A novel least-squares method to characterize in-vivo joint functional passive regional stiffness zones.

OBJECTIVE

To present and evaluate a method to objectively quantify the functional regions of joint lumped passive stiffness.

BACKGROUND

Joint passive stiffness has an important clinical role in constraining the degrees of freedom at a given joint. Links between passive stiffness and injury, pathology and function may be better understood if joint passive stiffness can be accurately quantified. Thus, a technique was developed to objectively partition passive stiffness curves into 3 linear regions (low, transition and high stiffness).

METHODS

The passive stiffness of the lumbar spine is presented as an example. Simulated data was used to determine the sensitivity of the method to Gaussian white noise in force measurements. An experimentally determined lumbar passive flexion curve was used to demonstrate the technique on human data. Breakpoint analysis was employed on the resulting moment-angle cures to partition the curve into low, transition and high stiffness zones.

RESULTS

The proposed method was successful at discriminating between the three stiffness zones and quantifying the passive stiffness within each zone. The algorithm had difficulty determining parameters in the low-stiffness zone in the presence of noise.

CONCLUSIONS

The proposed method can be used as an objective method to investigate passive stiffness. Breakpoint Analysis can identify the three functional linear zones of passive stiffness. The slopes of these linear regions are then used as a measure of passive stiffness, which have applications in clinical populations and research studies, to assess time varying responses, or changes in stiffness following an intervention.