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.

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.

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.