Analysis of ankle muscle activity: A study on static balance with eyes closed and high-heeled shoes.

BACKGROUND: Changes in sensory afferent interfere with the control of postural stability by the central nervous system. Wearing high-heeled shoes is an example of an external disturbance that changes sensory inputs and results in several postural adjustments to control stability. Thus, our purpose is to investigate the influence of high-heeled shoes and visual absence on maintenance of static balance and on ankle muscle activity among young women. Our hypothesis is that the combination of high-heeled shoes with visual absence lead to an increase of postural sway and of levels of activation of the stabilizing ankle muscles. METHODS: Nine volunteers remained in an unrestrained erect posture on a force platform for collecting of stabilometric and electromyographic parameters in four bipodal conditions: barefoot with open eyes, barefoot with closed eyes, with high heels and open eyes and with high heels and closed eyes. RESULTS: When comparing the experimental condition open and closed eyes with high heels, there were significant differences for all stabilometric variables, except for the confidence ellipse area. Statistical differences were found for the medial gastrocnemius muscle in all comparison pairs with high heels. CONCLUSION: The wearing high-heeled shoes showed to be the most influencing disturbance on static balance. Our findings suggest ankle muscle activity is adapted according to changes of the center of pressure sway and the wearing of high heels changes the muscle activation and postural sway.

Influence of Binaural Beats Stimulation of Gamma Frequency over Memory Performance and EEG Spectral Density.

Similar to short-term memory, working memory cannot hold information for a long period of time. Studies have shown that binaural beats (BB) can stimulate the brain through sound, affecting working memory function. Although the literature is not conclusive regarding the effects of BB stimulation (stim) on memory, some studies have shown that gamma-BB stim (40 Hz) can increase attentional focusing and improve visual working memory. To better understand the relationship between BB stim and memory, we collected electroencephalographic data (EEG) from 30 subjects in 3 phases-a baseline, with gamma-BB stim, and control stim-in a rest state, with eyes closed, and while performing memory tasks. Both EEG data and memory task performance were analyzed. The results showed no significant changes in the memory task performance or the EEG data when comparing experimental and control conditions. We concluded that brain entrainment was not achieved with our parameters of gamma-BB stimulation when analyzing EEG power spectral density (PSD) and memory task performance. Hence, we suggest that other aspects of EEG data, such as connectivity and correlations with task performance, should also be analyzed for future studies.

Wrist Rigidity Evaluation in Parkinson's Disease: A Scoping Review.

(1) Background: One of the main cardinal signs of Parkinson's disease (PD) is rigidity, whose assessment is important for monitoring the patient's recovery. The wrist is one of the joints most affected by this symptom, which has a great impact on activities of daily living and consequently on quality of life. The assessment of rigidity is traditionally made by clinical scales, which have limitations due to their subjectivity and low intra- and inter-examiner reliability. (2) Objectives: To compile the main methods used to assess wrist rigidity in PD and to study their validity and reliability, a scope review was conducted. (3) Methods: PubMed, IEEE/IET Electronic Library, Web of Science, Scopus, Cochrane, Bireme, Google Scholar and Science Direct databases were used. (4) Results: Twenty-eight studies were included. The studies presented several methods for quantitative assessment of rigidity using instruments such as force and inertial sensors. (5) Conclusions: Such methods present good correlation with clinical scales and are useful for detecting and monitoring rigidity. However, the development of a standard quantitative method for assessing rigidity in clinical practice remains a challenge.

Multidimensional Assessment of Individuals with Parkinson's Disease: Development and Structure Validation of a Self-Assessment Questionnaire.

(1) Background: Several instruments are used to assess individuals with Parkinson's disease (PD). However, most instruments necessitate the physical presence of a clinician for evaluation, were not designed for PD, nor validated for remote application. (2) Objectives: To develop and validate a self-assessment questionnaire that can be used remotely, and to assess the respondents' health condition. (3) Methods: A questionnaire, so-called Multidimensional Assessment Questionnaire for Individuals with PD (MAQPD), was developed, administered remotely, and completed by 302 people with PD. MAQPD was validated using factor analysis (FA). The participants' level of impairment was estimated using factor loadings. The scale's accuracy was assessed estimating floor and ceiling effects and Cronbach's alpha. (4) Results: FA suggested classifying the questions into daily activities, cognition, and pain. The respondents did not have extremely severe impairment (most scores ranged from 100 to 180 points), and the factors with the lowest scores were cognition and pain. The instrument had no significant floor or ceiling effects (rates less than 15%), and the Cronbach's alpha value was larger than 0.90. (5) Conclusion: MAQPD is the only remote self-administered tool found in the literature capable of providing a detailed assessment of the general health status of individuals with PD.

Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review.

The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual's interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user's attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.

Measurement of post-stroke spasticity based on tonic stretch reflex threshold: implications of stretch velocity for clinical practice.

PURPOSE: The most commonly used method for the clinical evaluation of spasticity is the modified Ashworth scale (MAS), which is subjective. In this regard, the spasticity assessment through the tonic stretch reflex threshold, which is an objective method, has emerged as an alternative. It is based on the value of the dynamic stretch reflex threshold, which is measured at different stretch velocities. However, by this definition, it is not possible to define the speed at which passive stretches should be performed during evaluation. OBJECTIVE: This study aimed to evaluate whether the speed-variation sequence used to acquire the dynamic stretch reflex threshold influences the tonic stretch reflex threshold (TSRT) and, consequently, the estimation of spasticity by this method. METHODS: Three forms of stretching-variation speed were adopted, i.e., increasing, decreasing, and randomised. The study was performed using 10 post-stroke patients. RESULTS AND CONCLUSIONS: The results showed that the stretch protocols were not all the same and that the method of increasing was most suitable for performing manual passive stretches to evaluate TSRT in these patients. Another analysis was the correlation between MAS and tonic stretch reflex threshold; a weak correlation was observed between the increasing and decreasing methods, and moderate correlation was observed between the random methods. Implications for Rehabilitation We demonstrated that the protocol of execution of passive stretches influences in the measurement of the tonic stretch reflex threshold (TSRT). We recommend the method of increasing velocity for performing manual passive stretches. We also build software with a reliable biological data acquisition system, which makes acquisition and processing of data in real time. In this way, the TSRT is a promising quantitative measure to assess post-stroke spasticity, calculated automatically. We also we provided the use of portable instruments to facilitate the assessment of spasticity in clinical practice.

Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks

Abstract Introduction Historically, assessing the quality of human gait has been a difficult process. Advanced studies can be conducted using modern 3D systems. However, due to their high cost, usage of these 3D systems is still restricted to research environments. 2D systems offer simpler and more affordable solutions. Methods In this study, the gait of 40 volunteers walking on a treadmill was recorded in the sagittal plane, using a 2D motion capture system. The extracted joint angles data were used to create cyclograms. Sections of the cyclograms were used as inputs to artificial neural networks (ANNs), since they can represent the kinematic behavior of the lower body. This allowed for prediction of future states of the moving body. Results The results indicate that ANNs can predict the future states of the gait with high accuracy. Both single point and section predictions were successfully performed. Pearson's correlation coefficient and matched-pairs t-test ensured that the results were statistically significant. Conclusion The combined use of ANNs and simple, accessible hardware is of great value in clinical practice. The use of cyclograms facilitates the analysis, as several gait characteristics can be easily recognized by their geometric shape. The predictive model presented in this paper facilitates generation of data that can be used in robotic locomotion therapy as a control signal or feedback element, aiding in the rehabilitation process of patients with motor dysfunction. The system proposes an interesting tool that can be explored to increase rehabilitation possibilities, providing better quality of life to patients.

An improved approach for measuring the tonic stretch reflex response of spastic muscles.

We propose a new method for detecting the onset of the stretch reflex response for assessment of spasticity based on the Tonic Stretch Reflex Threshold (TSRT). Our strategy relies on a three-stage approach to detect the onset of the reflex EMG activity: (i) Reduction of baseline activity by means of Empirical Mode Decomposition; (ii) Extraction of the complex envelope of the EMG signal by means of Hilbert Transform (HT) and; iii) A double threshold decision rule. Simulated and real EMG data were used to evaluate and compare our method (TSRT-EHD) against three other popular methods described in the literature to assess TSRT ('Kim', 'Ferreira' and 'Blanchette'). Four different groups of signals containing simulated evoked stretch reflex EMG activities were generated: groups A and B without spontaneous EMG activity at rest and signal-to-noise ratio (SNR) of 10dB and 20dB respectively; groups C and D with spontaneous EMG activity at rest, as observed frequently in spastic muscles, and SNR of 10dB and 20dB respectively. The results with simulated data showed a significantly higher accuracy of TSRT-EHD for detecting the onset of the reflex EMG activity in groups C and D when compared to the other methods. Analyses using real data from five post stroke spastic subjects demonstrated that the TSRTs generated by each method were dramatically different from one another. Nevertheless, only TSRT-EHD provided valid measures across all subjects.

Is it possible to identify the gender and age group of adults from gait analysis with hip-knee cyclograms?

INTRODUCTION: Cyclograms are gait analysis tools that characterize the geometric aspect of the pattern of locomotion. Cyclograms are angle-angle diagrams that are very useful for representing cyclic patterns such as walking. This study is based on the hypothesis that parameters extracted from hip-knee cyclograms of individuals walking on a treadmill with 0° and 5° slopes can be used to determine the age group and sex of the volunteers. METHODS: In total, 40 physically active healthy adult volunteers, 20 young people (10 of each gender) and 20 elderly (10 of each gender), were divided into 4 groups, and the average value of area (A), perimeter (P) and the ratio P/√A of cyclogram were calculated, as well as the speed and cadence. RESULTS: The young male (YM) speeds were higher than the elderly male (EM) speeds (p=0.00), and the young female (YF) speeds were higher than the elderly female (EF) speeds (p=0.00). No difference in speed was found between YM and YF (p=0.59) or between EM and EF (p=0.95). The parameters extracted directly from the cyclogram allowed us to distinguish the studied groups according to age group (p<0.05), especially with the treadmill inclined at 5°, but it was not enough to determine gender (p>0.51). CONCLUSION: The hypothesis was partially confirmed because parameters extracted from the hip-knee cyclograms could differentiate volunteers by age group but not gender.

[Use of an artificial neural network to predict the incidence of malaria in the city of Cantá, state of Roraima]. / A utilização de uma rede neural artificial para previsão da incidência da malária no município de Cantá, estado de Roraima.

INTRODUCTION: Malaria is endemic in the Brazilian Amazon region, with different risks for each region. The City of Cantá, State of Roraima, presented one of the largest annual parasite indices in Brazil for the entire study period, with a value always greater than 50. The present study aimed to use an artificial neural network to predict the incidence of malaria in this city in order to assist health coordinators in planning and managing resources. METHODS: Data were collected on the website of the Ministry of Health, SIVEP--Malaria between 2003 and 2009. An artificial neural network was structured with three neurons in the input layer, two intermediate layers and an output layer with one neuron. A sigmoid activation function was used. In training, the backpropagation method was used, with a learning rate of 0.05 and momentum of 0.01. The stopping criterion was to reach 20,000 cycles or a target of 0.001. The data from 2003 to 2008 were used for training and validation. The results were compared with those from a logistic regression model. RESULTS: The results for all periods provided showed that the artificial neural network had a smaller mean square error and absolute error compared with the regression model for the year 2009. CONCLUSIONS: The artificial neural network proved to be adequate for a malaria forecasting system in the city studied, determining smaller predictive values with absolute errors compared to the logistic regression model and the actual values.

A utilização de uma rede neural artificial para previsão da incidência da malária no município de Cantá, estado de Roraima / Use of an artificial neural network to predict the incidence of malaria in the city of Cantá, state of Roraima

INTRODUÇÃO: A malária é uma doença endêmica na Amazônia Legal Brasileira, apresentando riscos diferentes para cada região. O Município de Cantá, no Estado de Roraima, apresentou para todo o período estudado, um dos maiores índices parasitários anuais do Brasil, com valor sempre maior que 50. O presente estudo visa à utilização de uma rede neural artificial para previsão da incidência da malária nesse município, a fim de auxiliar os coordenadores de saúde no planejamento e gestão dos recursos. MÉTODOS: Os dados foram coletados no site do Ministério da Saúde, SIVEP - Malária entre 2003 e 2009. Estruturou-se uma rede neural artificial com três neurônios na camada de entrada, duas camadas intermediárias e uma camada de saída com um neurônio. A função de ativação foi à sigmoide. No treinamento, utilizou-se o método backpropagation, com taxa de aprendizado de 0,05 e momentum 0,01. O critério de parada foi atingir 20.000 ciclos ou uma meta de 0,001. Os dados de 2003 a 2008 foram utilizados para treinamento e validação. Comparam-se os resultados com os de um modelo de regressão logística. RESULTADOS: Os resultados para todos os períodos previstos mostraram-se que as redes neurais artificiais obtiveram um menor erro quadrático médio e erro absoluto quando comparado com o modelo de regressão para o ano de 2009. CONCLUSÕES: A rede neural artificial se mostrou adequada para um sistema de previsão de malária no município estudado, determinando com pequenos erros absolutos os valores preditivos, quando comparados ao modelo de regressão logística e aos valores reais.

INTRODUCTION: Malaria is endemic in the Brazilian Amazon region, with different risks for each region. The City of Cantá, State of Roraima, presented one of the largest annual parasite indices in Brazil for the entire study period, with a value always greater than 50. The present study aimed to use an artificial neural network to predict the incidence of malaria in this city in order to assist health coordinators in planning and managing resources. METHODS: Data were collected on the website of the Ministry of Health, SIVEP - Malaria between 2003 and 2009. An artificial neural network was structured with three neurons in the input layer, two intermediate layers and an output layer with one neuron. A sigmoid activation function was used. In training, the backpropagation method was used, with a learning rate of 0.05 and momentum of 0.01. The stopping criterion was to reach 20,000 cycles or a target of 0.001. The data from 2003 to 2008 were used for training and validation. The results were compared with those from a logistic regression model. RESULTS: The results for all periods provided showed that the artificial neural network had a smaller mean square error and absolute error compared with the regression model for the year 2009. CONCLUSIONS: The artificial neural network proved to be adequate for a malaria forecasting system in the city studied, determining smaller predictive values with absolute errors compared to the logistic regression model and the actual values.