title: Improving the Transparency of an Exoskeleton Knee Joint Based on the Understanding of Motor Intent Using Energy Kernel Method of EMG.
abstract: Transparent control is still highly challenging for robotic exoskeletons, especially when a simple strategy is expected for a large-impedance device. To improve the transparency for late-phase rehabilitation when "patient-in-charge" mode is necessary, this paper aims at adaptive identification of human motor intent, and proposed an iterative prediction-compensation motion control scheme for an exoskeleton knee joint. Based on the analysis of human-machine interactive mechanism (HMIM) and the semiphenomenological biomechanical model of muscle, an online adaptive predicting controller is designed using a focused time-delay neural network (FTDNN) with the inputs of electromyography (EMG), position and interactive force, where the activation level of muscle is estimated from EMG using a novel energy kernel method. The compensating controller is designed using the normative force-position control paradigm. Initial experiments on the human-machine integrated knee system validated the effectiveness and ease of use of the proposed control scheme.
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.forename: Yan
authors.initials: Y
authors.lastname: Zeng
authors.forename: Yuehong
authors.initials: Y
authors.lastname: Yin
mesh_terms: D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D000067190:Exoskeleton Device; D006801:Humans; D033182:Intention; D007719:Knee Joint / Q000502:physiology*; D008328:Man-Machine Systems; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D016059:Range of Motion, Articular / Q000502:physiology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity