Eye Closure Research Articles

Normative data for instrumented posturography: a systematic review and meta-analysis

Postural control is a multisensory adaptive system performing predictive (anticipatory) and/or reactive (compensatory) actions, with varying degrees of accuracy, to maintain balance in a changing environmental context. Common instrumentation to evaluate balance includes static and dynamic force platforms; added sway-referenced perturbations on the dynamic platform constitute its main advantage. Clinical applications notwithstanding, normative data are needed for interpretation in clinical settings. Posturography norms are used to compare a reference group (healthy individuals) and a specific patient population. This work, to the best of our knowledge, represents the first attempt to synthesize the literature on normative data for computerized posturography using a combined mixed method. The search strategy resulted in the retrieval of 1,244 articles from PubMed, Web of Science, and Science Direct. After deduplication, 689 articles were screened based on title and abstract. One hundred and seven articles met the criteria after the first screening. In-depth, full-text screening resulted in the inclusion of 44 studies for the systematic review and 17 studies for the meta-analyses. The main findings of the systematic review are (1) extensive heterogeneity was found in methodological characteristics, (2) there was insufficient risk of bias mitigation, (3) the majority of tasks evaluated less than four components of the systems framework for postural control (SFPC), and (4) studies mostly used distance domain sway parameters and did not report the influence of other variables on postural sway. Based on the multilevel meta-analyses, females appeared to outperform males in eyes closed (EC) conditions significantly. Based on the network meta-analyses, we found that younger children swayed more than those aged between 8 and 14 years both in eyes open (EO) conditions and EC conditions significantly. The results also revealed a significant difference in sway between individuals of age range between 50 and 79 years old and younger individuals, with more instability observed in older participants both in EO conditions and in EC conditions. Thus, future studies need to ensure that enough information about participants is provided. Standardization of experimental conditions and sway parameters harmonization are still needed to ensure high-quality assessment (QA). Finally, evidence-based postural impairment management requires both age- and sex-related normative data.Systematic review registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023378144, identifier PROSPERO 2023 CRD42023378144.

Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals.

This study aims to investigate using eyes-open (EO) and eyes-closed (EC) resting-state EEG data to diagnose cognitive impairment using machine learning methods, enhancing timely intervention and cost-effectiveness in dementia research. A total of 890 participants aged 40-90 were included in the study, comprising 269healthy controls (HC), 356 individuals with mild cognitive impairment (MCI), and 265 with Alzheimer's disease (AD) from a cohort study. Resting-state EEG (rEEG) signals were recorded and transformed into relative power spectral density (PSD) data for analysis. The processed PSD data, representing 19 scalp regions, were then input into a Random Forest (RF) machine learning classifier to identify distinctive EEG patterns across the groups. Statistical comparisons between the groups were conducted using one-way ANOVA, applied to the relative PSD features extracted from the EEG data, to assess significant differences in EEG activity across the diagnostic categories. The study found that rEEG-based categorization effectively differentiates between cognitively impaired individuals and healthy individuals. The EO rEEG achieved the highest performance metrics across various models. For HC vs MCI (combined hemisphere), the accuracy, sensitivity, specificity, and AUC were 92%, 99%, 83%, and 96%, respectively. For HC vs AD (parietal, temporal, occipital), these metrics were 95%, 96%, 94%, and 99%. The HC vs CASE (MCI + AD) (combined hemisphere) results were 90%, 99%, 73%, and 92%. The metrics for HC vs MCI vs AD (frontal, parietal, temporal) were 89%, 88%, 94%, and 96%. The study demonstrates that EO rEEG can effectively distinguish between cognitive impairment and healthy states, leading to early diagnosis, cost-effective treatment, and better clinical outcomes for dementia patients. EO and EC rEEG models trained with relative PSD, particularly from parietal, temporal, occipital, and central scalp regions, can significantly assist clinicians in practice.

Deep Learning for Karolinska Sleepiness Scale Classification Based On Eye Aspect Ratio with SMOTE-Enhanced Data Balancing

This paper addresses the challenge of accurately classifying sleepiness levels based on the Karolinska Sleepiness Scale (KSS) using Eye Aspect Ratio (EAR) data, especially when class imbalance leads to biased predictions. The research proposes a deep learning framework that integrates a Multi-Layer Perceptron (MLP) with the Synthetic Minority Over-sampling Technique (SMOTE) to balance the training data. EAR features, representing eye closure patterns, are extracted from video frames, and SMOTE is applied to generate synthetic data for underrepresented sleepiness classes. By training the MLP model on this balanced dataset, the system achieves a 97.6% classification accuracy in distinguishing four distinct sleepiness levels based on the KSS, demonstrating its effectiveness in reducing prediction bias and managing class imbalance, both crucial for real-time drowsiness detection systems

Electroencephalography Longitudinal Markers of Central Neuropathic Pain Intensity in Spinal Cord Injury: A Home-Based Pilot Study

Background: It is well known from cross-sectional studies that pain intensity affects brain activity as measured by electroencephalography (EEG) in people with neuropathic pain (NP). However, quantitative characterisation is scarce. Methods: In this longitudinal study, ten people with spinal cord injury-related NP recorded their home EEG activity ten days before and after taking medications over a period of several weeks. Results: The reduction in pain due to medications was accompanied by changes in the resting state EEG and its reactivity to eyes opening (EO) and closing (EC). There was a significant positive correlation between the frontal theta band and the intensity of pain (visual numerical scale) pre-medication (p = 0.007, Pearson R = 0.29) and theta, alpha, and lower beta (6–15 Hz) band power and the intensity of pain after post-medication over the frontal, central, and parietal cortices. Reactivity had a negative correlation with pain intensity at all locations and frequency bands and showed similar behaviour in wider frequency bands like 8–15 Hz at the occipital cortex and 2–12 Hz at the frontal cortex. Conclusions: EEG could be used to detect the intensity of NP to serve as a surrogate or pharmacodynamic marker.

Machine Learning based modulation format classification framework for inter-satellite optical wireless communication system (IsOWCS)

Abstract The exponential growth in demand for high-capacity optical systems has driven the advancement of advanced modulation formats to upgrade transmission capacity and transmission quality. Effective fault diagnosis and self-configuration in inter-satellite optical wireless communication systems (IsOWCS) depend intensely on the generated data. Machine learning (ML) approaches offer promising solutions in evaluating the execution of these networks. In this study, a dataset was created using OptiSystem 18.0. The dataset was composed of various modulation formats such as duobinary, return-to-zero (RZ), non-return-to-zero (NRZ), 33 % RZ, chirped NRZ, vestigial sideband (VSB) NRZ, carrier-suppressed return-to-zero (CSRZ), and VSB CSRZ. The classification of modulation formats has been presented in this study using ML. The dataset was created by varying input power from 0 to 20 dBm and evaluating parameters such as Q factor, input/output signal-to-noise ratio (SNR), power, range, eye closure, amplitude, height, eye opening, output OSNR. Four ML classifiers were used to predict the classification of different modulation formats. Random forest (RF) classifier performed exceptionally well and achieved 100 % accuracy. Moreover, an interactive user-friendly web page was also developed using Anvil for modulation format classification. The proposed research underscores the significance of selecting the appropriate modulation format to optimize the performance and transmission distance of IsOWCS, subsequently enhancing the operation of high-speed optical communication systems.

Effect of electroacupuncture on the characteristic signal of electroencephalogram in the patients with chronic insomnia： a randomized controlled trial

To observe the effect of electroacupuncture (EA) on sleep quality and electroencephalogram (EEG) signal in the patients with chronic insomnia and to explore its mechanism. Sixty patients with chronic insomnia were randomized into an EA group (30 cases, 3 cases dropped out) and a health learning group (30 cases, 3 cases dropped out). In the health learning group, the sleep health education was provided. In the EA group, besides the health education as the health learning group, EA was delivered at Baihui (GV20), Yintang (GV24+), bilateral Shenmen (HT7), bilateral Sanyinjiao (SP6), with continuous wave, 2 Hz in frequency, once every other day, and 3 interventions a week. EA was given for 4 weeks successi-vely, composed of 12 treatments. The score of Pittsburgh sleep quality index (PSQI), Hamilton anxiety scale (HAMA), and patient health questionnaire-9 (PHQ-9) before and after treatment were compared between the two groups. The EEG signals were collected and the relative power and entropy of EEG before and after treatment were compared between the two groups. After treatment, the scores of PSQI, HAMA and PHQ-9, the relevant power of β wave in F8 and T3 channels, and that of α wave in T4 channel, and the entropy in T3 channel under the eyes-open (EO) resting state, as well as the relevant power and the entropy of γ wave in F8 channel under the eyes-closed (EC) resting state in the EA group were lower (P<0.05) than those before the treatment. All of the above results were lower than those of the health learning group at the same time points (P<0.05). Under the EO resting state, the relevant power of the θ and δ waves of T3 channel and θ wave of T4, O2 and Pz channels of patients in the EA group after the treatment were increased (P<0.05) than those before the treatment, and higher (P<0.05) than those of the health learning group at the same time points. Acupuncture is effective on chronic insomnia, which may be related to the regulation EEG signals.

Internet of things assisted deep learning enabled driver drowsiness monitoring and alert system using CNN-LSTM framework

Abstract Driver fatigue has been generally viewed as a critical road safety factor and has been cited for contributing to a good percentage of traffic accidents. Developing systems to monitor and alert drowsy drivers is essential in reducing incidents. This research proposes a robust framework for driver drowsiness detection using a CNN-LSTM architecture developed by fusing facial landmark analysis with multiple aspect ratios. It uses, as key metrics in detecting drowsiness, the Eye Aspect Ratio (EAR), Pupil Circularity (PUC), the Mouth Aspect Ratio (MAR), and the Mouth over Eye Aspect Ratio (MOE). CNN-LSTM had been trained on YawDD, NITYMD, FL3D, and custom datasets. Data augmentation techniques such as flipping, scaling, shearing, rotation, brightness and contrast adjustment are used to generalize under different illumination conditions and driver postures. The system is implemented on NVIDIA’s 128-core Jetson Nano GPU platform and does real-time processing of video frames captured by a CSI camera. It detects eye closure and yawning as symptoms of driver fatigue and immediately raises an alert through vibrations in the seatbelt and pre-recorded voice messages. The Internet connectivity allows remote monitoring via mobile applications, making it safer by ensuring that alerts reach the driver and passengers. This CNN-LSTM model has been carefully tested for various scenarios, including day and night conditions, proving its effectiveness. The proposed framework indicated excellent performance concerning accuracy (98%), precision (95%), recall (93%), F1 score (94%), and AUC (99%), thereby revealing its efficiency in practical scenarios. By incorporating EAR, MAR, PUC, and MOE for the early detection of drowsiness in this CNN-LSTM architecture, the system will be able to alert the driver ahead of time so that necessary precautions can be taken to avoid an accident. The proposed approach enhances the driver's safety and is a scalable solution that adapts to different environments and populations.

Modulation of neural networks and symptom correlated in fibromyalgia: A randomized double-blind multi-group explanatory clinical trial of home-based transcranial direct current stimulation.

Transcranial direct current stimulation (tDCS) might modulate neural activity and promote neural plasticity in patients with fibromyalgia (FM). This multi-group randomized clinical trial compared home-based active tDCS (HB-a-tDCS) on the left dorsolateral prefrontal cortex (l-DLPFC) or home-based sham tDCS (HB-s-tDCS), and HB-a-tDCS or HB-s-tDCS on the primary motor cortex (M1) in the connectivity analyses in eight regions of interest (ROIs) across eight resting-state electroencephalography (EEG) frequencies. We included 48 women with FM, aged 30 to 65, randomly assigned to 2:1:2:1 to receive 20 sessions during 20 minutes of HB-a-tDCS 2mA or HB-s-tDCS, over l-DLPFC or M1, respectively. EEG recordings were obtained before and after treatment with eyes open (EO) and eyes closed (EC). In the EC condition, comparing pre to post-treatment, the HB-a-tDCS on l-DLPFC decreased the lagged coherence connectivity in the delta frequency band between the right insula and left anterior cingulate cortex (ACC) (t = -3.542, p = .048). The l-DLPFC HB-a-tDCS compared to HB-s-tDCS decreased the lagged coherence connectivity in the delta frequency band between the right insula and left ACC (t = -4.000, p = .017). In the EO condition, the l-DLPFC HB-a-tDCS compared to M1 HB-s-tDCS increased the lagged coherence connectivity between the l-DLPFC and left ACC in the theta band (t = -4.059, p = .048). Regression analysis demonstrated that the HB-a-tDCS effect on the l-DLPFC was positively correlated with sleep quality. On the other hand, the HB-a-tDCS on l-DLPFC and HB-s-tDCS on M1 were positively correlated with pain catastrophizing. These results show that HB-a-tDCS affects the neural connectivity between parts of the brain that control pain's emotional and attentional aspects, which are most noticeable at lower EEG frequencies in a rest state. This effect on neural oscillations could serve as a neural marker associated with its efficacy in alleviating fibromyalgia symptoms. identifier [NCT03843203].

Brainwave Patterns and Metabolic Adaptations in Rowers Crossing the Atlantic: A Case Series Pilot Study.

This pilot study was designed to test the hypothesis that quantitative electroencephalographic (qEEG) measurements reflect physiological adaptations for brain energy reallocation. The study focused on a team of three well-matched male rowers participating in a 30-day, 2,650-mile continuous transatlantic rowing competition, examining the effects of extreme, prolonged stress on brain function and metabolic adaptations. Measurements at the start and finish lines included body weight, height, waist circumference, body fat, and a panel of hormones and biochemical markers. Post-race qEEG parameters were recorded under eyes-open (EO) and eyes-closed (EC) conditions. qEEG data were compared to a reference population (ages 6-90 years) and to an age-matched 27-year-old male medical student serving as a control subject. qEEG analysis evaluated voltage amplitudes, wave distribution patterns, theta-to-beta ratios (TBR), and coherence levels. Hormonal changes and oxidative stress markers were also assessed before and after race. Two rowers exhibited post-race dominance of high-frequency beta activity, while one displayed co-dominance of delta and beta waves. Compared to the control subject (TBR = 1.25), the rowers' low TBRs (< 0.2) indicated high vigilance and low relaxation during EC conditions. Cortisol levels increased in all rowers and were associated with beta coherence >1 SD above the reference mean. Testosterone decreased in two rowers but increased in one; the smallest cortisol increase corresponded with the largest testosterone decrement. Decreases in oxidative stress markers correlated with a shift from right- to left-sided alpha asymmetry, consistent with redistribution of alpha wave energy to the nondominant hemisphere. This pattern was also observed in the control subject. Increased testosterone in one rower was linked to a decrease in the percentage of sites exhibiting normal theta frequencies, indicating a potential role for testosterone in brain energy reallocation. The findings suggest that qEEG measurements reflect physiological adaptations in response to extreme stress, supporting the hypothesis that metabolic energy is reallocated to optimize vigilance and performance. The observed correlations between hormonal changes, oxidative stress markers, and qEEG parameters provide preliminary evidence of mechanisms for brain energy reallocation. These insights highlight the potential for qEEG to identify biomarkers of stress adaptation and lay the groundwork for larger studies to further elucidate these mechanisms.

Efficacy and feasibility of a breath sensor for detecting driver fatigue and drowsiness

Objectives Drowsiness has been shown to impair reaction times, decision-making abilities, and awareness of hazards critical to driving in a manner similar to the effects of driving under the influence of alcohol.1 Drowsy driving has been estimated to cause thousands of fatal crashes per year and has resulted in billions of dollars in lost revenue.2 However, while breathalyzers and ignition interlock devices can be used to mitigate the risk of drunk driving, no such devices are commercially available for roadside or behind-the-wheel diagnostics for drowsiness. To address this gap, the objective of this study was to demonstrate the efficacy and feasibility of a breath sensor to detect fatigue and drowsiness in a simulated driving environment. Data and methods Solid state breath sensors calibrated to previously identified breath biomarkers of fatigue and drowsiness were arrayed to determine changes in the breath profiles of subjects undergoing a full-motion driving simulation designed to elicit driver fatigue. A multi-sensor approach was used rather than relying on a single biomarker. The biomarker profile targeted compounds such as benzene derivatives (e.g., benzene, benzaldehyde, toluene, phenol), 2-butanone, and 2-ethyl-1-hexanol, among others. To address potential confounders such as eating/drinking, cigarette smoke, alcohol, and medical conditions like COPD or diabetes, the study was conducted under controlled conditions: participants refrained from eating for a couple of hours before the experiment, were nonsmokers, had not consumed alcohol for the past day, and had no known medical conditions. The breath biomarker profiles were then correlated with established drowsiness metrics, specifically the PERCLOS (Percentage of Eye Closure) and the Karolinska Sleepiness Scale (KSS). Results The breath sensor array collected breath biomarker profiles for 61 subjects. The biomarker profiles demonstrated a trend consistent with increasing PERCLOS and KSS responses (see Figure). During the 60-minute driving simulation designed to elicit driver fatigue, the PERCLOS showed a steady increase in response over time, while the KSS did not show an increase in perceived sleepiness until the 60 min mark. Similarly, the breath biomarker sensor response showed a significant increase at the 60-minute timepoint, consistent with increases in both the PERCLOS and the KSS, demonstrating evidence of increased driver fatigue and drowsiness. Conclusions These findings suggest that integrating drowsiness breath sensor arrays into vehicle systems could significantly enhance drowsiness detection, thereby reducing crashes, injuries, and fatalities. Moreover, the scalability and non-invasiveness of the proposed system lays the groundwork for broader applications, extending beyond vehicles to fields such as aviation and healthcare.

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

Predicting Drowsy Driver Break Taking During Long Drives.

The current study investigated the factors that predict drowsy drivers' decisions regarding whether to take breaks versus continue driving during long simulator drives. Driver drowsiness contributes to substantial numbers of motor vehicle crashes, injuries, and deaths. Previous research has shown that taking a nap and consuming caffeine can temporarily mitigate drowsiness and enable continued safe driving. Ninety drivers completed a 150-mile highway drive in a driving simulator after a day of partial sleep restriction. Drivers passed several simulated rest areas where they could take breaks. To replicate drivers' motivation to reach their destination safely but also quickly, drivers were told that they would be paid more for completing the simulated drive faster but would forfeit their payment if they crashed. Break taking was predicted by drivers' self-ratings of drowsiness and by the severity of lane departures. However, even at the highest levels of drowsiness, most drivers bypassed simulated rest areas without stopping. In comparing self-rated drowsiness to drowsiness measured by eye closures, drivers often under- and over-estimate their own level of drowsiness. Drowsy drivers use their own self-assessed drowsiness when deciding whether to take breaks. These self-assessments are often incorrect, and even when drivers rate themselves as severely drowsy they are unlikely to stop to rest during long drives. The findings reveal the need for effective drowsy driving countermeasures to motivate drivers to stop to take breaks. Results underscore the need to educate and/or motivate drivers to respond sooner to warning signs of drowsiness.

Reliability of the Wii balance board for static and dynamic balance assessment in total knee arthroplasty patients

PurposePoor performance in practicing balance tasks is partly caused by the changes in the knee function and balance control activities following total knee arthroplasty (TKA). The purpose of this study was to determine the test-retest reliability of the static and dynamic balance measures using Wii Balance Board (WBB) in patients after TKA. DesignThirty-one TKA patients (6 males, 27 females, mean age 55 ± 5.3 years) were involved in the study. They performed four standing balance trials on a WBB on two occasions, with an interval of 3–7 days apart. Double leg stance and functional reach task with opened and closed eyes were assessed in four trials. Extracted center of pressure (COP) measures collected included: mean velocity, medio-lateral displacement, antero-posterior displacement, and area. Intraclass correlation coefficients (ICC), standard error of measure (SEM), minimal detectable change (MDC) and Bland-Altman plots were calculated to show the reliability of COP measures. ResultsICCs ranged from 0.51 to 0.86 for the four trials indicating moderate-to-good reliability. Just the medio-lateral displacement in double leg open eyes condition showed weak reliability (ICC = 0.29). The highest and lowest ICC values (0.86, 0.29) were obtained for COP medio-lateral displacement in double leg open eyes condition and medio-lateral displacement in double leg close eyes condition measures, respectively. SEM (0.03–5.93) and MDC (0.09–16.45) range scores varied. Bland-Altman plots revealed no significant difference between two trials in both opened and closed eyed conditions, indicating strong agreement between assessments. ConclusionThese findings suggest that the WBB was a reliable tool for assessing static and dynamic balance tests in TKA patients, making it as a suitable tool for standing balance assessment in clinical settings.

Surgery for facial palsy in the hands of otorhinolaryngologists: a population-based study.

Modern facial surgery can improve eye closure and address facial functional and emotional expression disabilities in case of severe acute facial paralysis with low probability of recovery and in cases of chronic flaccid facial paralysis. Reports on outcome typically originate from specialized tertiary care centers, whereas population-based data from routine care beyond specialized centers is sparse. Therefore, patients' characteristics, surgical techniques, postoperative complications, and patients' satisfaction with the final outcome were analyzed for all inpatients with facial paralysis undergoing facial surgery in Thuringia, a federal state in Germany, between 2006 and 2022. 260 patients (female 41.5%; median age 65 years) were included. On average, the surgery rate was higher for men than for women (0.83 ± 0.39 versus 0.58 ± 0.24 per 100,000 population per year). For first surgery, static procedures were dominating (67.3%), followed by dynamic reconstruction (13.8%), and combined static and dynamic reconstructions (13.5%). The most frequent type of surgery was upper lid weight loading (38.5%), hypoglossal-facial jump nerve suture (17.3%), and facial-facial interpositional graft suture (16.9%). Bleeding/hematoma formation needing revision surgery was the most frequent complication (6.2%). Overall, 70.4% of the patients were satisfied with the final result. The satisfaction was higher if the target was to improve eye closure (65.2%) or to improve upper face function (65.3%) than to improve the lower face function (53.3%). If facial nerve reconstruction surgery and/or upper lid weight placement was performed, the satisfaction was significantly higher. If revision surgery was needed to improve the result, the satisfaction was significantly lower.

Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing.

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts.

Eye closures in spoken Hebrew

Abstract The present study focuses on eye closure (EC) as a communicative facial gesture in Israeli Hebrew media talk and pays particular attention to its coordination with co-expressive verbal, prosodic, and embodied resources. Drawing on the interactional approach to language and embodied action, the study demonstrates that EC can convey four contextual meanings — concentration, hedging, negation, and totality — depending on the context in which it occurs and the verbal material with which it is co-produced. The paper proposes that these contextual meanings are derived from the more general meaning of disengagement conveyed by EC. Moreover, the present paper suggests several potential semiotic connections between the basic physiological use of EC as a reflex of self-protection and evasion and its observed contextual meanings. This provides evidence for the co-optation of the basic functions of facial gestures for communicative purposes via metaphorical-metonymical extensions.

Effects of Neuromuscular Training on Postural Balance and Physical Performance in Older Women: Randomized Controlled Trial.

Background: Aging causes morphological and physiological changes, especially in the musculoskeletal and somatosensory systems, leading to sarcopenia and reduced postural balance, increasing the risk of falls and loss of independence. This study aimed to analyze the effects of a neuromuscular training program on postural balance, physical performance, and muscle strength in older Chilean women. Methods: The double-blind randomized controlled trial included 48 participants aged 65-80 who were divided into three groups: a control group (CG), a multicomponent training group (MCG), and a neuromuscular training group (NMG). Postural balance was assessed using a force platform with open eyes (OE) and closed eyes (CE) conditions, measuring ML velocity, AP velocity, mean radius, and area. Physical performance was evaluated with the Short Physical Performance Battery (SPPB), including static balance, 4-m walking speed, and 5× sit-to-stand chair test. Muscle strength was measured using a hydraulic hand dynamometer to record maximum grip force. Results: Significant time × group interactions in the CE condition were found for mean radius (F = 0.984; p = 0.017; ηp2 = 0.184), AP velocity (F = 10.01; p = 0.001; ηp2 = 0.312), and ML velocity (F = 4.30; p = 0.027; ηp2 = 0.163). Significant pre-post differences in the NMG for mean radius (p < 0.001), AP velocity (p < 0.001), and ML velocity (p = 0.029) were observed, with no significant changes in CG. Significant time × group interactions were also found in the SPPB test score (F = 11.49; p < 0.001; ηp2 = 0.343), gait speed (F = 5.513; p = 0.012; ηp2 = 0.198), and sit-to-stand test (F = 5.731; p = 0.010; ηp2 = 0.206), but not in the balance score (F = 2.099; p = 0.148). Handgrip strength showed no significant interactions (F = 3.216; p = 0.061; ηp2 = 0.127). Conclusions: These findings suggest that neuromuscular training is a promising intervention to mitigate the decline in balance and physical function associated with aging, offering a targeted approach to improve the quality of life in the elderly.

Revisiting the earliest hyperscanning study: power and functional connectivity in the alpha band may link brains far apart.

This brief report revisits the earliest known hyperscanning study published in 1965, which examined simultaneous EEG recordings of identical twins separated by six meters. The original study's findings suggested that eye closure in one twin elicited alpha activity in the other, despite physical separation. Leveraging contemporary signal processing techniques, we reanalyzed the digitized data to validate their findings. Spectral analysis confirmed alpha activity in the twins' EEG signals, aligning with the original observations. Multitapering along with background noise subtraction also revealed the alpha activity in the unrelated subject, which could not be observed by visual inspection alone. Coherence analysis revealed significant alpha band synchrony between a twin and an unrelated subject, differing from the initial study's conclusions. Our findings indicate that even historical data can yield new insights when revisited with contemporary data analysis tools and highlight the potential for future large-scale studies using advanced techniques to explore nonlocal brain interactions.

Effects of shuttle balance exercises on gait speed, postural control, and quality of life in older males: A randomized controlled trial.

Enhancing physical function and quality of life in older adults at higher risk of falls is challenging because of the lack of established interventions. This study examines the impact of a 6-week balance training program using the shuttle balance device on gait speed, postural control, and quality of life in older men. This single-blinded randomized controlled trial was conducted in a research laboratory. Thirty-two participants aged ≥60years were randomly assigned to either an experimental group (EG; n=16) or a control group (CG; n=14). The EG participated in a 6-week shuttle balance exercise program, while the CG maintained regular physical activity routines. The main outcome measures included gait speed (assessed via the timed up and go test), postural control (assessed via center of pressure data on a force plate), and quality of life (evaluated using the SF-36 questionnaire). Post-intervention, the EG showed significant improvements compared with the CG (p<0.05). The 95% confidence intervals for the differences between groups were as follows: closed eyes (CE) mean velocity (-39.07, -0.13), CE sway area (-48.86, -0.18), SF-36 total score (9.01, 16.81), SF-36 physical functioning (7.00, 24.81), SF-36 physical role functioning (1.80, 27.57), SF-36 pain (15.01, 36.82), SF-36 general health state (7.48, 26.08), SF-36 vitality (5.60, 28.35), and SF-36 mental health (0.21, 21.12). A 6-week shuttle balance training program significantly improves postural control and quality of life in older males. These findings suggest the potential effectiveness of shuttle balance exercises in enhancing physical function and well-being in this population. Further research is needed to validate these findings and explore the long-term effects with larger sample sizes.

Electro-Stimulation System with Artificial-Intelligence-Based Auricular-Triggered Algorithm to Support Facial Movements in Peripheral Facial Palsy: A Simulation Pilot Study.

Facial palsy causes severe functional disorders and impairs quality of life. Disturbing challenges for patients with acute facial palsy, but also with those with chronic facial palsy with synkinesis, are the loss of the ability to smile and insufficient eyelid closure. A potential treatment for these conditions could be a closed-loop electro-stimulation system that stimulates the facial muscles on the paretic side as needed to elicit eye closure, eye blink and smile in a manner similar to the healthy side. This study focuses on the development and evaluation of such a system. An artificial intelligence (AI)-based auricular-triggered algorithm is used to classify the intended facial movements. This classification is based on surface electromyography (EMG) recordings of the extrinsic auricular muscles, specifically the anterior, superior, and posterior auricular muscle on the paretic side. The system then delivers targeted surface electrical stimulation to contract the appropriate facial muscles. The evaluation of the system was conducted with 17 patients with facial synkinesis, who performed various facial movements according to a paradigm video. The system's performance was evaluated through a simulation, using previously captured data as the inputs. The performance was evaluated by means of the median macro F1-score, which was calculated based on the stimulation signal (output of the system) and the actual movements the patients performed. This study showed that such a system, using an AI-based auricular-triggered algorithm, can support with a median macro F1-score of 0.602 for the facial movements on the synkinetic side in patients with unilateral chronic facial palsy with synkinesis.