  Control strategies for active prostheses or orthoses use sensor inputs to
recognize the user's locomotive intention and generate corresponding control
commands for producing the desired locomotion. In this paper, we propose a
learning-based shared model for predicting ankle-joint motion for different
locomotion modes like level-ground walking, stair ascent, stair descent, slope
ascent, and slope descent without the need to classify between them. Features
extracted from hip and knee joint angular motion are used to continuously
predict the ankle angles and moments using a Feed-Forward Neural Network-based
shared model. We show that the shared model is adequate for predicting the
ankle angles and moments for different locomotion modes without explicitly
classifying between the modes. The proposed strategy shows the potential for
devising a high-level controller for an intelligent prosthetic ankle that can
adapt to different locomotion modes.
