Abstract
As one of the most direct indicators of the transparency between a human and an exoskeleton, interactive force has rarely been fused with electromyography (EMG) in the control of human-exoskeleton systems, the performances of which are largely determined by the accuracy of the continuous joint angle prediction. To achieve intuitive and naturalistic human intent learning, a state space model (SSM) for continuous angle prediction of knee joint is developed. When the influence of the interactive force is often ignored in the existing models of human-exoskeleton systems, interactive force is applied as the measurement model output of the proposed SSM, and the EMG signal is used as the state model input signal to indicate muscle activation. The forward dynamics of joint motion and the human-machine interaction mechanism, i.e., the biomechanical interpretations of the interactive force generation mechanism, are derived as the bases for the state model and measurement model based on Hill’s muscle model and semiphenomenological (SP) muscular model, respectively. Gaussian process (GP)-based nonlinear autoregressive with the exogenous inputs (NARX) model and back-propagation neural network (BPNN) are applied to provide better adaptivity for the SSM in practical applications. Corresponding experimental results demonstrate the validity and superiority of the method.
Highlights
Modern robots, such as rehabilitation robots, try to actively interact with human partners towards achieving a common task
One reason was that the addition of interactive force in our model would improve the performance of joint angle prediction [30]
This paper develops a state space model for continuous joint angle prediction in a human-exoskeleton system
Summary
Modern robots, such as rehabilitation robots, try to actively interact with human partners towards achieving a common task. Two main related problems remain: one is the choice of the learning model for continuous joint angle prediction, the other is the selection of input signals that could reflect both the human motion dynamics and the human-robot interaction state well. The motivation of this paper is to develop a GP-integrated SSM for continuous joint prediction and to provide biomechanical interpretations for its state model and measurement model in human-exoskeleton integrated applications. Interactive force is applied as the measurement model output and the EMG signal is used as the state model input to indicate muscle activation. Features alone.InWhen the impacts of interactive force onNARX the human-exoskeleton measurement practical applications, the GP-based model and back system are considered, the performance of joint angle to prediction. Corresponding experimental results demonstrate the validity and superiority of the method
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have