Learn More
We have developed a time-varying, parallel- cascade system identification algorithm to separate joint stiffness into intrinsic and reflex components at each point in time throughout rapid movements. The components are identified using an iterative algorithm in which intrinsic and reflex dynamics are identified using separate time-varying (TV) techniques(More)
It has been postulated that the central nervous system (CNS) can tune the mechanical behavior of a joint by altering reflex stiffness in a task-dependant manner. However, most of the evidence supporting this hypothesis has come from the analysis of H-reflexes or electromyogram (EMG) responses. Changes in overall stiffness have been documented but, as yet,(More)
Joint stiffness is defined as the dynamic relationship between the position of the joint and torque acting about it. Joint stiffness is composed of two components: intrinsic and reflex stiffness. Measuring the two stiffness components cannot be done simply because the two components appear and change together. A number of approaches have been used to(More)
Joint stiffness defines the dynamic relationship between the position of the joint and the torque acting about it. It consists of two components: intrinsic and reflex stiffness. Many previous studies have investigated joint stiffness in an open-loop environment, because the current algorithm in use is an open-loop algorithm. This paper explores issues(More)
Measurement of joint dynamic stiffness during time-varying conditions is crucial to understand the role of joint mechanics during movement. Stiffness can be separated into intrinsic and reflex components, and are modeled as linear dynamic and Hammerstein systems, respectively. Time-varying identification methods using ensemble data have been developed(More)
Joint impedance is an important property of the human muscular system and plays a role in the control of movement and posture. Previous studies showed that joint impedance varies with the position of the joint and activation level of the surrounding muscles; however, it remains unknown how it varies during movement. Non-parametric algorithms that estimate(More)
Dynamic joint stiffness defines the dynamic relationship between the position of the joint and the torque acting about it; hence it is important in the control of movement and posture. Joint stiffness consists of two components: intrinsic stiffness and reflex stiffness. Measuring intrinsic and reflex torques directly is not possible, thus estimating(More)
System identification of physiological systems poses unique challenges, especially when the structure of the system under study is uncertain. Nonparametric techniques can be useful for identifying system structure, but these typically assume stationarity and require large amounts of data. Both of these requirements are often not easily obtained in the study(More)
This study aimed at testing the reliability and construct validity of a trunk perturbation protocol (TPP) that estimates the intrinsic and reflexive contributions to low-back stiffness. The TPP consists of a series of pseudorandom position-controlled trunk perturbations in an apparatus measuring forces and displacements at the harness surrounding the(More)