Search Results (105)

Background and Objectives: Heart failure (HF) represents a major public health challenge worldwide, with rising prevalence, high morbidity and mortality rates, and substantial healthcare costs. Non-invasive telemonitoring has emerged as a promising adjunct in HF management, yet its clinical effectiveness remains unclear. Materials and Methods: In this systematic review, we summarize randomized controlled trials (RCTs) between 2004 and 2024 examining the efficacy of non-invasive telemonitoring on mortality, readmission, and quality of life (QoL) in HF. In addition, we characterize the heterogeneity of features of different telemonitoring interventions. Results: In total, 32 RCTs were included, comprising 13,294 participants. While some individual studies reported benefits, non-invasive telemonitoring demonstrated mixed effects on mortality, readmission rates, and QoL. The most common modality for interfacing with patients was by mobile application (53%), followed by web portals (22%), and stand-alone devices (19%). Periodic feedback (63%) was more common than continuous feedback (31%) or on-demand feedback (6%). Clinician reviews of patient telemonitoring data was event-triggered (44%) more commonly than based on a prespecified timeline (38%). In most designs (90%), patients played a passive role in telemonitoring. Conclusions: Non-invasive telemonitoring interventions for HF exhibited considerable variation in duration and system design and had a low rate of patient engagement. Future work should focus on identifying telemonitoring-responsive subgroups and refining telemonitoring strategies to complement traditional HF care. Full article

Purpose: We aimed to evaluate the safety and feasibility of a remote electrocardiogram (ECG) monitoring-based cardiac rehabilitation (CR) program during an early postoperative period in patients who underwent cardiac surgery. Methods: Five days after cardiac surgery, patients were referred to a CR department and participated in a low-intensity inpatient CR program while wearing an ECG monitoring device. Prior to discharge, the patients underwent a cardiopulmonary exercise test (CPET) and squat endurance test to determine the suitable intensity and target heart rate (HR) for home-based CR (HBCR). During 2 weeks of the HBCR period after discharge, patients participated in aerobic and resistance exercises. Electrocardiogram data were transmitted to a cloud, where researchers closely monitored them through a website and provided feedback to the patients via telephone calls. Grip strength (GS), 6 min walk distance (6 MWD), EuroQol-5 dimension (EQ-5D), short-form 36-item health survey (SF-36), and Korean Activity Scale/Index (KASI) were measured at three different time points: 5 d post-surgery (T1), pre-discharge (T2), and 2 weeks after discharge (T3). Squat endurance tests and CPET were performed only at T2 and T3. Result: Sixteen patients completed the study, seven (44%) of whom underwent coronary artery bypass graft surgery (CABG). During the study period between T2 and T3, peak VO₂ improved from 12.39 ± 0.57 to 17.93 ± 1.25 mL/kg/min (p < 0.01). The squat endurance test improved from 16.69 ± 2.31 to 21.81 ± 2.31 (p < 0.01). In a comparison of values of time points between T1 and T3, the GS improved from 28.30 ± 1.66 to 30.40 ± 1.70 kg (p = 0.02) and 6 MWD increased from 249.33 ± 20.92 to 387.02 ± 22.77 m (p < 0.01). The EQ-5D and SF-36 improved from 0.59 ± 0.03 to 0.82 ± 0.03 (p < 0.01) and from 83.99 ± 3.40 to 122.82 ± 6.06 (p < 0.01), and KASI improved from 5.44 ± 0.58 to 26.11 ± 2.70 (p < 0.01). In a subgroup analysis, the CABG group demonstrated a greater increase in 6 MWD (102.29 m, p < 0.01) than the non-CABG group. At the end of the study, 75% of the patients expressed satisfaction with the early CR program guided by remote ECG monitoring. Conclusions: Our findings suggest that early remote ECG monitoring-based CR programs are safe and feasible for patients who have undergone cardiac surgery. Additionally, the program improved aerobic capacity, functional status, and quality of life. Full article

Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices. Full article

(1) Background: Biofeedback and neurofeedback are gaining attention as non-invasive rehabilitation strategies in Parkinson’s disease (PD) treatment, aiming to modulate motor and non-motor symptoms through the self-regulation of physiological signals. (2) Objective: This review explores the application of biofeedback techniques, electromyographic (EMG) biofeedback, heart rate variability (HRV) biofeedback, and electroencephalographic (EEG) neurofeedback in PD rehabilitation, analyzing their impacts on motor control, autonomic function, and cognitive performance. (3) Methods: This review critically examined 15 studies investigating the efficacy of electromyographic (EMG), heart rate variability (HRV), and electroencephalographic (EEG) feedback interventions in PD. Studies were selected through a systematic search of peer-reviewed literature and analyzed in terms of design, sample characteristics, feedback modality, outcomes, and clinical feasibility. (4) Results: EMG biofeedback demonstrated improvements in muscle activation, gait, postural stability, and dysphagia management. HRV biofeedback showed positive effects on autonomic regulation, emotional control, and cardiovascular stability. EEG neurofeedback targeted abnormal cortical oscillations, such as beta-band overactivity and reduced frontal theta, and was associated with improvements in motor initiation, executive functioning, and cognitive flexibility. However, the reviewed studies were heterogeneous in design and outcome measures, limiting generalizability. Subgroup trends suggested modality-specific benefits across motor, autonomic, and cognitive domains. (5) Conclusions: While EMG and HRV systems are more accessible for clinical or home-based use, EEG neurofeedback remains technically demanding. Standardization of protocols and further randomized controlled trials are needed. Future directions include AI-driven personalization, wearable technologies, and multimodal integration to enhance accessibility and long-term adherence. Biofeedback presents a promising adjunct to conventional PD therapies, supporting personalized, patient-centered rehabilitation models. Full article

AI code generators are increasingly used in creative contexts, offering operational efficiencies on the one hand and prompting concerns about psychological and neurophysiological strain on the other. This study employed a multimodal approach to examine the affective, autonomic, and creative consequences of AI-assisted coding in early-stage learners. Fifty-eight undergraduate design students with no formal programming experience were randomly assigned to either an AI-assisted group or a control group and engaged in a two-day generative programming task. Emotional states (PANAS), creative self-efficacy (CSES), and subjective workload (NASA-TLX) were assessed, alongside continuous monitoring of heart rate variability (HRV; RMSSD and LF/HF). Compared to the controls, the AI-assisted group exhibited greater increases in negative affect (p = 0.006), reduced parasympathetic activity during the task (p = 0.001), and significant post-task declines in creative self-efficacy (p < 0.05). Expert evaluation of creative outputs revealed a significantly lower performance in the AI group (p = 0.040), corroborated by behavioral observations showing higher tool dependency, emotional volatility, and rigid problem-solving strategies. These findings indicate that, in novice users, the opacity and unpredictability of AI feedback may disrupt emotional regulation and autonomic balance, thereby undermining creative engagement. The results highlight the need to consider neurocognitive vulnerability and the learner’s developmental stage when integrating AI tools into cognitively demanding creative workflows. Full article

Skin is the largest organ of the human body and holds the functions of sensing, protecting, and regulating. Since ancient times, people have decorated their skin by painting themselves, cutting, and using accessories to express their personality and aesthetic consciousness as a kind of artistic expression, one that shows the development and change of aesthetic consciousness. However, there are concerns regarding the inconvenience, high time cost, and negative body perception with traditional tattoos. In addition, the trend of skin decoration has gradually withdrawn due to a lack of intelligent interaction. In response to these problems, we proposed a wearable electronic skin tattoo that offers a novel means of communication and emotional expression for individuals with communication impairments, WABEIT. The tattoo uses skin-friendly PDMS as the base material, combines multi-mode sensing components such as silver wire circuit, a programmable Surface-Mounted Device (SMD), a thin-film-pressure sensor, and a heart rate sensor, and combines the embedded development board Arduino Nano for intelligent interaction, forming a wearable electronic interactive tattoo capable of sensing the environment, human–computer interaction, and the changeable performance of intelligent perception. The sensor is also equipped with a mobile power supply to support portability. The advantages of WABEIT are as follows: first, it avoids the pain, allergy, and long production process of traditional tattoos. Second, the patterns can adapt to different needs and generate feedback for users, which can effectively express personal emotions. Thirdly, the facility of removal reduces social discrimination and occupational constraints, which is especially suitable for East Asia. Experimental results indicate that the device exhibits a high sensitivity in signal response, a wide variety of pattern changes, and reliable interactive capabilities. The study demonstrates that the proposed design philosophy and implementation strategy can be generalized to the interactive design of other wearable devices, thereby providing novel insights and methodologies for human–computer interaction, electronic devices, and sensor applications. Full article

Sleep disorders increase the risk of cardiovascular diseases. However, the underlying mechanisms remain unclear. This study aims to examine the critical role of oxytocin neurons in the paraventricular nucleus (PVN^OXT) in regulating the cardiovascular system and to elucidate potential mechanisms through which sleep disturbance may contribute to cardiovascular diseases. In this study, using an automated sleep deprivation system, mice were given chronic sleep deprivation (cSD) for 7 days, 6 h per day. cSD induced blood transcriptomic alterations accompanied by lower heart rate, higher blood pressure, and elevated cardiac autophagy/apoptosis. Instant optogenetic activation of oxytocin neurons in the paraventricular nucleus (PVN^OXT) provoked heart rate suppression in normal mice, whereas in cSD mice, activation precipitated intermittent cardiac arrest. On the contrary, inhibition of PVN^OXT showed no influence on the cardiovascular system of normal mice, but it attenuated cSD-induced rise in blood pressure. Long-term low-frequency stimulation (LTF) of PVN^OXT decreased neuronal excitability and oxytocin release, effectively reversing cSD-mediated cardiovascular responses. Mechanistically, cSD triggered the upregulation of blood-derived 3-mercaptopyruvate sulfurtransferase (mPST), and a suppression of PVN^OXT postsynaptic activity to a certain extent. The quick and long-term decrease of oxytocin by LTF could lead to feedback inhibition in mPST expression and thus reverse cSD-mediated cardiovascular responses. Altogether, modulation of PVN^OXT could mediate cSD-induced cardiovascular abnormalities without affecting normal mice. Our research provided potential targets and key mechanisms for cardiovascular diseases associated with sleep disorders. Full article

Obesity hypoventilation syndrome (OHS) is a multifaceted condition characterized by significant respiratory, cardiovascular, and psychological consequences. Positive airway pressure (PAP) therapy remains the cornerstone treatment, improving respiratory function, neurocognition, and mental health disorders such as depression and anxiety. However, its long-term impact on quality of life, physical activity, and broader health outcomes is not fully understood. Challenges such as residual apnoea/hypopnea index, reduced physical activity, and impaired quality of life persist despite high adherence rates. Factors like hypercapnia and daytime respiratory symptoms play a pivotal role in patient outcomes, underscoring the need for strategies beyond adherence alone. This review explores the interplay between OHS, heart failure, and sleep dysfunction, advocating for personalized PAP settings, targeted management of residual respiratory events, and enhanced patient education. Digital health technologies, including remote monitoring and feedback systems, present promising tools to optimize care delivery and foster holistic management. By integrating physiological, psychological, and digital health perspectives, this narrative review aims to advance understanding and improve outcomes for patients with OHS and other complex sleep-disordered breathing conditions. Full article

Background/Objectives: Stuttering is a speech disorder involving fluency disruptions like repetitions, prolongations, and blockages, often leading to emotional distress and social withdrawal. Here, we present Augmented Multisensory Feedback Stimulation (AMFS), a novel personalized intervention to improve speech fluency in people who stutter (PWS). AMFS includes a five-day intensive phase aiming at acquiring new skills, plus a reinforcement phase designed to facilitate the transfer of these skills across different contexts and their automatization into effortless behaviors. The concept of our intervention derives from the prediction of the neurocomputational model Directions into Velocities of Articulators (DIVA). The treatment applies dynamic multisensory stimulation to disrupt PWS’ maladaptive over-reliance on sensory feedback mechanisms, promoting the emergence of participants’ natural voices. Methods: Forty-six PWS and a control group, including twenty-four non-stuttering individuals, participated in this study. Stuttering severity and physiological measures, such as heart rate and electromyographic activity, were recorded before and after the intensive phase and during the reinforcement stage in the PWS but only once in the controls. Results: The results showed a significant reduction in stuttering severity at the end of the intensive phase, which was maintained during the reinforcement training. Crucially, worse performance was found in PWS than in the controls at baseline but not after the intervention. In the PWS, physiological signals showed a reduction in activity during the training phases compared to baseline. Conclusions: Our findings show that AMFS provides a promising approach to enhancing speech fluency. Future studies should clarify the mechanisms underlying such intervention and assess whether effects persist after the treatment conclusion. Full article

Stress has become a common mental health issue in modern society, causing individuals to experience acute behavioral changes. Exposure to prolonged stress without proper prevention and treatment may cause severe damage to one’s physiological and psychological health. Researchers around the world have been working to find and create solutions for early stress detection using machine learning (ML). This paper investigates the possibility of utilizing Tiny Machine Learning (TinyML) in developing a wearable device, comparable to a smartwatch, that is equipped with both physiological and psychological data detection system to enable edge computing and give immediate feedback for stress prediction. The main challenge of this study was to fit a trained ML model into the microcontroller’s limited memory without compromising the model’s accuracy. A TinyML-based framework using a Raspberry Pi Pico RP2040 on a customized board equipped with several health sensors was proposed to predict stress levels by utilizing accelerations, body temperature, heart rate, and electrodermal activity from a public health dataset. Moreover, a few selected machine learning models underwent hyperparameter tuning before a porting library was used to translate them from Python to C/C++ for deployment. This approach led to an optimized XGBoost model with 86.0% accuracy and only 1.12 MB in size, hence perfectly fitting into the 2 MB constraint of RP2040. The prediction of stress on the edge device was then tested and validated using a separate sub-dataset. This trained model on TinyML can also be used to obtain an immediate reading from the calibrated health sensors for real-time stress predictions. Full article

To obtain the optimal driving visual guidance methods in sudden low-visibility fog environments, it is crucial to analyze the changes in visual characteristics and information demand under low-visibility foggy conditions. The paper constructs a driving visual information demand model for foggy environments based on visual information input and output, using Shannon’s theory and feedback control theory. Two types of foggy road sections with the same visibility, one with guidance lights and one without, were selected for real-vehicle experiments based on the driver’s blood pressure, heart rate, and driving gaze domain tests. The study found the following: (1) In sudden foggy environments, the amount of driving information obtained by drivers decreases instantly with a sudden drop in visibility, failing to meet the information demand for driving cognition, thereby disrupting the dynamic balance state of driving based on speed, visibility, and other road environment factors. The experiment also found that in low-visibility environments, the radius of the human eye’s visual gaze domain becomes smaller, with the gaze range mainly concentrated directly in front of the vehicle, and the lower the visibility, the smaller the gaze domain range; (2) Foggy conditions affect changes in drivers’ blood pressure and heart rate. Installing guidance lights with sufficient illumination at foggy sections to compensate for drivers’ visual information can effectively supplement the visual information required for safe driving; (3) The experiment indicates that the guidance effect of the lights is most pronounced when visibility is within the range of [50 m, 150 m]; however, when visibility is above 500 m, the presence of guidance lights can, to some extent, affect driving safety and increase the risk of accidents. Full article

We have examined the impact of CrossFit^® workout sessions on physical fitness, comparing the obtained outcomes with the recommendations of the American College of Sports Medicine. In addition, we provide suggestions to improve training monitoring, as well as practical applications for researchers, coaches and practitioners. CrossFit^® imposes high cardiorespiratory and metabolic demands, promoting improvements in circulatory capacity, oxidative metabolism and muscular endurance. Sustained elevations in heart rate contribute to cardiovascular conditioning, while a post-exercise hypotensive effect may help to reduce cardiovascular risks. Structured CrossFit^® programs have led to improvements in maximal strength and muscular endurance, with substantial increases in squat performance observed in both untrained and recreationally active individuals. In addition, CrossFit^® improves mental health through its motivating community. However, the high metabolic demands, increased creatine kinase levels and reduced performance in the countermovement jump reveal that muscle damage and neuromuscular fatigue can persist for up to 48 h. Balancing these intense sessions with adequate recovery is crucial, as improper management may lead to overtraining and compromise fitness gains. Future research should explore long-term cardiovascular adaptations, differences in gains and recovery between males and females and the application of real-time biomarker and artificial intelligence technologies to improve the training efficiency and safety. Machine learning algorithms could further personalize feedback, adapting to each individual’s biomechanics and physiological responses over time. Full article

Background: Despite the known impact of propofol and remifentanil on hemodynamics and patient outcomes, there is a lack of comprehensive quantitative analysis, particularly in surgical settings, considering the influence of noxious stimuli. The aim of this study was to develop a quantitative semi-mechanistic population model that characterized the time course changes in mean arterial pressure (MAP) and heart rate (HR) due to the effects of propofol, remifentanil, and different types of noxious stimulation related to the clinical routine. Methods: Data from a prospective study were used; the study analyzed the effects of propofol and remifentanil general anesthesia on female patients in physical status of I-II according to the American Society of Anesthesiologists (ASA I-II) undergoing gynecology surgery. Patients were consecutively assigned to different administration schemes of propofol and remifentanil targeted at different effect-site concentrations. Esophageal instrumentation, laryngeal mask airway insertion, hysteroscopy, and tetanus stimuli were applied. Data from patients with chronic hypertension were discarded. Results: MAP and HR observations from 77 patients were analyzed. The hemodynamic effects were described using turn-over models incorporating feedback mechanisms. Analyses revealed that propofol and remifentanil elicited effects on the turn-over of MAP and HR, respectively, with estimates of plasma drug concentrations causing an inhibition-half of the maximum effect (C50) of 8.79 µg∙mL⁻¹ and 4.57 ng∙mL⁻¹. Hysteroscopy exerted an increase in MAP (but not in HR), which was well-characterized by the model, with a predicted typical increase of 28 mmHg and a dissipation half-life of 33 min. The impact of other noxious stimuli on MAP or HR could not be identified. Model simulations indicated that propofol and remifentanil, titrated to inhibit the motor response to noxious stimuli, regardless of dose combinations, cause a significant risk of hypotension, especially following induction and at the end of surgery (when surgical intervention is completed, before the awakening phase). Conclusions: The developed semi-mechanistic and fully identifiable model provides quantitative information on how propofol, remifentanil, and surgical stimulus (hysteroscopy) interact to produce the hemodynamic changes (of MAP and HR) commonly observed in clinical practice. Full article

Canine-assisted interactions (CAIs) have been explored to offer therapeutic benefits to human participants in various contexts, from addressing cancer-related fatigue to treating post-traumatic stress disorder. Despite their widespread adoption, there are still unresolved questions regarding the outcomes for both humans and animals involved in these interactions. Previous attempts to address these questions have suffered from core methodological weaknesses, especially due to absence of tools for an efficient objective evaluation and lack of focus on the canine perspective. In this article, we present a first-of-its-kind system and study to collect simultaneous and continuous physiological data from both of the CAI interactants. Motivated by our extensive field reviews and stakeholder feedback, this comprehensive wearable system is composed of custom-designed and commercially available sensor devices. We performed a repeated-measures pilot study, to combine data collected via this system with a novel dyadic behavioral coding method and short- and long-term surveys. We evaluated these multimodal data streams independently, and we further correlated the psychological, physiological, and behavioral metrics to better elucidate the outcomes and dynamics of CAIs. Confirming previous field results, human electrodermal activity is the measure most strongly distinguished between the dyads’ non-interaction and interaction periods. Valence, arousal, and the positive affect of the human participant significantly increased during interaction with the canine participant. Also, we observed in our pilot study that (a) the canine heart rate was more dynamic than the human’s during interactions, (b) the surveys proved to be the best indicator of the subjects’ affective state, and (c) the behavior coding approaches best tracked the bond quality between the interacting dyads. Notably, we found that most of the interaction sessions were characterized by extended neutral periods with some positive and negative peaks, where the bonded pairs might display decreased behavioral synchrony. We also present three new representations of the internal and overall dynamics of CAIs for adoption by the broader field. Lastly, this paper discusses ongoing options for further dyadic analysis, interspecies emotion prediction, integration of contextually relevant environmental data, and standardization of human–animal interaction equipment and analytical approaches. Altogether, this work takes a significant step forward on a promising path to our better understanding of how CAIs improve well-being and how interspecies psychophysiological states can be appropriately measured. Full article

In an intelligent driving environment, monitoring the physiological state of drivers is crucial for ensuring driving safety. This paper proposes a method for monitoring and analyzing driver physiological characteristics by combining electronic vehicle identification (EVI) with multimodal biometric recognition. The method aims to efficiently monitor the driver’s heart rate, breathing frequency, emotional state, and fatigue level, providing real-time feedback to intelligent driving systems to enhance driving safety. First, considering the precision, adaptability, and real-time capabilities of current physiological signal monitoring devices, an intelligent cushion integrating MEMSs (Micro-Electro-Mechanical Systems) and optical sensors is designed. This cushion collects heart rate and breathing frequency data in real time without disrupting the driver, while an electrodermal activity monitoring system captures electromyography data. The sensor layout is optimized to accommodate various driving postures, ensuring accurate data collection. The EVI system assigns a unique identifier to each vehicle, linking it to the physiological data of different drivers. By combining the driver physiological data with the vehicle’s operational environment data, a comprehensive multi-source data fusion system is established for a driving state evaluation. Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN extracts spatial features from the input signals, while the LSTM processes time-series data to capture the temporal characteristics. This combined model effectively identifies and analyzes the driver’s physiological state, enabling timely anomaly detection. The method was validated through real-vehicle tests involving multiple drivers, where extensive physiological and driving behavior data were collected. Experimental results show that the proposed method significantly enhances the accuracy and real-time performance of physiological state monitoring. These findings highlight the effectiveness of combining EVI with multimodal biometric recognition, offering a reliable means for assessing driver states in intelligent driving systems. Furthermore, the results emphasize the importance of personalizing adjustments based on individual driver differences for more effective monitoring. Full article