摘要：AbstractJoint stiffness has been extensively used to study joint biomechanics. It can be described by a block-oriented, nonlinear, parallel-cascade structure under quasi-stationary conditions defined by the joint operating point. The model parameters are modulated dramatically during functional tasks where the joint operating point is varied with time. This paper reviews three parametric methods developed by our laboratory to identify joint stiffness: a refined instrumental variable method for transfer function identification of time-invariant stiffness, a MOESP subspace method for state-space identification of time-invariant stiffness, and a linear parameter varying subspace method for time-varying stiffness. The effectiveness of each method is demonstrated using experimental data recorded during posture and movement.
关键词：KeywordsBiomedical SystemsIdentification AlgorithmsParameter EstimationSubspace MethodsLeast-Squares MethodsState-Space ModelsSystem Transfer FunctionsMechanical PropertiesNeural ControlNonlinear Systems
