The results of this study provide evidence that the Brazilian IKDC Subjective Knee Form has psychometric properties similar to the original version. In addition, it was a reliable evaluation instrument for patients with knee-related problems.

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Although injuries to the posterolateral corner of the knee were previously considered to be a rare condition, they have been shown to be present in almost 16% of all knee injuries and are responsible for sustained instability and failure of concomitant reconstructions if not properly recognized. Although also once considered to be the “dark side of the knee”, increased knowledge of the posterolateral corner anatomy and biomechanics has led to improved diagnostic ability with better understanding of physical and imaging examinations. The management of posterolateral corner injuries has also evolved and good outcomes have been reported after operative treatment following anatomical reconstruction principles.

The aim of the study was to analyze and compare the residuals obtained from ground reaction force (GRF) models developed using two different neural network configurations (one network with three outputs; and three networks with one output each), based on accelerometer data. Seventeen healthy subjects walked along a walkway, with a force plate embedded, with a three dimensional accelerometer attached to the shank. Multilayer perceptron networks (MLP) models were developed with the 3D accelerometer data as inputs to predict the GRF. The residuals of these models were evaluated graphically and numerically to verify the fitting. A visual analysis of the simulated signals suggests the model was able to adequately predict the GRF. The errors and correlations found in the MLP models for the 3D GRF is at least similar to other studies, although some of them showed higher errors. There was not difference between the two MLP configurations. However, despite the high correlation coefficient and closeness to a normal probability distribution, the residual analysis still presented a higher kurtosis and skewness, suggesting that the inclusion of other variables and the increase of the validation sample size could increase the fitting of the simulation.

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