Reduced complexity of force and muscle activity during low level isometric contractions of the ankle in diabetic individuals

Educational Psychology Analysis Method for Extracting Students' Facial Information Based on Image Big Data

At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. There are not many researches on the application of artificial intelligence-based neural network facial expression recognition technology in practical teaching. With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. Method. A Hadoop cluster consisting of 3 nodes is built on the Linux system, and the environment required for Opencv execution is compiled for each node, which provides support for subsequent parallel optimization, feature extraction, feature fusion, and recognition of student facial images. The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. The color features, texture features, and Sift features of students' facial images and common distractors were analyzed. A parallel extraction framework of student facial image features is designed, and based on this, the student facial image feature extraction under Hadoop platform is implemented. This paper proposes a dynamic sequential facial expression recognition method that combines shallow and deep features with an attention mechanism. The relative position of facial landmarks and local area texture features based on FACS represent shallow-level features. At the same time, the structure of ALexNet is improved to extract the deep features of sequence images to express high-level semantic features. The effectiveness of the facial expression recognition system is improved by introducing three attention mechanisms: self-attention, weight-attention, and convolutional attention. Results/Discussion. Through the analysis of the teaching effect, we found that when teachers can obtain the correct student's academic mood, they can intervene on the students' positive academic mood. The purpose of the intervention is to improve the positive academic emotions of students. After the students receive the intervention, their academic emotions are also improved and are positively correlated with their academic performance. Through the analysis of teaching effect, the research can achieve the predetermined goal. From the specific teaching effect of this study, it is concluded that in classroom teaching, teachers should devote energy to intervene in students' positive academic emotions, in order to improve students' positive academic emotions, which will improve students' academic performance and teaching.

Stepping Back to Minimal Footwear: Applications Across the Lifespan

Minimal footwear has existed for tens of thousands of years and was originally designed to protect the sole of the foot. Over the past 50 yr, most footwear has become increasingly more cushioned and supportive. Here, we review evidence that minimal shoes are a better match to our feet, which may result in a lower risk of musculoskeletal injury.

Physiological complexity: influence of ageing, disease and neuromuscular fatigue on muscle force and torque fluctuations

NEW FINDINGS

What is the topic of this review? Physiological complexity in muscle force and torque fluctuations, specifically the quantification of complexity, how neuromuscular complexityis altered by perturbations and the potential mechanism underlying changes in neuromuscular complexity. What advances does it highlight? The necessity to calculate both magnitude- and complexity-based measures for the thorough evaluation of force/torque fluctuations. Also the need for further research on neuromuscular complexity, particularly how it relates to the performance of functional activities (e.g. manual dexterity, balance, locomotion).

ABSTRACT

Physiological time series produce inherently complex fluctuations. In the last 30 years, methods have been developed to characterise these fluctuations, and have revealed that they contain information about the function of the system producing them. Two broad classes of metrics are used: (1) those which quantify the regularity of the signal (e.g. entropy metrics); and (2) those which quantify the fractal properties of the signal (e.g. detrended fluctuation analysis). Using these techniques, it has been demonstrated that ageing results in a loss of complexity in the time series of a multitude of signals, including heart rate, respiration, gait and, crucially, muscle force or torque output. This suggests that as the body ages, physiological systems become less adaptable (i.e. the systems' ability to respond rapidly to a changing external environment is diminished). More recently, it has been shown that neuromuscular fatigue causes a substantial loss of muscle torque complexity, a process that can be observed in a few minutes, rather than the decades it requires for the same system to degrade with ageing. The loss of torque complexity with neuromuscular fatigue appears to occur exclusively above the critical torque (at least for tasks lasting up to 30 min). The loss of torque complexity can be exacerbated with previous exercise of the same limb, and reduced by the administration of caffeine, suggesting both peripheral and central mechanisms contribute to this loss. The mechanisms underpinning the loss of complexity are not known but may be related to altered motor unit behaviour as the muscle fatigues.

Neural Signatures of Handgrip Fatigue in Type 1 Diabetic Men and Women

Type 1 diabetes (T1D) is associated with reduced muscular strength and greater muscle fatigability. Along with changes in muscular mechanisms, T1D is also linked to structural changes in the brain. How the neurophysiological mechanisms underlying muscle fatigue is altered with T1D and sex related differences of these mechanisms are still not well investigated. The aim of this study was to determine the impact of T1D on the neural correlates of handgrip fatigue and examine sex and T1D related differences in neuromuscular performance parameters, neural activation and functional connectivity patterns between the motor regions of the brain. Forty-two adults, balanced by condition (healthy vs T1D) and sex (male vs female), and performed submaximal isometric handgrip contractions until voluntary exhaustion. Initial strength, endurance time, strength loss, force variability, and complexity measures were collected. Additionally, hemodynamic responses from motor-function related cortical regions, using functional near-infrared spectroscopy (fNIRS), were obtained. Overall, females exhibited lower initial strength (p < 0.0001), and greater strength loss (p = 0.023) than males. While initial strength was significantly lower in the T1D group (p = 0.012) compared to the healthy group, endurance times and strength loss were comparable between the two groups. Force complexity, measured as approximate entropy, was found to be lower throughout the experiment for the T1D group (p = 0.0378), indicating lower online motor adaptability. Although, T1D and healthy groups fatigued similarly, only the T1D group exhibited increased neural activation in the left (p = 0.095) and right (p = 0.072) supplementary motor areas (SMA) over time. A sex × condition × fatigue interaction effect (p = 0.044) showed that while increased activation was observed in both T1D females and healthy males from the Early to Middle phase, this was not observed in healthy females or T1D males. These findings demonstrate that T1D adults had lower adaptability to fatigue which they compensated for by increasing neural effort. This study highlights the importance of examining both neural and motor performance signatures when investigating the impact of chronic conditions on neuromuscular fatigue. Additionally, the findings have implications for developing intervention strategies for training, rehabilitation, and ergonomics considerations for individuals with chronic conditions.

Linear mixed-effects models for the analysis of high-density electromyography with application to diabetic peripheral neuropathy

This article demonstrates the power and flexibility of linear mixed-effects models (LMEMs) to investigate high-density surface electromyography (HD-sEMG) signals. The potentiality of the model is illustrated by investigating the root mean squared value of HD-sEMG signals in the tibialis anterior muscle of healthy (n = 11) and individuals with diabetic peripheral neuropathy (n = 12). We started by presenting the limitations of traditional approaches by building a linear model with only fixed effects. Then, we showed how the model adequacy could be increased by including random effects, as well as by adding alternative correlation structures. The models were compared with the Akaike information criterion and the Bayesian information criterion, as well as the likelihood ratio test. The results showed that the inclusion of the random effects of intercept and slope, along with an autoregressive moving average correlation structure, is the one that best describes the data (p < 0.01). Furthermore, we demonstrate how the inclusion of additional variance structures can accommodate heterogeneity in the residual analysis and therefore increase model adequacy (p < 0.01). Thus, in conclusion, we suggest that adopting LMEM to repeated measures such as electromyography can provide additional information from the data (e.g. test for alternative correlation structures of the RMS value), and hence provide new insights into HD-sEMG-related work.

Study protocol for a randomized controlled trial on the effect of the Diabetic Foot Guidance System (SOPeD) for the prevention and treatment of foot musculoskeletal dysfunctions in people with diabetic neuropathy: the FOotCAre (FOCA) trial I

BACKGROUND

This study is part of a series of two clinical trials. Taking into account the various musculoskeletal alterations of the foot and ankle in people with diabetic peripheral neuropathy (DPN) and the need for self-care to avoid more serious dysfunctions and complications, a self-manageable exercise protocol that focuses on strengthening the foot muscles is presented as a potentially effective preventive method for foot and gait complications. The aim of this trial is to investigate the effect of a customized rehabilitation technology, the Diabetic Foot Guidance System (SOPeD), on DPN status, functional outcomes and gait biomechanics in people with DPN.

METHODS/DESIGN

Footcare (FOCA) trial I is a randomized, controlled and parallel two-arm trial with blind assessment. A total of 62 patients with DPN will be allocated into either a control group (recommended foot care by international consensus with no foot exercises) or an intervention group (who will perform exercises through SOPeD at home three times a week for 12 weeks). The exercise program will be customized throughout its course by a perceived effort scale reported by the participant after completion of each exercise. The participants will be assessed at three different times (baseline, completion at 12 weeks, and follow-up at 24 weeks) for all outcomes. The primary outcomes will be DPN symptoms and severity classification. The secondary outcomes will be foot-ankle kinematics and kinetic and plantar pressure distribution during gait, tactile and vibration sensitivities, foot health and functionality, foot strength, and functional balance.

DISCUSSION

As there is no evidence about the efficacy of rehabilitation technology in reducing DPN symptoms and severity or improving biomechanical, clinical, and functional outcomes for people with DPN, this research can contribute substantially to clarifying the therapeutic merits of software interventions. We hope that the use of our application for people with DPN complications will reduce or attenuate the deficits caused by DPN. This rehabilitation technology is freely available, and we intend to introduce it into the public health system in Brazil after demonstrating its effectiveness.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04011267. Registered on 8 July 2019.

Effects of Nonstationarity on Muscle Force Signals Regularity During a Fatiguing Motor Task

Physiological signals present fluctuations that can be assessed from their temporal structure, also termed complexity. The complexity of a physiological signal is usually quantified using entropy estimators, such as Sample Entropy. Recent studies have shown a loss of force signal complexity with the development of neuromuscular fatigue. However, these studies did not consider the stationarity of the force signals which is an important prerequisite of Sample Entropy measurements. Here, we investigated the effect of the potential nonstationarity of force signals on the kinetics of neuromuscular fatigue-induced change in force signal's complexity. Eleven men performed submaximal intermittent isometric contractions of knee extensors until exhaustion. Neuromuscular fatigue was assessed from changes in voluntary and electrically evoked contractions. Sample Entropy values were computed from submaximal force signals throughout the fatiguing task. The Dickey-Fuller test was used to statistically investigate the stationarity of force signals and the Empirical Mode Decomposition was applied to detrend these signals. Maximal voluntary force, central voluntary activation and muscle twitch decreased throughout the task (all ), indicating the development of global, central and peripheral fatigue, respectively. We found an increase in Sample Entropy with fatigue ( p = 0.024 ) when not considering the nonstationarity of force signals (i.e., 43% of nonstationary signals). After applying the Empirical Mode Decomposition, we found a decrease in Sample Entropy with fatigue ( p = 0.002 ). These findings confirm the presence of nonstationarity in force signals during submaximal isometric contractions which influences the kinetics of Sample Entropy with neuromuscular fatigue.

Impact of diabetic neuropathy severity on foot clearance complexity and variability during walking

BACKGROUND

The control of foot trajectory during swing phase is important to achieve safe clearance with the ground. Complexity of a physiological control system arises from the interaction of structural units and regulatory feedback loops that operate to enable the organism to adapt to a non-static environment. Diabetic polyneuropathy (DPN) impairs peripheral feedback inputs and alters ankle control during gait, which might affect toe clearance (ToC) parameters and its complexity, predisposing DPN-subjects to tripping and falling.

RESEARCH QUESTION

How do different DPN-severity degrees change ToC trajectory and minimum ToC, and its complexity during gait of diabetic subjects?

METHODS

15 healthy controls and 69 diabetic subjects were assessed and classified into DPN-severity degrees by an expert fuzzy model: absent (n = 26), mild (n = 21) and severe (n = 22). Three-dimensional kinematics was measured during comfortable walking. ToC was the minimum vertical distance between the marker placed at the first metatarsal head and the ground during swing. Mean ToC, ToC standard deviation (SD) between trials, and sample entropy (SaEn) and standard deviation (SD) of ToC trajectory were calculated from the ToC temporal series. ANOVA and ANCOVA (with the walking speed as the covariate) and Bonferroni pairwise post-hoc tests (P < 0.05) were used to compare groups.

RESULTS

Mean ToC and ToC SD did not show differences between groups (ANCOVA F = 0.436; df = 3; P = 0.705; F=1.719; df=3; P=0.170, respectively). ToC trajectory SD also did not show differences between groups (ANCOVA F = 3.98; df = 3; P = 0.755). Severe-DPN subjects showed higher ToC_Traj_SaEn than controls (ANCOVA F=2.60; df=3; P = 0.05).

SIGNIFICANCE

Severe-DPN subjects showed a more complex pattern of overall foot-ankle trajectory in swing phase in comparison to controls, although did not present lower minimum ToC values. The higher complexity of ToC might lead to an increase in the motor system output (more strategies, increase in variability), resulting in a more unstable system and selected motor strategies.

Exercise training in older adults, what effects on muscle force control? A systematic review of randomized clinical trials

AIM

To determine the magnitude of the effects of different exercise training (ET) modalities on variables of muscle force control in older adults.

METHODS

Relevant articles were searched in PubMed, Web of Science, Science Direct and Scopus, using the keywords: Aged AND "Exercise Movement Techniques" AND ("Complexity of torque" OR "Complexity of force" OR "Variability of torque" OR "Variability of force" OR "Force Steadiness" OR "Force fluctuations"). To be included in the full analysis, the studies had to be randomized controlled trials in which older adults were submitted to ET programs and muscle force control assessment.

RESULTS

The searches resulted in 702 articles from which 6 met all the inclusion criteria. The trials involved 171 healthy and functionally limited older adults (71.64 ± 1.53 years). Studies included resistance, steadiness and functional training programs. Training sessions were 2-3 time per week, lasted 6-16 months with intensities determined as percentage of the one repetition maximum loads. There is a heterogeneity regarding experimental set-up and data analysis parameters between studies. The findings show an improved muscle force control in older adults after ET. Such response is better evidenced by the assessment of the coefficient of variation (CV) of the force signals. There is moderate evidence that resistance training programs are effective to decrease CV of knee extensor force signals at lower force targets.

CONCLUSIONS

The findings from this review suggest that ET programs are effective to improve muscle force control in older adults.

Fatigue-Mediated Loss of Complexity is Contraction-Type Dependent in Vastus Lateralis Electromyographic Signals

The purpose of this study was to investigate the effect of fatigue status and contraction type on complexity of the surface electromyographic (sEMG) signal. Twelve females (mean age ± SD = 21.1 ± 1.4 years) performed three fatigue-inducing protocols that involved maximal concentric, eccentric, or isometric knee-extensor contractions over three non-consecutive sessions. Pre- and post-fatigue assessments were also completed each session and consisted of three maximal efforts for each type of contraction. Complexity of sEMG signals from the vastus lateralis was assessed using Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA) as expressed using the scaling exponent α. The results showed that fatigue decreased (p < 0.05) sEMG complexity as indicated by decreased SampEn (non-fatigued: 1.57 ± 0.22 > fatigued: 1.46 ± 0.25) and increased DFA α (non-fatigued: 1.27 ± 0.26 < fatigued: 1.32 ± 0.23). In addition, sEMG complexity was different among contraction types as indicated by SampEn (concentric: 1.58 ± 0.22 > eccentric: 1.47 ± 0.27 and isometric: 1.50 ± 0.21) and DFA α (concentric: 1.27 ± 0.18 < isometric: 1.32 ± 0.18). Thus, these findings suggested sEMG complexity is affected by fatigue status and contraction type, with the degree of fatigue-mediated loss of complexity dependent on the type of contraction used to elicit fatigue.

Voluntary control of breathing affects center of pressure complexity during static standing in healthy older adults

Background Physiological/biomechanical systems display high degrees of complexity in their corresponding physiological and/or biomechanical outputs, indicative of normal healthy physiological functioning, though little attention has been paid to potential mechanisms which may affect complexity. Center of pressure (CoP) dynamics also display high degrees of complexity and may be affected via altered respiratory-motor interactions such as during voluntary control of breathing. Purpose The purpose of this study was to investigate the differences in the complexity of CoP dynamics during autonomous vs. voluntary control of breathing and between different voluntarily controlled breathing conditions. Methods Center of pressure recordings were taken from 18 older adults during static standing under three different breathing conditions: 1) neutral breathing, 2) abdominal breathing, and 3) thoracic breathing, the first constituting the autonomous breathing condition and the latter two constituting voluntarily controlled breathing conditions. CoP dynamics were quantified using sample entropy, standard deviation, 95% sway area, and average radial velocity. Repeated measure MANOVAs were used to assess the effect of breathing on CoP dynamics, with top-down application of ANOVAs and pairwise comparison as needed. Results Voluntary control of breathing during both conditions resulted in significantly higher CoP variability and lower sample entropy than during autonomous control of breathing in the mediolateral direction, indicating less complex dynamics and loss of system control. No significant differences between voluntary breathing conditions were observed. Conclusion Voluntary control of breathing significantly affected on CoP dynamics during static standing. The complexity of the postural control system may be affected via alterations in respiratory-motor interactions.