摘要：AbstractThis paper presents a novel approach for continuous gait phase estimation for human level walking, stair ascent and stair descent relying only on the kinematic variables of the shank, which are measurable by a single Inertial Measurement Unit (IMU) placed at the shank. We use data from an experiment with an instrumented stair to train Artificial Neural Networks (ANNs) and to obtain the data necessary for a k-Nearest-Neighbour (kNN) method. Both methods are used for a continuous gait phase estimation separately for each of the three locomotion modes level walking, stair ascent and stair descent. The so called pseudo- velocities are introduced, a substitution for anslational velocities as input values. The presented gait phase estimation with ANNs achieves a good performance (mean absolute error > 6%) for all three locomotion modes for one test subject and is much faster in comparison to a kNN approach. The use of ANNs seams promising regarding performance and speed for a future implementation on an active prosthesis.
关键词：KeywordsGait PhaseGait AnalysisMachine LearningArtificial Neural NetworkLocomotionStair ClimbingAssisted WalkingProsthesisInertial Measurement Unit
