Extended Data Fig. 1: Weights, inputs, and effects for the data-driven classification model.
From: Personalizing exoskeleton assistance while walking in the real world
The data-driven classifier decoded latent information from human movement that was not otherwise interpretable, allowing exoskeleton assistance to be optimized without laboratory-based measurement equipment. Top row: Model weights and mean inputs. The model compares data from two control laws at a time and associates inputs with higher or lower metabolic rate to estimate which control law resulted in a lower metabolic rate. Inputs comprised differences in ankle angle and ankle angular velocity at 30 different points in the gait cycle and differences in the four control law parameters of peak torque magnitude, peak time, rise time, and fall time. The data-driven model weights that multiply these differences are shown as a background colour of blue or red. Blue indicates that a positive difference is associated with lower metabolic rate, while red indicates that a positive difference is associated with higher metabolic rate. Darker colours indicate greater influence. Black lines depict the average, across all training data, of the differences in inputs. To generate this average, we ordered each pair-wise comparison by metabolic rate, such that inputs from the control law with a higher metabolic rate were always subtracted from those with a lower metabolic rate. Typical values of the model inputs differ, in part because of differences in units, and so the magnitudes of model weights do not correspond well to the contributions of those terms to the classification overall. Bottom row: The classification contributions of each term in the model, averaged over the entire training set. The percent contribution is calculated as the absolute value of the product of the model weight and the input difference, summed over all pair-wise comparisons, divided by the sum over all model terms. For the x-axes, 0% and 100% of the gait cycle refer to the instant of heel strike of the assisted limb at the beginning and end of one stride. Toe-off occurs at about 62% of the gait cycle. For a discussion of the intuitive meaning of the weights and contributions, please see the Methods subsection “Data-driven optimization”.
