Using mindfulness-based intervention to promote executive function in young children: a multivariable and multiscale sample entropy study.

Early childhood marks a pivotal period in the maturation of executive function, the cognitive ability to consciously regulate actions and thoughts. Mindfulness-based interventions have shown promise in bolstering executive function in children. This study used the functional near-infrared spectroscopy technique to explore the impact of mindfulness-based training on young children. Brain imaging data were collected from 68 children (41 boys, aged 61.8 ± 10.7 months) who were randomly assigned to either an intervention group (N = 37, aged 60.03 ± 11.14 months) or a control group (N = 31, aged 59.99 ± 10.89 months). Multivariate and multiscale sample entropy analyses were used. The results showed that: (1) brain complexity was reduced in the intervention group after receiving the mindfulness-based intervention in all three executive function tasks (ps < 0.05), indicating a more efficient neural processing mechanism after the intervention; (2) difference comparisons between the intervention and control groups showed significant differences in relevant brain regions during cognitive shifting (left dorsolateral prefrontal cortex and medial prefrontal cortex) and working memory tasks (left dorsolateral prefrontal cortex), which corroborates with improved behavioral results in the intervention group (Z = -3.674, P < 0.001 for cognitive shifting; Z = 2.594, P < 0.01 for working memory). These findings improve our understanding of early brain development in young children and highlight the neural mechanisms by which mindfulness-based interventions affect executive function. Implications for early intervention to promote young children's brain development are also addressed.

Dynamics of gas exchange and heart rate signal entropy in standard cardiopulmonary exercise testing during critical periods of growth and development.

Standard cardiopulmonary exercise testing (CPET) produces a rich dataset but its current analysis is often limited to a few derived variables such as maximal or peak oxygen uptake (V̇O2). We tested whether breath-by-breath CPET data could be used to determine sample entropy (SampEn) in 81 healthy children and adolescents (age 7-18 years old, equal sex distribution). To overcome challenges of the relatively small time-series CPET data size and its nonstationarity, we developed a Python algorithm for short-duration physiological signals. Comparing pre- and post-ventilatory threshold (VT1) CPET phases, we found: (1) SampEn decreased by 9.46% for V̇O2 and 5.01% for V̇CO2 (p < 0.05), in the younger, early-pubertal participants; and (2) HR SampEn fell substantially by 70.8% in the younger and 77.5% in the older participants (p < 0.001). Across all ages, females exhibited greater HR SampEn than males during both pre- and post VT1 CPET phases by 14.10% and 23.79%, respectively, p < 0.01. In females, late-pubertal had 17.6% lower HR SampEn compared to early-pubertal participants (p < 0.05). Breath-by-breath gas exchange and HR data from CPET are amenable to SampEn analysis that leads to novel insight into physiological responses to work intensity, and sex and maturational effects.

Physiological Regularity and Synchrony in Individuals with Gaming Disorder.

Individuals with gaming disorder (GD) show emotional dysregulation and autonomic dysfunction in daily life. Although studies have shown that the relaxation method of breathing exercise (BE) improves cardiopulmonary synchrony, the physiological regularity and synchrony of GD remain unclear. In this study, we investigated the regularities of pulse wave (PW), thoracic wall movement (TWM), and abdominal wall movement (AWM) using sample entropy (SE) and assessed the vascular-respiratory and TWM-AWM synchrony using cross-sample entropy (CSE). Twenty individuals with GD and 26 healthy control (HC) individuals participated in baseline, gaming, and recovery stages, both before and after BEs. The results showed that both groups had significantly higher SETWM, SEAWM, and CSETWM-AWM during gaming than baseline. Before BE, CSEPW-TWM and CSEPW-AWM during gaming were considerably higher in the GD group than in the HC group. Compared to before BE, both groups had decreased SETWM and CSETWM-AWM during gaming, particularly in the HC group. Online gaming may induce pulse wave and respiratory irregularities, as well as thoracic-abdominal wall movement asynchrony. Individuals with GD who engage in prolonged gaming periods may exhibit lower vascular-respiratory synchrony compared to the HC group. SETWM, SEAWM, CSETWM-AWM, CSEPW-TWM, and CSEPW-AWM may serve as biomarkers for assessing the risk of GD. BE may improve TWM regularity and vascular-respiratory synchrony during gaming, potentially alleviating addictive behavior.

Bifurcation Diagrams of Nonlinear Oscillatory Dynamical Systems: A Brief Review in 1D, 2D and 3D.

We discuss 1D, 2D and 3D bifurcation diagrams of two nonlinear dynamical systems: an electric arc system having both chaotic and periodic steady-state responses and a cytosolic calcium system with both periodic/chaotic and constant steady-state outputs. The diagrams are mostly obtained by using the 0-1 test for chaos, but other types of diagrams are also mentioned; for example, typical 1D diagrams with local maxiumum values of oscillatory responses (periodic and chaotic), the entropy method and the largest Lyapunov exponent approach. Important features and properties of each of the three classes of diagrams with one, two and three varying parameters in the 1D, 2D and 3D cases, respectively, are presented and illustrated via certain diagrams of the K values, -1≤K≤1, from the 0-1 test and the sample entropy values SaEn>0. The K values close to 0 indicate periodic and quasi-periodic responses, while those close to 1 are for chaotic ones. The sample entropy 3D diagrams for an electric arc system are also provided to illustrate the variety of possible bifurcation diagrams available. We also provide a comparative study of the diagrams obtained using different methods with the goal of obtaining diagrams that appear similar (or close to each other) for the same dynamical system. Three examples of such comparisons are provided, each in the 1D, 2D and 3D cases. Additionally, this paper serves as a brief review of the many possible types of diagrams one can employ to identify and classify time-series obtained either as numerical solutions of models of nonlinear dynamical systems or recorded in a laboratory environment when a mathematical model is unknown. In the concluding section, we present a brief overview of the advantages and disadvantages of using the 1D, 2D and 3D diagrams. Several illustrative examples are included.

Different types of mastoid vibrations affect the amount and structure of minimum toe clearance variability differently.

BACKGROUND: The majority of research primarily examines the role of the vestibular system in regulating balance by assessing gait parameters in the transverse plane while neglecting those in the sagittal plane. The present study aimed to examine the impact of various forms of mastoid vibration (MV) on minimum toe clearance (MTC) and its pattern of variability. This study proposed two hypotheses: 1) the application of MV reduced the MTC, and 2) the application of different forms of MV influenced the amount and structure of MTC variability. METHODS: A total of twenty young adults participated in this study. A high-resolution motion capture system with eight cameras captured the minimum toe clearance. Three locomotor tasks were randomly assigned to these young participants: 1) walking normally on the treadmill, 2) walking with unilateral MV, and 3) walking with bilateral MV. The dependent variables were the mean of MTC, the amount, and the structure of MTC variability. The amount of MTC variability was calculated by the coefficient of variation represented, and the structure of MTC variability was measured using a sample entropy measure for a total of 200 MTCs. RESULTS: Applying unilateral and bilateral MV decreased the MTC significantly (-1.6 %, p = 0.038; -4.3 %, p < 0.001) compared to normal walking. Also, applying unilateral MV increased the amount (11.8 %, p = 0.001) and structure of MTC variability (14.3 %, p < 0.001) compared to normal walking. However, applying bilateral MV decreased the amount (-8.8 %, p = 0.001) and structure of MTC variability (-9.0 %, p < 0.001) compared to regular walking. CONCLUSION: Although the statistical differences in MTC and MTC variability were observed in the present study, the mean differences among the different MV conditions were relatively small, thereby requiring meticulous deliberation when extrapolating the results when implementing this MTC in the pathological cohort.

Detection of Pilots' Psychological Workload during Turning Phases Using EEG Characteristics.

Pilot behavior is crucial for aviation safety. This study aims to investigate the EEG characteristics of pilots, refine training assessment methodologies, and bolster flight safety measures. The collected EEG signals underwent initial preprocessing. The EEG characteristic analysis was performed during left and right turns, involving the calculation of the energy ratio of beta waves and Shannon entropy. The psychological workload of pilots during different flight phases was quantified as well. Based on the EEG characteristics, the pilots' psychological workload was classified through the use of a support vector machine (SVM). The study results showed significant changes in the energy ratio of beta waves and Shannon entropy during left and right turns compared to the cruising phase. Additionally, the pilots' psychological workload was found to have increased during these turning phases. Using support vector machines to detect the pilots' psychological workload, the classification accuracy for the training set was 98.92%, while for the test set, it was 93.67%. This research holds significant importance in understanding pilots' psychological workload.

Impairment of inhibitory control due to repetitive subconcussions from indirect brain impacts: Evidence from event-related potentials and resting-state EEG complexity in parachuters.

The present study aims to investigate the unknown relationship between inhibitory control and repetitive subconcussion induced by the indirect brain impacts. We enrolled 28 parachuters exposed to repetitive subconcussion (SC) and 27 matched health controls (HC). Parachuters who have completed at least 70 actual parachuting (71-112 times) and at least 1500 simulated platform jumps (1500-4500 times) were included in the SC group. The SC group had a reduced accuracy rate in both the Stroop congruent and incongruent conditions. Larger N2 and N450 amplitudes were elicited in the frontal regions of the SC group, which indicate compensatory adaptations to the deficit in conflict monitoring. The reduced frontal resting-state EEG complexity in full-band (1-40 Hz) may demonstrate the frontal structural damage following the indirect brain impacts of repetitive subconcussion. Pearson correlation analysis showed that in the SC group, the frontal beta-band sample entropy values are positively correlated with the accuracy rate of the Stroop incongruent condition, suggesting the frontal beta-band sample entropy values may serve as potential electrophysiological markers of impaired inhibitory control after indirectly repetitive brain impacts. This study provides the robust evidence that repetitive subconcussion resulting from indirect brain impacts may lead to impairment of inhibitory control.

Sample Entropy Computation on Signals with Missing Values.

Sample entropy embeds time series into m-dimensional spaces and estimates entropy based on the distances between points in these spaces. However, when samples can be considered as missing or invalid, defining distance in the embedding space becomes problematic. Preprocessing techniques, such as deletion or interpolation, can be employed as a solution, producing time series without missing or invalid values. While deletion ignores missing values, interpolation replaces them using approximations based on neighboring points. This paper proposes a novel approach for the computation of sample entropy when values are considered as missing or invalid. The proposed algorithm accommodates points in the m-dimensional space and handles them there. A theoretical and experimental comparison of the proposed algorithm with deletion and interpolation demonstrates several advantages over these other two approaches. Notably, the deviation of the expected sample entropy value for the proposed methodology consistently proves to be lowest one.

Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics.

A stroke represents a significant medical condition characterized by the sudden interruption of blood flow to the brain, leading to cellular damage or death. The impact of stroke on individuals can vary from mild impairments to severe disability. Treatment for stroke often focuses on gait rehabilitation. Notably, assessing muscle activation and kinematics patterns using electromyography (EMG) and stereophotogrammetry, respectively, during walking can provide information regarding pathological gait conditions. The concurrent measurement of EMG and kinematics can help in understanding disfunction in the contribution of specific muscles to different phases of gait. To this aim, complexity metrics (e.g., sample entropy; approximate entropy; spectral entropy) applied to EMG and kinematics have been demonstrated to be effective in identifying abnormal conditions. Moreover, the conditional entropy between EMG and kinematics can identify the relationship between gait data and muscle activation patterns. This study aims to utilize several machine learning classifiers to distinguish individuals with stroke from healthy controls based on kinematics and EMG complexity measures. The cubic support vector machine applied to EMG metrics delivered the best classification results reaching 99.85% of accuracy. This method could assist clinicians in monitoring the recovery of motor impairments for stroke patients.

Exploratory study of the lateral body sway predictability as frame of reference for gait rehabilitation following a total knee arthroplasty.

AIMS: The aim of the present study was to investigate whether the predictability of fronto-parallel trunk rotations (lateral body sway) could serve as a frame of reference to monitor recovery after total knee arthroplasty (TKA). METHODS: Before surgery, 11 TKA patients were asked to perform a treadmill walking task at three different speeds. In addition, their gait abilities were scored on three standard clinical walking tests. The treadmill walking task was repeated at three different timepoints following surgery, i.e., at 3, 6 and 12 months post-TKA. The movements of the trunk were digitized with an inertial sensor to capture the amplitude and the sample entropy (SEn) of the lateral body sway that were evaluated in separate ANOVAs. RESULTS: Before surgery the TKA group showed larger body sway (P = 0.025) with smaller SEn values (P = 0.038), which both restored to levels of healthy adults in the 12 months following surgery. Systematic correlations between the SEn values and the clinical test scores were found. CONCLUSIONS: The current findings show that movement behavior of the trunk in the fronto-parallel plane was affected by knee osteoarthritis and suggest that the predictability of the lateral body sway may serve as an index of recovery after TKA.

Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain.

Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09954-y.

Complexity decline of hippocampal CA1 circuit model due to cholinergic deficiency associated with Alzheimer's disease.

A hallmark of Alzheimer's disease (AD) is cholinergic system dysfunction, directly affecting the hippocampal neurons. Previous experiments have demonstrated that reduced complexity is one significant effect of AD on electroencephalography (EEG). Motivated by these, this study explores reduced EEG complexity of cholinergic deficiency in AD by neurocomputation. We first construct a new hippocampal CA1 circuit model with cholinergic action. M-current IM and calcium-activated potassium current IAHP are newly introduced in the model to describe cholinergic input from the medial septum. Then, by enhancing IM and IAHP to mimic cholinergic deficiency, how cholinergic deficiency influences the model complexity is investigated by sample entropy (SampEn) and approximate entropy (ApEn). Numerical results show a more severe cholinergic deficit with lower model complexity. Furthermore, we conclude that the decline of SampEn and ApEn is due to the greatly diminished excitability of model neurons. These suggest that decreased neuronal excitability due to cholinergic impairment may contribute to reduced EEG complexity in AD. Subsequently, statistical analysis between simulated AD patients and normal control (NC) groups demonstrates that SampEn and auto-mutual-information (AMI) decrease rates significantly differ. Compared to NC, AD patients have a lower SampEn and a less negative AMI decline rate. These imply a low rate of new-generation information in AD brains with cholinergic deficits. Interestingly, the statistical correlation between SampEn and AMI is analyzed, and they have a large negative Pearson correlation coefficient. Thus, AMI reduction rates may be a complementary tool for complex analysis. Our modeling and complex analysis are expected to provide a deeper understanding of the reduced EEG complexity resulting from cholinergic deficiency.

Exploitation of Bio-Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images.

In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper, through clustering techniques like fuzzy c-means (FCM), possibilistic fuzzy c-means (PFCM), and possibilistic c means (PCM) and sample entropy features are extracted from normal and cirrhotic liver ultrasonic images. The extracted features are classified as normal and cirrhotic through the Gaussian mixture model (GMM), Softmax discriminant classifier (SDC), harmonic search algorithm (HSA), SVM (linear), SVM (RBF), SVM (polynomial), artificial algae optimization (AAO), and hybrid classifier artificial algae optimization (AAO) with Gaussian mixture mode (GMM). The classifiers' performances are compared based on accuracy, F1 Score, MCC, F measure, error rate, and Jaccard metric (JM). The hybrid classifier AAO-GMM, with the PFCM feature, outperforms the other classifiers and attained an accuracy of 99.03% with an MCC of 0.90.

[A study on the effects of transcranial direct current stimulation combined with motor imagery on brain function based on electroencephalogram and near infrared spectrum].

Motor imagery is often used in the fields of sports training and neurorehabilitation for its advantages of being highly targeted, easy to learn, and requiring no special equipment, and has become a major research paradigm in cognitive neuroscience. Transcranial direct current stimulation (tDCS), an emerging neuromodulation technique, modulates cortical excitability, which in turn affects functions such as locomotion. However, it is unclear whether tDCS has a positive effect on motor imagery task states. In this paper, 16 young healthy subjects were included, and the electroencephalogram (EEG) signals and near-infrared spectrum (NIRS) signals of the subjects were collected when they were performing motor imagery tasks before and after receiving tDCS, and the changes in multiscale sample entropy (MSE) and haemoglobin concentration were calculated and analyzed during the different tasks. The results found that MSE of task-related brain regions increased, oxygenated haemoglobin concentration increased, and total haemoglobin concentration rose after tDCS stimulation, indicating that tDCS increased the activation of task-related brain regions and had a positive effect on motor imagery. This study may provide some reference value for the clinical study of tDCS combined with motor imagery.

The Amount and Pattern of Reciprocal Compensations Predict Performance Stability in a Visually Guided Finger Force Production Task.

Previous work suggests that synergistic activity among motor elements implicated in force production tasks underlies enhanced performance stability associated with visual feedback. A hallmark of synergistic activity is reciprocal compensation, that is, covariation in the states of motor elements that stabilizes critical performance variables. The present study examined if characteristics of reciprocal compensation are indicators of individuals' capacity to respond adaptively to variations in the resolution of visual feedback about criterion performance. Twenty healthy adults (19.25 ± 1.25 years; 15 females and five males) pressed two sensors with their index fingers to produce a total target force equivalent to 20% of their maximal voluntary contraction under nine conditions that differed in the spatial resolution of real-time feedback about their performance. By combining within-trial uncontrolled manifold and sample entropy analyses, we quantified the amount and degree of irregularity (i.e., non-repetitiveness) of reciprocal compensations over time. We found a U-shaped relationship between performance stability and gain. Importantly, this relationship was moderated by the degree of irregularity of reciprocal compensation. Lower irregularity in reciprocal compensation patterns was related to individuals' capacity to maintain (or minimize losses in) performance under changes in feedback resolution. Results invite future investigation into how interindividual variations in reciprocal compensation patterns relate to differences in control strategies supporting adaptive responses in complex, visually guided motor tasks.

The use of nonlinear analysis in understanding postural control: A scoping review.

Nonlinear analyses have emerged as an approach to unraveling the intricate dynamics and underlying mechanisms of postural control, offering insights into the complex interplay of physiological and biomechanical factors. However, achieving a comprehensive understanding of the application of nonlinear analysis in postural control studies remains a challenge due to the various nonlinear measurement methods currently available. Thus, this scoping review aimed to identify existing nonlinear analyses used to study postural control in both dynamic and quiet tasks, and to summarize and disseminate the available literature on the use of nonlinear analysis in postural control. For this purpose, a scoping review was conducted and reported following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) Checklist and Explanation. Searches were conducted up to July 2023 on PubMed/Medline, Embase, CINAHL, Web of Science, and Google Scholar databases, resulting in the inclusion of 397 unique studies. The main classes employed among the studies were entropy-based, fractal-based, quantification of recurrence plots, and quantification of stability, with a total of 91 different algorithms distributed among these classes. The most common condition used to study postural control was quiet standing, followed by dynamic standing and gait tasks. Although various algorithms were utilized for this purpose, sample entropy was employed in 43% of studies to explore mechanisms related to postural control. Among them, 28% were in quiet standing, 3.27% were in dynamic standing, and 4.78% to study postural control during the gait. The results also provide insights into nonlinear analysis for future studies, concerning the complexity and interactions within the postural control system across various task demands.

Comparative analysis of resting-state EEG-based multiscale entropy between schizophrenia and bipolar disorder.

BACKGROUND: Studies that use nonlinear methods to identify abnormal brain dynamics in patients with psychiatric disorders are limited. This study investigated brain dynamics based on EEG using multiscale entropy (MSE) analysis in patients with schizophrenia (SZ) and bipolar disorder (BD). METHODS: The eyes-closed resting-state EEG data were collected from 51 patients with SZ, 51 patients with BD, and 51 healthy controls (HCs). Patients with BD were further categorized into type I (n = 23) and type II (n = 16), and then compared with patients with SZ. A sample entropy-based MSE was evaluated from the bilateral frontal, central, and parieto-occipital regions using 30-s artifact-free EEG data for each individual. Correlation analyses of MSE values and psychiatric symptoms were performed. RESULTS: For patients with SZ, higher MSE values were observed at higher-scale factors (i.e., 41-70) across all regions compared with both HCs and patients with BD. Furthermore, there were positive correlations between the MSE values in the left frontal and parieto-occipital regions and PANSS scores. For patients with BD, higher MSE values were observed at middle-scale factors (i.e., 13-40) in the bilateral frontal and central regions compared with HCs. Patients with BD type I exhibited higher MSE values at higher-scale factors across all regions compared with those with BD type II. In BD type I, positive correlations were found between MSE values in all left regions and YMRS scores. CONCLUSIONS: Patients with psychiatric disorders exhibited group-dependent MSE characteristics. These results suggest that MSE features may be useful biomarkers that reflect pathophysiological characteristics.

Wearable laser Doppler flowmetry for non-invasive assessment of diabetic foot microcirculation: methodological considerations and clinical implications.

Significance: Type 2 diabetes mellitus (T2DM) is a global health concern with significant implications for vascular health. The current evaluation methods cannot achieve effective, portable, and quantitative evaluation of foot microcirculation. Aim: We aim to use a wearable device laser Doppler flowmetry (LDF) to evaluate the foot microcirculation of T2DM patients at rest. Approach: Eleven T2DM patients and twelve healthy subjects participated in this study. The wearable LDF was used to measure the blood flows (BFs) for regions of the first metatarsal head (M1), fifth metatarsal head (M5), heel, and dorsal foot. Typical wavelet analysis was used to decompose the five individual control mechanisms: endothelial, neurogenic, myogenic, respiratory, and heart components. The mean BF and sample entropy (SE) were calculated, and the differences between diabetic patients and healthy adults and among the four regions were compared. Results: Diabetic patients showed significantly reduced mean BF in the neurogenic (p=0.044) and heart (p=0.001) components at the M1 and M5 regions (p=0.025) compared with healthy adults. Diabetic patients had significantly lower SE in the neurogenic (p=0.049) and myogenic (p=0.032) components at the M1 region, as well as in the endothelial (p<0.001) component at the M5 region and in the myogenic component at the dorsal foot (p=0.007), compared with healthy adults. The SE in the myogenic component at the dorsal foot was lower than at the M5 region (p=0.050) and heel area (p=0.041). Similarly, the SE in the heart component at the dorsal foot was lower than at the M5 region (p=0.017) and heel area (p=0.028) in diabetic patients. Conclusions: This study indicated the potential of using the novel wearable LDF device for tracking vascular complications and implementing targeted interventions in T2DM patients.

Sensory reweighting of postural control requires distinct rambling and trembling sway adaptations.

BACKGROUND: Implementation of the Sensory Organization Test (SOT) under the rambling-trembling (RM-TR) framework allows for an examination of both individual sensory contributions and compensatory mechanisms, a valuable insight in research and clinical settings. Such investigation could substantially improve our ability to assess and treat fall risk in older adults and people living with neurological disorders. RESEARCH QUESTION: How are RM and TR components of sway influenced by SOT-induced challenges in healthy adults? METHODS: Twenty-three healthy adults (27.4±8 years; 10 male) volunteered to participate in this cross-sectional study. Each participant completed a VR-based SOT program, which included six conditions with varied visual environments (normal, blacked-out, conflict) and support surfaces (stable, unstable foam), while a force plate captured forces at the plantar surface. Center of pressure (COP) was calculated and decomposed into RM-TR components. For each time series, range, root-mean-square (RMS) and sample entropy (SampEn) were extracted. Individual contributions of somatosensation, vision, and vestibular sense, as well as the preference ratio, were calculated. Repeated measures ANOVA were used to compare the effects of time series type (COP, RM, TR) and SOT condition. Paired t-tests were used to assess the difference in preference ratio between RM and TR components. RESULTS AND SIGNIFICANCE: TR sway behavior was impacted significantly by the sensory challenges induced by the SOT procedure, while RM was largely unaffected. Such findings are characteristic of healthy individuals, capable of competently re-weighting sensory input, but still facing challenge-based adaptations. Additionally, the mediolateral SampEn preference ratio was higher in TR compared to RM, indicating potential differences in compensation strategies between supraspinal and spinal/peripheral control mechanisms. These findings serve as a foundation for future RM-TR analyses using SOT procedures, aiding in our ability to implement targeted diagnostic and treatment methods, ultimately reducing the incidence of falls in aging and individuals with neurological conditions.

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine.

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope's working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time-frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope's output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time-frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope's output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10-3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h.