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, there is no direct evidence that the CNS can control reflex stiffness independently of the intrinsic stiffness. We have used a novel identification algorithm to estimate intrinsic and reflex stiffness and feed it back to subjects in real-time. Using this biofeedback, subjects could learn to control reflex stiffness independently of intrinsic stiffness. At low torque levels, subjects could vary their reflex stiffness gain by a factor of 4, while maintaining elastic stiffness and torque constant. EMG measurements confirmed that the contraction levels of the ankle muscles remained constant. Further experiments showed that subjects could change their reflexes rapidly on command. Thus, we conclude that the CNS can control reflex stiffness independently and so has great flexibility in adjusting the mechanical properties of a joint to meet functional requirements.

Dynamic joint stiffness defines the dynamic relationship between the position of a joint and the torque acting about it and can be separated into intrinsic and reflex components. Under stationary conditions, these can be identified using a nonlinear parallel-cascade algorithm that models intrinsic stiffness-a linear dynamic response to position-and reflex stiffness-a nonlinear dynamic response to velocity-as parallel pathways. Experiments using this method show that both intrinsic and reflex stiffness depend strongly on the operating point, defined by position and torque, likely because of some underlying nonlinear behavior not modeled by the parallel-cascade structure. Consequently, both intrinsic and reflex stiffness will appear to be time-varying whenever the operating point changes rapidly, as during movement. This paper describes and validates an extension of the parallel-cascade algorithm to time-varying conditions. It describes the ensemble method used to estimate time-varying intrinsic and reflex stiffness. Simulation results demonstrate that the algorithm can track rapid changes in joint stiffness accurately. Finally, the performance of the algorithm in the presence of noise is tested. We conclude that the new algorithm is a powerful new tool for the study of joint stiffness during functional tasks.

Objective: Regulating the impedance of our joints is essential for the effective control of posture and movement. The impedance of a joint is governed mainly by the mechanical properties of the muscle-tendon units spanning it. Many studies have quantified the net impedance of joints but not the specific contributions from the muscles and tendons. The inability to quantify both muscle and tendon impedance limits the ability to determine the causes underlying altered movement control associated with aging, neuromuscular injury, and other conditions that have different effects on muscle and tendon properties. Therefore, we developed a technique to quantify joint, muscle, and tendon impedance simultaneously and evaluated this technique at the human ankle. Methods: We used a single degree of freedom actuator to deliver pseudorandom rotations to the ankle while measuring the corresponding torques. We simultaneously measured the displacement of the medial gastrocnemius muscletendon junction with B-mode ultrasound. From these experimental measurements, we were able to estimate ankle, muscle, and tendon impedance using non-parametric system identification. Results: We validated our estimates by comparing them to previously reported muscle and tendon stiffness, the position-dependent component of impedance, to demonstrate that our technique generates reliable estimates of these properties. Conclusion: Our approach can be used to clarify the respective contributions from the muscle and tendon to the net mechanics of a joint. Significance: This is a critical step forward in the ultimate goal of understanding how muscles and tendons govern ankle impedance during posture and movement.