Fig. 3. Design of the MGA algorithm.

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Fig. 3. Design of the MGA algorithm. Design of the MGA algorithm. (A) Stick diagram decomposition of whole-body movements, continuous CoP trajectory, and EMG activity of ankle muscles during standing with upward forces ranging from 25 to 60% of body weight support (5% increments) for a nonambulatory individual with a SCI. (B) Plot showing the relationship between the upward force and the Euclidian distance between the data of the participant shown in (A) and healthy subjects in the space defined by PC1 and PC2 (PCA performed on 15 parameters). The minimal distance (blue dot) was defined as the optimal upward force. (C) The measured variables were fed into an artificial neural network that calculated the correction of body weight support (Δ, upward force in percentage of body weight) to facilitate gait. The plot shows the relationship between the experimentally determined correction and the prediction of the neural network. Each dot corresponds to a given condition of upward force for a subject with SCI or stroke who contributed to the training or test data set. The histogram plot reports the occurrence rate of errors in the prediction of the corrections calculated by the artificial neural network. (D) Three-dimensional plots reporting the relationships between the upward force, the optimal forward force, and the speed. Each data point corresponds to values measured in the simulations and in subjects with SCI or stroke. To include data from subjects with varying biometry, the speed is represented as the Froude number, which takes into account the length of the leg to normalize the speed. A polynomial function was fitted through both simulated and experimental data points. Jean-Baptiste Mignardot et al., Sci Transl Med 2017;9:eaah3621 Published by AAAS