Search Results (160)

Defensive pessimism, an important emotion regulation and motivation strategy, has increasingly attracted scholarly attention in psychology. Recently, sensor-based methods have begun to supplement or replace traditional questionnaire surveys in personality research. However, current approaches for collecting vital signs data face several challenges, including limited monitoring durations, significant data deviations, and susceptibility to external interference. This paper proposes a novel approach using a NiCr/NiSi alloy film temperature sensor, which has a K-type structure and flexible piezoelectric pressure sensor to identify and predict defensive pessimism personality traits. Experimental results indicate that the Seebeck coefficients for K-, T-, and E-type thermocouples are approximately 41 μV/°C, 39 μV/°C, and 57 μV/°C, respectively, which align closely with national standards and exhibit good consistency across multiple experimental groups. Moreover, radial artery frequency experiments demonstrate a strong linear relationship between pulse rate and the intensity of external stimuli, where stronger stimuli correspond to faster pulse rates. Simulation experiments further reveal a high correlation between radial artery pulse frequency and skin temperature, and a regression model based on the physiological sensor data shows a good fit (p < 0.05). These findings verify the feasibility of using temperature and flexible piezoelectric pressure sensors to identify and predict defensive pessimism personality characteristics. Full article

Due to the increasing number of vehicles and the aging population, the vulnerability to sudden medical emergencies among drivers is a growing problem. Events such as heart attack, stroke, and loss of consciousness can occur without warning and endanger everyone on the road. Modern vehicles, equipped with electronic systems, can support real-time driver’s health monitoring through early detection technologies. The existing Driver Monitoring Systems (DMS) in our cars assess behavioral states such as drowsiness and distraction. In the future, DMS will include biometric sensors to monitor vital signs such as heart rate and respiration. By finding predictors of sudden illnesses (SI), such a system will provide valuable time for the driver to react before the strike of a medical event. In this paper, we present our vision for DMS operation with physiological monitoring capabilities. A brief overview of sensor’s types and their locations in the vehicle interior used in the research studies for monitoring the corresponding physiological parameters is presented. A comparative analysis of the advantages and disadvantages of the sensing methods used for physiological monitoring of the driver in real driving scenarios is made. Full article

Driver monitoring systems (DMSs) are increasingly important for road safety, aiming to reduce driver-caused accidents. Traditional DMSs, focusing on behavioral and observable signals, lack the sensitivity to detect changes in the driver’s health status. Monitoring physiological parameters offers the opportunity to objectively assess the driver’s condition in real time and detect early signs of medical emergencies. After a brief overview of the physiological parameters that are critical for assessing the driver’s condition, we examine the different methods and sensors for obtaining the relevant physiological signals with their advantages and limitations. Based on this review, a taxonomy of methods, techniques, and sensors for acquisition of physiological signals in DMSs is proposed. It provides a systematically structured and detailed classification to understand the relationships between physiological parameters and the different methods and sensors for their measurement. This taxonomy can serve as a fundamental framework for researchers and developers to design and implement reliable next-generation DMSs based on physiological signals. Full article

This study evaluates the accuracy of a low-cost wearable system for the continuous monitoring of vital signs, including heart rate, blood oxygen saturation (Sp${\mathrm{O}}_2$), blood pressure trend (BPT), and body temperature. The prototype was built using the nRF52840 microcontroller, which integrates photoplethysmography and infrared sensors. The heart rate and Sp${\mathrm{O}}_2$ data were collected under three body positions (Rest, Sitting, and Standing), while all measurements were performed using both anatomical configurations: BPT-Finger and BPT-Earlobe. Results were compared against validated commercial devices: UT-100 for heart rate and Sp${\mathrm{O}}_2$, G-TECH LA800 for blood pressure, and G-TECH THGTSC3 for body temperature. Ten participants were monitored over a ten-day period. Bland–Altman analysis revealed clinically acceptable agreement thresholds of ±5 mmHg for blood pressure, ±5–10 bpm for heart rate, ±4% for Sp${\mathrm{O}}_2$, and ±0.5 °C for temperature. Both wearable configurations demonstrated clinically acceptable agreement across all vital signs. The BPT-Earlobe configuration exhibited superior stability and lower variability in the Rest and Sitting positions, likely due to reduced motion artifacts. Conversely, the BPT-Finger configuration showed higher Sp${\mathrm{O}}_2$ accuracy in the Standing position, with narrower limits of agreement. These findings highlight the importance of sensor placement in maintaining measurement consistency across physiological conditions. With an estimated cost of only ~USD 130—compared to ~USD 590 for the commercial alternatives—the proposed system presents a cost-effective, scalable, and accessible solution for decentralized health monitoring, particularly in underserved or remote environments. Full article

Background: Beach volleyball (BVb) is a highly demanding Olympic sport characterized by intense physical activity and unique environmental challenges, including varying weather conditions and sandy, unstable court surfaces. Despite its popularity, there is a notable lack of scientific research addressing the metabolic and immune responses of elite female athletes in this sport. This study aims to address this gap by investigating two world-class Olympic medalists, female BVb players, who represent a country with a rich history in the sport. Methods: Two athletes underwent a simulated competition day consisting of two matches. A standardized protocol was utilized to collect blood and urine samples at seven time points, allowing for analysis throughout the competition and recovery phases. The analysis included various electrolytes, as well as hematological, metabolic, and inflammatory markers. Additionally, we assessed selected hormones, such as insulin, serotonin, ACTH, and cortisol, along with amino acids related to energy metabolism and neurotransmitter synthesis. Results: Both athletes presented a trend toward electrolyte disturbances, especially hypokalemia, with a mean decrease of 15% and individual values reaching as low as 3.3 mmol/L post-match. This indicates that BVb may pose a risk for such disturbances. Additionally, the matches led to 20% to 60% increases in muscle injury markers, with incomplete recovery even after a day of rest, signaling persistent physiological stress post-competition. This increase was matched by stimulating stress hormones (ACTH and cortisol rose up to 4-fold and 3-fold, respectively), and markers of exercise intensity, such as lactate and ammonium. Moreover, the simulated BVb competition day impacted the amino acid response, with the Fischer ratio (BCAA/AAA) and blood tryptophan decreasing to a minimum of 60% of the initial levels and blood serotonin increasing by up to 180%, which are signs of an increased risk of central fatigue onset, according to the Fischer and Newsholme theory. Conclusions: The responses examined in this exploratory study contribute to a deeper understanding of the metabolic and immune demands placed on elite female BVb players, suggesting practical applications. By addressing the similar physiological responses observed among the athletes and emphasizing their unique individual responses—despite following the same protocol under identical conditions and sharing similar life habits for an extended period—this study highlights the critical necessity for the n-of-1 monitoring of athletes. Full article

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

Chronic lower limb ischemia is a debilitating condition, particularly prevalent among elderly patients and individuals ineligible for revascularization procedures. Gene therapy aimed at promoting therapeutic angiogenesis presents a promising alternative treatment strategy. Objectives: This study evaluated the preclinical safety of a gene therapy drug composed of the plasmids pBudK-coVEGF-coANG and pBudK-coGDNF in laboratory animals. Safety assessment followed a single intramuscular injection at a dose 30 times higher than the proposed therapeutic level. Methods: Acute toxicity was monitored over a 24-h period. Genotoxicity was assessed using the micronucleus test at doses of 200, 1000, and 5000 μg/kg. Bone marrow cytology was analyzed to detect hematopoietic toxicity. Delayed toxicity was evaluated over a two-week recovery period. Results: No signs of acute toxicity were observed, even at the highest dose. The micronucleus test revealed no genotoxic effects, with no significant increase in micronucleated polychromatic erythrocytes compared to control groups. Bone marrow erythroblast parameters remained within normal physiological ranges. Additionally, no delayed adverse effects were detected during the recovery period. Conclusions: The gene therapy drug demonstrated a favorable preclinical safety profile, exhibiting no evidence of toxicity or genotoxicity, even at substantially elevated doses. These findings support the continued development of this therapy as a potential treatment for chronic lower limb ischemia in patients who are not candidates for surgical intervention. Full article

Fibre optic sensors (FOSs) have developed as a transformative technology in healthcare, often offering unparalleled accuracy and sensitivity in monitoring various physiological and biochemical parameters. Their applications range from tracking vital signs to guiding minimally invasive surgeries, enabling advancements in medical diagnostics and treatment. However, the integration of FOSs into biomedical applications faces numerous challenges. This article describes some challenges for adopting FOSs for biomedical purposes, exploring technical and practical obstacles, and examining innovative solutions. Significant challenges include biocompatibility, miniaturization, addressing signal processing complexities, and meeting regulatory standards. By outlining solutions to the stated challenges, it is intended that this article provides a better understanding of FOS technologies in biomedical settings and their implementation. A broader appreciation of the technology, offered in this article, enhances patient care and improved medical outcomes. Full article

Background and Objectives: Thermography is a non-invasive diagnostic technique that measures skin surface temperatures to reflect normal or abnormal physiology. This review explores the clinical utility of thermography in diagnosing and monitoring stroke, with an emphasis on its clinical applications. Materials and Methods: This systematic review followed PRISMA guidelines, with a protocol published prior to analysis. Three databases were screened up to end of 2024. Article selection was conducted in two stages: title and abstract screening using Rayyan^®, followed by full-text eligibility assessment. Discrepancies were resolved through consensus. Risk of bias assessment was performed with ROBINS-I. Narrative synthesis was planned in addition to descriptive statistics. Results: A total of 20 studies were included after screening 277 records. Thermography emerged as a promising tool for stroke patients in both the acute and chronic phases. In the acute phase, it demonstrated potential in detecting early signs of carotid occlusive disease by identifying temperature differences in the forehead or neck regions. Additionally, thermography contributed to the differential diagnosis of Wallenberg syndrome. In the chronic phase, it exhibited clinical utility in monitoring rehabilitation progress. Conclusions: Thermography shows promise as a non-invasive tool for stroke assessment and monitoring. While preliminary studies suggest physiological relevance, its clinical utility remains investigational and requires further validation. Full article

Weaning is a critical period for calves, affecting their well-being, health, and productivity. The present study analyzed 61 calves to evaluate the effects of weaning as a stress factor by monitoring hematological, biochemical, and immune parameters. Blood samples were collected at the start of weaning at 130–135 days (T0), 3 (T1) and 7 (T2) days later, and finally at 150 days of age. The results showed changes in the levels of cortisol, inflammatory cytokines (TNF-α, INF-γ and IL-8) and acute-phase proteins. Alterations in white blood cells were also observed, such as neutrophilia and lymphopenia, which are typical signs of an adverse stress response. Biochemically, the switch from milk to solid food caused imbalances in protein, cholesterol, and triglyceride levels, indicating a relevant metabolic adaptation. Serum electrophoresis showed a decrease in albumin and an increase in γ-globulins, a sign of immune maturation. The results suggest that weaning is one of the most important stressors for calves, with both physiological and immune effects. However, this practice is necessary for breeding productivity. Future research could investigate the role of genetics and environment in weaning resilience to further optimize breeding practices. Full article

In-vehicle physiological sensing is emerging as a vital approach to enhancing driver monitoring and overall automotive safety. This pilot study explores the feasibility of a pressure-based system, repurposing commonplace occupant classification electronics to capture respiration signals during real-world driving. Data were collected from a driver-seat-embedded, fluid-filled pressure bladder sensor during normal on-road driving. The sensor output was processed using simple filtering techniques to isolate low-amplitude respiratory signals from substantial background noise and motion artifacts. The experimental results indicate that the system reliably detects the respiration rate despite the dynamic environment, achieving a mean absolute error of 1.5 breaths per minute with a standard deviation of 1.87 breaths per minute (9.2% of the mean true respiration rate), thereby bridging the gap between controlled laboratory tests and real-world automotive deployment. These findings support the potential integration of unobtrusive physiological monitoring into driver state monitoring systems, which can aid in the early detection of fatigue and impairment, enhance post-crash triage through timely vital sign transmission, and extend to monitoring other vehicle occupants. This study contributes to the development of robust and cost-effective in-cabin sensor systems that have the potential to improve road safety and health monitoring in automotive settings. Full article

Radars are promising tools for contactless vital sign monitoring. As a screening device, radars could supplement polysomnography, the gold standard in sleep medicine. When the radar is placed lateral to the person, vital signs can be extracted simultaneously from multiple body parts. Here, we present a method to select every available breathing and heartbeat signal, instead of selecting only one optimal signal. Using multiple concurrent signals can enhance vital rate robustness and accuracy. We built an algorithm based on persistence diagrams, a modern tool for time series analysis from the field of topological data analysis. Multiple criteria were evaluated on the persistence diagrams to detect breathing and heartbeat signals. We tested the feasibility of the method on simultaneous overnight radar and polysomnography recordings from six healthy participants. Compared against single bin selection, multiple selection lead to improved accuracy for both breathing (mean absolute error: 0.29 vs. 0.20 breaths per minute) and heart rate (mean absolute error: 1.97 vs. 0.66 beats per minute). Additionally, fewer artifactual segments were selected. Furthermore, the distribution of chosen vital signs along the body aligned with basic physiological assumptions. In conclusion, contactless vital sign monitoring could benefit from the improved accuracy achieved by multiple selection. The distribution of vital signs along the body could provide additional information for sleep monitoring. Full article

Mind wandering is a common issue among schoolchildren and academic students, often undermining the quality of learning and teaching effectiveness. Current detection methods mainly rely on eye trackers and electrodermal activity (EDA) sensors, focusing on external indicators such as facial movements but neglecting voice detection. These methods are often cumbersome, uncomfortable for participants, and invasive, requiring specialized, expensive equipment that disrupts the natural learning environment. To overcome these challenges, a new algorithm has been developed to detect mind wandering during reading aloud. Based on external indicators like the blink rate, pitch frequency, and reading rate, the algorithm integrates these three criteria to ensure the accurate detection of mind wandering using only a standard computer camera and microphone, making it easy to implement and widely accessible. An experiment with ten participants validated this approach. Participants read aloud a text of 1304 words while the algorithm, incorporating the Viola–Jones model for face and eye detection and pitch-frequency analysis, monitored for signs of mind wandering. A voice activity detection (VAD) technique was also used to recognize human speech. The algorithm achieved 76% accuracy in predicting mind wandering during specific text segments, demonstrating the feasibility of using noninvasive physiological indicators. This method offers a practical, non-intrusive solution for detecting mind wandering through video and audio data, making it suitable for educational settings. Its ability to integrate seamlessly into classrooms holds promise for enhancing student concentration, improving the teacher–student dynamic, and boosting overall teaching effectiveness. By leveraging standard, accessible technology, this approach could pave the way for more personalized, technology-enhanced education systems. Full article

Background/Objectives: One of the primary goals of neurorehabilitation after stroke is gait reeducation, as it provides the patient with greater autonomy and enhances their safety in daily activities. A preliminary clinical study was undertaken to determine whether robotic gait reeducation using the Lokomat device is more effective than conventional therapy in achieving gait symmetry. Methods: The research group consisted of 107 patients, with an average age of 63.54 years, all in the subacute stage of hemiparesis. These patients underwent 4 weeks of neurorehabilitation and were assigned into experimental and control groups. The patients in the experimental group underwent neurorehabilitation (20 sessions) and twice-weekly walking on the Lokomat device (10 sessions). The control group received equivalent neurorehabilitation and conventional gait reeducation. We monitored the return of ideal limb loading (to a 50:50 ratio) and the restoration of the step length on the paretic limb to a physiological length (73 cm), as well as the subsequent restoration of gait symmetry. The measurements were performed using the HP Cosmos Zebris Treadmill FDM-T device. The Wilcoxon Signed Rank test was conducted within each group to analyze the effectiveness of gait reeducation before and after therapy. To compare the results between the two groups, the Mann–Whitney test (α = 0.05) was employed. Results: There was no significant difference between the robotic and conventional therapy groups (p = 0.432 (>0.05)). A significant change occurred only in the control group in the 50:50 limb loading parameter (p = 0.042). There were no significant changes in the other parameters. Conclusions: Under the conditions of our study, robot-guided gait reeducation did not appear to be more effective than conventional therapy. The monthly duration of gait reeducation is insufficient to achieve a symmetrical gait in patients with spastic hemiparesis. Full article

Calving is a crucial event in dairy production, and predicting its exact date is challenging due to variable gestation lengths and environmental factors. Failure to recognize signs of impending calving can lead to dystocia, increased calf mortality, veterinary costs, and economic losses. This review discusses both invasive and non-invasive methods for monitoring calving signs. Invasive methods, such as temperature loggers, intra-vaginal GSM devices, and blood progesterone tests, provide direct physiological data but can cause stress to the animals. Non-invasive approaches, including the use of sensors on limbs, necks, or tails, as well as video monitoring systems, offer less intrusive alternatives, improving cow comfort. Advances in artificial intelligence (AI) and machine learning have enhanced the predictive accuracy of these methods, enabling better management of the calving process. Full article