, acc, dec, max. acc, max. dec, average speed, max. speed) and neuromuscular (player load, impact). The IL variables were average HR, max. HR and %max. HR. The main results show that players with greater functional capacity (FC = 4.0) developed higher values in the IL and neuromuscular variables analyzed (p < 0.05), as well as in some kinematic variables like distance, dec and average speed (p < 0.05). This information is relevant because it helps to personalize the training load based on competitive demands and create a gradual and adaptable training program. This information helps athletes to develop better performance during training and prevent injuries caused by overexertion. [ABSTRACT FROM AUTHOR]

Medieval combat sport is a form of mixed martial art in which combatants engage in fighting using offensive and defensive equipment while dressed in full armor. The sport is considered extremely taxing, making it nearly impossible to maintain the same level of performance. However, this form of sport has not been thoroughly analyzed, and its impact on human physical response is largely unknown. To address this gap, the study reported here aimed to introduce and test a procedure for analyzing human physical responses within the framework of the sport. To accomplish this, two experienced combatants were asked to engage in a series of strikes, performed in the form of a set duel simulating a professional fight competition. The kinematic aspect of the procedure was examined using motion analysis with the help of an IMU suit, while the physiological aspect was evaluated based on blood lactate levels and heart rate measurements. Furthermore, an ergometer test conducted in a laboratory setting aimed to determine the lactate threshold. The duel results showed noticeable decreases in the kinematic aspects of the strikes, such as the velocity of impact, and a dramatic rise in physiological aspects, such as heart rate and blood lactate levels. During the duel sets, the blood lactate surpassed the threshold level, and at the end, the heart rate exceeded the maximum age-related level. Practicing medieval combat sport has been shown to impose an extreme physical load on the bodies of combatants, noticeably affecting their performance levels. [ABSTRACT FROM AUTHOR]

When animals perceive an acute stressor like a predator, they typically undergo a suite of physiological changes that function to improve survival during the encounter, such as elevation in cardiac output, to supply more energy to muscles. If bodily energy is limited, such as by parasites or infections, these functions could become less efficient and lessen host survival. In the aquatic world of microorganisms, individuals can become colonized by other organisms on their surface (epibionts), which could sap energy from their host from their weight, or even compete with the host for food. Here, we tested if one epibiont (a ciliated protozoan, Vorticella spp.) affects its hosts' ability to mount a physiological stress reaction. We collected wild daphnia (Daphnia ambigua) that had varying burdens of these on their bodies and exposed them to a simulated stressor (crushed daphnia, to simulate nearby predation) under a microscope while monitoring for changes in their heart rates in real time. Out of 121 daphnia, those with no Vorticella epibionts showed no meaningful changes in their heart rate after exposure, but those with light or heavy burdens showed immediate elevations (within 5 min). Moreover, the heart rates of heavily burdened daphnia continued to rise for 1.5 h thereafter, to as much as 17% higher than at baseline. These patterns were unexpected, as they suggest that the ciliated epibionts act to elevate their hosts' physiological reaction, rather than dampen it, perhaps by churning the water column around the host, thereby enhancing the chemical alarm cue. The procedures used in this study may be useful for future investigations into the acute stress reactions of daphnia or other microorganisms. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz's ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves A C C r o b u s t scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model's adversarial robustness while maintaining a high accuracy rate. [ABSTRACT FROM AUTHOR]

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart. [ABSTRACT FROM AUTHOR]

The aim of the study was to examine the association between cardiorespiratory and metabolic parameters and match running performance (MRP) in highly trained football players. The sample of participants consisted of 41 national-level football players (aged 23.20 ± 3.40 years, body height 182.00 ± 5.15 cm, and body mass 76.86 ± 6.06 kg) from the Serbian Super league. For the purposes of this research, the following measurements were applied. A maximal multistage progressive treadmill test, with a direct measurement of maximal oxygen consumption (VO2max) (using Fitmate MED, Cosmed, Rome, Italy) was conducted, alongside continuous heart rate monitoring. Capillary blood samples were taken from the hyperemic area using specific test strips, and, after sample collection, lactate concentration was immediately determined using a lactate analyzer. MRP variables were analyzed according to the BioIRC model of motion structure analysis, based on existing standards for profiling movement intensity. The results of multiple regression analysis indicated an association between cardiac parameters and total distance (R2 = 54.3%, p = 0.000), high-speed running (R2 = 46.4%, p = 0.000), and jogging (R2 = 33.6%, p = 0.004). Regression analysis revealed an association between cardiorespiratory parameters and total distance (R2 = 24.8%, p = 0.014), and high-speed running (R2 = 20%, p = 0.039). Meanwhile, no association was found between lactate concentration and running performance. The explanation for these regression analysis results is based on the observation that functional abilities represent significant potential for expressing movement performance, a crucial condition for success in football. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform ( C W T ) analysis. In this way, leveraging the radar's on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of − 44.8 to + 44.8 deg. In the Bland–Altman plot, the measured results indicate the average difference of − 1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative non-contact heart rate monitoring method based on Wi-Fi Channel State Information (CSI). This approach integrates both amplitude and phase information of the CSI signal through rotational projection, aiming to optimize the accuracy of heart rate estimation in home environments. We develop a frequency domain subcarrier selection algorithm based on Heartbeat to subcomponent ratio (HSR) and design a complete set of signal filtering and subcarrier selection processes to further enhance the accuracy of heart rate estimation. Heart rate estimation is conducted by combining the peak frequencies of multiple subcarriers. Extensive experimental validations demonstrate that our method exhibits exceptional performance under various environmental conditions. The experimental results show that our subcarrier selection method for heart rate estimation achieves an average accuracy of 96.8%, with a median error of only 0.8 bpm, representing an approximately 20% performance improvement over existing technologies. [ABSTRACT FROM AUTHOR]

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value. [ABSTRACT FROM AUTHOR]

This document discusses the advancements in wearable sensing devices and technology. It presents a collection of papers that address various aspects of wearable technology, including authentication and security, localization in wireless sensor networks, sign language recognition, healthcare systems, enhancing user experience, and positioning and movement analysis. The document also highlights future research directions in the field, such as interdisciplinary integration, scalability and miniaturization, enhanced security, personalized healthcare, and improving user experience. Overall, this comprehensive overview provides valuable insights into the current state of wearable sensing devices and technology and sets the stage for future innovations in the field. [Extracted from the article]

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (VX, VY, and VZ) or EASI leads (VES, VAS, and VAI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions. [ABSTRACT FROM AUTHOR]

This short-term survey examined the effect of body part pain on subjective and objective handball performance in Japanese male national handball athletes. Fourteen athletes participated in this study. Assessments of pain in 10 body parts and subjective performance (concentration and satisfaction with body movement) were performed using a visual analog scale from 0 to 10 over four consecutive training days. Monitoring of heart rate and body acceleration during training was also performed to quantify the objective performance. Path analysis and linear mixed modeling were employed to assess the relationship between body pain scores and subjective/objective handball performance. Over the four days of the study period, the body part in which most athletes reported pain was the dominant shoulder (6 of 14 athletes), followed by the dominant knee, the dominant elbow, the dominant ankle joint, and the non-dominant ankle joint (3 of 14 athletes). The path analysis revealed that pain in the dominant elbow negatively correlated with concentration (standardized path coefficient = −0.644, p = 0.00), which was associated with satisfaction with body movement (standardized path coefficient = 0.704, p = 0.00). No significant effect of body pain on objective performance (heart rate and body acceleration) was found among the athletes in this study. The results suggested that the elite athletes were practicing with pain. Even if pain does not physically affect athletes' objective performance, pain in the upper extremities, associated with the primary handball movement of throwing, may reduce the quality of practice by lowering athletes' subjective performance. [ABSTRACT FROM AUTHOR]

Integrating modern and innovative technologies such as the Internet of Things (IoT) and Machine Learning (ML) presents new opportunities in healthcare, especially in medical spa therapies. Once considered palliative, these therapies conducted using mineral/thermal water are now recognized as a targeted and specific therapeutic modality. The peculiarity of these treatments lies in their simplicity of administration, which allows for prolonged treatments, often lasting weeks, with progressive and controlled therapeutic effects. Thanks to new technologies, it will be possible to continuously monitor the patient, both on-site and remotely, increasing the effectiveness of the treatment. In this context, wearable devices, such as smartwatches, facilitate non-invasive monitoring of vital signs by collecting precise data on several key parameters, such as heart rate or blood oxygenation level, and providing a perspective of detailed treatment progress. The constant acquisition of data thanks to the IoT, combined with the advanced analytics of ML technologies, allows for data collection and precise analysis, allowing real-time monitoring and personalized treatment adaptation. This article introduces an IoT-based framework integrated with ML techniques to monitor spa treatments, providing tailored customer management and more effective results. A preliminary experimentation phase was designed and implemented to evaluate the system's performance through evaluation questionnaires. Encouraging preliminary results have shown that the innovative approach can enhance and highlight the therapeutic value of spa therapies and their significant contribution to personalized healthcare. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, patient privacy, signal noise, and long-term safety impede the use of wearable devices in clinical practice. This scoping review aims to assess the performance and safety of novel multi-channel, sensor-based biopotential wearable devices in adults. A comprehensive search strategy was employed on four databases, resulting in 143 records and the inclusion of 12 relevant studies. Most studies focused on healthy adult subjects (n = 6), with some examining controlled groups with atrial fibrillation (AF) (n = 3), long QT syndrome (n = 1), and sleep apnea (n = 1). The investigated bio-sensor devices included chest-worn belts (n = 2), wrist bands (n = 2), adhesive chest strips (n = 2), and wearable textile smart clothes (n = 4). The primary objective of the included studies was to evaluate device performance in terms of accuracy, signal quality, comparability, and visual assessment of ECGs. Safety findings, reported in five articles, indicated no major side effects for long-term/continuous monitoring, with only minor instances of skin irritation. Looking forward, there are ample opportunities to enhance and test these technologies across various physical activity intensities and clinical conditions. [ABSTRACT FROM AUTHOR]

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents. [ABSTRACT FROM AUTHOR]

Technological development has boosted the use of multi-sensor devices to monitor athletes' performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM) could affect the heart rate signal reception (GARMINTM chest strap) and, therefore, data accuracy. Thirty-two physical education students (20 men and 12 women) performed 20 min of exercise in a cycle ergometer based on the warm-up of the Function Threshold Power 20 test in laboratory conditions, carrying two WIMU PROTM devices (Back: inter-scapula; Bicycle: bicycle's handlebar—20 cm from the chest) and two GARMINTM chest straps. A one-dimensional statistical parametric mapping test found full agreement between the two situations (inter-scapula vs. bicycle's handlebar). Excellent intra-class correlation values were obtained during the warm-up (ICC = 0.99, [1.00–1.00], p < 0.001), the time trial test (ICC = 0.99, [1.00–1.00], p < 0.001) and the cool-down (ICC = 0.99, [1.00–1.00], p < 0.001). The Bland–Altman plots confirmed the total agreement with a bias value of 0.00 ± 0.1 bpm. The interscapular back placement of the WIMU PROTM device does not affect heart rate measurement accuracy with a GARMINTM chest strap during cycling exercise in laboratory conditions. [ABSTRACT FROM AUTHOR]

Monitoring key physiological metrics, including heart rate and heart rate variability, has been shown to be of value in exercise science, disease management, and overall health. The purpose of this study was to investigate the diurnal variation of physiological responses and physical performances using digital biomarkers as a precise measurement tool during a walking football match (WFM) in higher-weight men. Nineteen males (mean age: 42.53 ± 12.18 years; BMI: 33.31 ± 4.31 kg·m−2) were engaged in a WFM at two different times of the day. Comprehensive evaluations of physiological parameters (e.g., cardiac autonomic function, lactate, glycemia, and oxygen saturation), along with physical performance, were assessed before, during, and after the match. Overall, there was a significant interaction (time of day x WFM) for mean blood pressure (MBP) (p = 0.007) and glycemia (p = 0.039). Glycemia decreased exclusively in the evening after WFM (p = 0.001), while mean blood pressure did not significantly change. Rating of perceived exertion was significantly higher in the evening than in the morning (p = 0.04), while the heart rate recovery after 1 min (HRR60s) of the match was lower in the evening than in the morning (p = 0.048). Overall, walking football practice seems to be safe, whatever the time of day. Furthermore, HRR60, glycemia, and (MBP) values were lower in the evening compared to the morning, suggesting that evening exercise practice could be safer for individuals with higher weight. The utilization of digital biomarkers for monitoring health status during WFM has been shown to be efficient. [ABSTRACT FROM AUTHOR]

Background: Combination devices to monitor heart rate/rhythms and physical activity are becoming increasingly popular in research and clinical settings. The Zio XT Patch (iRhythm Technologies, San Francisco, CA, USA) is US Food and Drug Administration (FDA)-approved for monitoring heart rhythms, but the validity of its accelerometer for assessing physical activity is unknown. Objective: To validate the accelerometer in the Zio XT Patch for measuring physical activity against the widely-used ActiGraph GT3X. Methods: The Zio XT and ActiGraph wGT3X-BT (Actigraph, Pensacola, FL, USA) were worn simultaneously in two separately-funded ancillary studies to Visit 6 of the Atherosclerosis Risk in Communities (ARIC) Study (2016–2017). Zio XT was worn on the chest and ActiGraph was worn on the hip. Raw accelerometer data were summarized using mean absolute deviation (MAD) for six different epoch lengths (1-min, 5-min, 10-min, 30-min, 1-h, and 2-h). Participants who had ≥3 days of at least 10 h of valid data between 7 a.m–11 p.m were included. Agreement of epoch-level MAD between the two devices was evaluated using correlation and mean squared error (MSE). Results: Among 257 participants (average age: 78.5 ± 4.7 years; 59.1% female), there were strong correlations between MAD values from Zio XT and ActiGraph (average r: 1-min: 0.66, 5-min: 0.90, 10-min: 0.93, 30-min: 0.93, 1-h: 0.89, 2-h: 0.82), with relatively low error values (Average MSE × 106: 1-min: 349.37 g, 5-min: 86.25 g, 10-min: 56.80 g, 30-min: 45.46 g, 1-h: 52.56 g, 2-h: 54.58 g). Conclusions: These findings suggest that Zio XT accelerometry is valid for measuring duration, frequency, and intensity of physical activity within time epochs of 5-min to 2-h. [ABSTRACT FROM AUTHOR]

Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool. [ABSTRACT FROM AUTHOR]

Heart rate is a key vital sign that can be used to understand an individual's health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present an approach that can minimize interference and thereby enable simultaneous heart rate monitoring of multiple individuals in close proximity using a commonly available smart speaker prototype. Our user study, conducted under various real-life scenarios, demonstrates the system's accuracy in sensing two users' heart rates when they are seated next to each other with a median error of 0.66 beats per minute (bpm). Moreover, the system can successfully monitor up to four people in close proximity. [ABSTRACT FROM AUTHOR]

Simple Summary: Two of the fundamental pillars of animal welfare are stress-free management and treatment. The extensive farming of cattle involves minimal human contact. However, regular veterinary treatments and vaccinations require restraining cattle with headlocks in crates in order to ensure the safety of workers. Since these animals are usually kept in herds and mainly graze freely, this procedure is a high-stress situation for them. Animals can vocalize a number of sounds as part of their behavior. For example, dams have a special nasal vocalization toward their calves with a calming effect. We hypothesized that, by removing noises, selecting the most representative part, and creating a repeated sound, this kind of nasal vocalization would have a calming effect on cattle restrained in headlocks. Our results show that the played processed nasal vocalization of a mother cow reduced the stress experienced by cattle during the test. Sound analysis is an important field of research for improving precision livestock farming systems. If the information carried by livestock sounds is interpreted correctly, it could be used to improve management and welfare assessment in this field. Therefore, we hypothesized that the nasal vocalization of a mother cow could have a calming effect on conspecifics. The nasal vocalization in our study was recorded from a mother cow (not part of the test herd) while it was licking its day-old calf. The raw sound was analyzed, cleaned from noises, and the most representative vocalization was lengthened to two minutes. Thirty cows having calves were randomly selected from eighty Hungarian grey cattle cows. Two test days were selected, one week apart; the weather circumstances in both days were similar. The herd was collected in a paddock, and the test site (a restraining crate with a headlock) was 21 m away from them. The cows from the herd were gently moved to the restraining crate, and, after the installation of the headlock, Polar® heart rate monitors were fixed on the animals. The recording of the RR intervals was carried out for two minutes. On day one of the test, the processed nasal sound was played to every second cow during the heart rate monitoring. When the sound ended, the heart rate monitor was removed. On test day two, the sound and no sound treatments were switched among the participating cows. At the end of the measurement, the headlock was opened, letting the animals out voluntarily, and a flight test was performed along a 5 m distance. The time needed to pass the 5 m length was measured with a stopwatch and divided by the distance. The RR intervals were analyzed with the Kubios HRV Standard (ver. 3.5.0) software. The following data were recorded for the entire measurement: average and maximum heart rate; SD1 and SD2; pNN50; VLF, LF, and HF. The quasi-periodic signal detected in the sound analyses can hardly be heard, even when it is enhanced to the maximum. This can be considered a vibration probably caused by the basis of articulation, such as a vibration of the tongue, for example. The SD2/SD1 ratio (0.97 vs. 1.07 for the animals having no sound and sound played, respectively, p = 0.0110) and the flight speed (0.92 vs. 1.08 s/m for the animals having no sound and sound played, respectively, p = 0.0409) indicate that the sound treatment had a calming effect on the restrained cows. The day of the test did not influence any of the measured parameters; therefore, no effect of the routine was observed. The yes–no sequence of the sound treatment significantly reduced the pNN50 and flight speed values, suggesting a somewhat more positive association with the headlock and the effectiveness of the processed nasal sound. In conclusion, we have demonstrated that, by means of sound analyses, not only information about individuals and the herd can be gathered but that, with proper processing, the sound obtained can be used to improve animal welfare. [ABSTRACT FROM AUTHOR]

Background: Individuals with rheumatoid arthritis (RA) are at a high risk of cardiovascular diseases (CVD). A reduced chronotropic response (CR), which produces exercise intolerance, is known to be a contributing factor to CVD and mortality. Studies have shown that patients with RA have a reduced CR. However, knowledge of CR-related factors in patients with RA is limited. This study aimed to explore CR-related factors, including CVD risk factors, inflammatory markers, and cardiorespiratory fitness (VO2PEAK). Methods: A total of 106 RA patients underwent a treadmill test, heart rate monitoring, and various assessments, including serological CVD risk factors, inflammatory markers, and VO2PEAK. Results: A total of 34% of participants demonstrated a reduced CR (≤80%). Body mass index, HOMA, hsCRP, and fibrinogen were inversely related to CR, while HDL, QUICKi, VO2PEAK, and RER exhibited a positive association. HDL and VO2PEAK emerged as independent CR-related factors in regression analysis. Conclusions: The current findings suggest that reduced CR in RA is associated with several CVD risk factors, inflammatory markers, and cardiorespiratory fitness. Future studies should investigate the effects of controlling these associated variables on CR in patients with RA. [ABSTRACT FROM AUTHOR]

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the need to explore non-invasive alternatives for monitoring vital signs in patients with T2D. Objective: This systematic review is the first that explores the current literature and critically evaluates the use and reporting of non-invasive wearable devices for monitoring vital signs in patients with T2D. Methods: Employing the PRISMA and PICOS guidelines, we conducted a comprehensive search to incorporate evidence from relevant studies, focusing on randomized controlled trials (RCTs), systematic reviews, and meta-analyses published since 2017. Of the 437 publications identified, seven were selected based on predetermined criteria. Results: The seven studies included in this review used various sensing technologies, such as heart rate monitors, accelerometers, and other wearable devices. Primary health outcomes included blood pressure measurements, heart rate, body fat percentage, and cardiorespiratory endurance. Non-invasive wearable devices demonstrated potential for aiding T2D management, albeit with variations in efficacy across studies. Conclusions: Based on the low number of studies with higher evidence levels (i.e., RCTs) that we were able to find and the significant differences in design between these studies, we conclude that further evidence is required to validate the application, efficacy, and real-world impact of these wearable devices. Emphasizing transparency in bias reporting and conducting in-depth research is crucial for fully understanding the implications and benefits of wearable devices in T2D management. [ABSTRACT FROM AUTHOR]

Active virtual reality games (AVRGs) have become more and more popular. As the intensity of this form of physical activity (PA) may be insufficient to achieve health-related benefits, it is worth looking for solutions that increase the intensity of PA. The main aim of the study was to evaluate the effect of leg loading in the form of ankle weights (AWs) on the PA intensity of young adults playing AVRGs using an omnidirectional treadmill. The enjoyment of the game and users' perceptions of the usefulness of this type of exercise were also evaluated. The study involved 26 university students. Each participant played an AVRG game on an omnidirectional treadmill twice, without and with ankle weights (2 kg per leg). The intensity of PA was evaluated using a heart rate monitor. The attractiveness of the game was assessed using the Physical Activity Enjoyment Scale (PACES). The study found that the percentage of maximum heart rate in participants playing AVRGs without ankle weights was significantly lower than that observed when playing with the weights. In both cases, PA intensity was high. A survey showed that the weights attached to the ankles did not affect the perceptions of the enjoyment of the game. The use of ankle weights appears to be an effective and simple way to increase the intensity of physical exercise during AVRGs based on locomotor movements performed with the lower limbs, especially since, according to study participants, such a procedure does not negatively affect the enjoyment of the game. Due to the high intensity of PA while playing VR games using an omnidirectional treadmill, it can be assumed that regular use of this solution is likely to provide health benefits. [ABSTRACT FROM AUTHOR]

A low-power long-term ambulatory ECG monitor was developed for the acquisition, storage and processing of three simultaneous leads DI, aVF and V2 with a beat-to-beat heart rate measurement in real time. It provides long-term continuous ECG recordings until 84 h. The monitor uses a QRS complex detection algorithm based on the continuous wavelet transform with splines, which automatically selects the scale for the analysis of ECG records with different sampling frequencies. It includes a lead-off detection to continuously monitor the electrode connections and a real-time system of visual and acoustic alarms to alert users of abnormal conditions in its operation. The monitor presented is based in an ADS1294 analogue front end with four channels, 24-bit analog-to-digital converters and programmable gain amplifiers, a low-power dual-core ESP32 microcontroller, a microSD memory for data storage in a range of 4 GB to 32 GB and a 1.4 in thin-film transistor liquid crystal display (LCD) variant with a resolution of 128 × 128 pixels. It has programmable sampling rates of 250, 500 and 1000 Hz; a bandwidth of 0 Hz to 50% of the selected sampling rate; a CMRR of −105 dB; an input margin of ±2.4 V; a resolution of 286 nV; and a current consumption of 50 mA for an average battery life of 84 h. The ambulatory ECG monitor was evaluated with the commercial data-acquisition system BIOPAC MP36 and its module for ECG LABEL SS2LB, simultaneously comparing the morphologies of two ECG records and obtaining a correlation of 91.78%. For the QRS detection in real time, the implemented algorithm had an error less than 5%. The developed ambulatory ECG monitor can be used for the analysis of the dynamics of the heart rate variability in long-term ECG records and for the development of one's own databases of ECG recordings of normal subjects and patients with cardiovascular and noncardiovascular diseases. [ABSTRACT FROM AUTHOR]

Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection. [ABSTRACT FROM AUTHOR]

Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the "masked phase autoencoders with a vision transformer network" (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time. [ABSTRACT FROM AUTHOR]

Unobtrusive monitoring of children's heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children's RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson's correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s. [ABSTRACT FROM AUTHOR]

Background: Heart rate is an essential diagnostic parameter indicating a patient's condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)—a device monitoring the electrical activity of the heart using several electrodes attached to a patient's upper body—or photoplethysmography (PPG). Methods: The following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired. Results: The presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman's plots and Pearson's Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM. Conclusions: The results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process. [ABSTRACT FROM AUTHOR]

In this work, an assistance system based on the Artificial Intelligence of Things (AIoT) framework was designed and implemented to provide convenience for visually impaired people. This system aims to be low-cost and multi-functional with object detection, obstacle distance measurement, and text recognition achieved by wearable smart glasses, heart rate detection, fall detection, body temperature measurement, and humidity-temperature monitoring offered by an intelligent walking stick. The total hardware cost is approximately $66.8, as diverse low-cost sensors and modules are embedded. Meanwhile, a voice assistant is adopted, which helps to convey detection results to users. As for the performance evaluation, the accuracies of object detection and text recognition in the wearable smart glasses experiments are 92.16% and 99.91%, respectively, and the maximum deviation rate compared to the mobile app on obstacle distance measurement is 6.32%. In addition, the intelligent walking stick experiments indicate that the maximum deviation rates compared to the commercial devices on heart rate detection, body temperature measurement, and humidity-temperature monitoring are 3.52%, 0.19%, and 3.13%, respectively, and the fall detection accuracy is 87.33%. Such results demonstrate that the proposed assistance system yields reliable performances similar to commercial devices and is impressive when considering the total cost as a primary concern. Consequently, it satisfies the fundamental requirements of daily life, benefiting the safety and well-being of visually impaired people. [ABSTRACT FROM AUTHOR]

This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device. [ABSTRACT FROM AUTHOR]

Coconut water is used as an alternative to conventional sports drinks for hydration during endurance cycling; however, evidence supporting its use is limited. This study determined if drinking coconut water compared to a sports drink altered cycling performance and physiology. In a randomized crossover trial, 19 experienced male (n = 15) and female (n = 4) cyclists (age 30 ± 9 years, body mass 79 ± 11 kg, V ̇ O2 peak 55 ± 8 mL·kg−1·min−1) completed two experimental trials, consuming either a commercially available sports drink or iso-calorific coconut water during 90 min of sub-maximal cycling at 70% of their peak power output, followed by a simulated, variable gradient, 20 km time trial. Blood glucose, lactate, sweat loss, and heart rate were monitored throughout the 90 min of sub-maximal cycling, as well as the time trial performance (seconds) and average power (watts). A repeated measures analysis of variance and effect sizes (Cohen's d) analysis were applied. There were no significant differences (p ≥ 0.05) between the treatments for any of the measured physiological or performance variables. Additionally, the effect size analysis showed only trivial (d ≤ 0.2) differences between the treatments for all the measured variables, except blood glucose, which was lower in the coconut water trial compared to the sports drink trial (d = 0.31). Consuming coconut water had a similar effect on the cycling time trial performance and the physiological responses to consuming a commercially available sports drink. [ABSTRACT FROM AUTHOR]

As medical technology continues to evolve, the importance of real-time feedback from physiological signals is increasingly being recognized. The advent of the Internet of Things (IoT) has facilitated seamless connectivity between sensors and virtual networks, enabling the integration of thoughtful medical care with real-time feedback capabilities. This project uses cloud storage technology and cloud software algorithms to enable data sharing and real-time feedback. Its main focus is to provide a system for real-time feedback on physiological signals and sleep quality analysis. The system uses smart wristbands and smart mobile devices to collect, transmit, and analyze physiological data. During sleep, users wear these devices, which capture and analyze their physiological data. The analyzed data are then stored in a cloud-based database. The research involves studying sleep quality and determining optimal sleep quality parameters based on the data stored in the cloud database. These parameters are designed to improve sleep quality. They are then transmitted to a mobile sleep aid device to control light conditions. The sleep aid software used in previous generations of mobile devices is the basis for expanding the integration of the sleep detection system. By combining the software of a mobile device platform with that of a smart wearable device, data can be obtained to monitor the wearer's movements, such as turning over and heartbeat. The monitoring aspect includes tracking the turning time, distance, and speed, while the heartbeat monitoring includes detecting changes in heart rate, frequency, and interval using photoplethysmography (PPG) and smart wearable devices. Subsequently, artificial intelligence methods are employed to conduct statistical analysis and categorize the gathered extensive dataset. The system reads the data and provides the user with assessments and suggestions to improve sleep quality and overall sleep condition. [ABSTRACT FROM AUTHOR]

The early detection of arrhythmia can effectively reduce the risk of serious heart diseases and save time for treatment. Many healthcare devices have been widely used for electrocardiogram (ECG) monitoring. However, most of them can only complete simple two-classes detection and have unacceptable hardware overhead and energy consumption. For achieving accurate and low-power arrhythmia detection, a novel ECG processor application specific integrated circuit (ASIC) is proposed in this paper, which can perform the prediction of five types of cardiac arrhythmias and heart rate monitoring. To realize hardware-efficient R-peak detection, an ECG pre-processing engine based on a first derivative and moving average comparison method is proposed. Efficient arrhythmia detection is realized by the proposed low-power classification engine, which is based on a carefully designed lightweight artificial neural network (ANN) with good prediction accuracy. The hardware reuse strategy is used to implement the hardware logic of ANN, where computations are executed by only one processing unit (PU), which is controlled by a flexible finite state machine (FSM). Also, the weights of ANN are configurable to facilitate model updates. We validate the functionality of the design using real-world ECG data. The proposed ECG processor is implemented using 55 nm CMOS technology, occupying an area of 0.33 mm2. This design consumes 12.88 μW at a 100 kHz clock frequency, achieving a classification accuracy of 96.69%. The comparison results with previous work indicate that our design has advantages in detection performance and power consumption, providing a good solution for low-power and low-cost ECG monitoring. [ABSTRACT FROM AUTHOR]