Joshua M. Rosvold

Reproduction of In Vivo Motion Using a Parallel Robot

Although alterations in knee joint loading resulting from injury have been shown to influence the development of osteoarthritis, actual in vivo loading conditions of the joint remain unknown. A method for determining in vivo ligament loads by reproducing joint specific in vivo kinematics using a robotic testing apparatus is described. The in vivo kinematics of the ovine stifle joint during walking were measured with 3D optical motion analysis using markers rigidly affixed to the tibia and femur. An additional independent single degree of freedom measuring device was also used to record a measure of motion. Following sacrifice, the joint was mounted in a robotic/universal force sensor test apparatus and referenced using a coordinate measuring machine. A parallel robot configuration was chosen over the conventional serial manipulator because of its greater accuracy and stiffness. Median normal gait kinematics were applied to the joint and the resulting accuracy compared. The mean error in reproduction as determined by the motion analysis system varied between 0.06 mm and 0.67 mm and 0.07 deg and 0.74 deg for the two individual tests. The mean error measured by the independent device was found to be 0.07 mm and 0.83 mm for the two experiments, respectively. This study demonstrates the ability of this system to reproduce in vivo kinematics of the ovine stifle joint in vitro. The importance of system stiffness is discussed to ensure accurate reproduction of joint motion.

A computational method to differentiate normal individuals, osteoarthritis and rheumatoid arthritis patients using serum biomarkers

The objective of this study was to develop a method for categorizing normal individuals (normal, n = 100) as well as patients with osteoarthritis (OA, n = 100), and rheumatoid arthritis (RA, n = 100) based on a panel of inflammatory cytokines expressed in serum samples. Two panels of inflammatory proteins were used as training sets in the construction of two separate artificial neural networks (ANNs). The first training set consisted of all proteins (38 in total) and the second consisted of only the significantly different proteins expressed (12 in total) between at least two patient groups. Both ANNs obtained high levels of sensitivity and specificity, with the first and second ANN each diagnosing 100% of test set patients correctly. These results were then verified by re-investigating the entire dataset using a decision tree algorithm. We show that ANNs can be used for the accurate differentiation between serum samples of patients with OA, a diagnosed RA patient comparator cohort and normal/control cohort. Using neural network and systems biology approaches to manage large datasets derived from high-throughput proteomics should be further explored and considered for diagnosing diseases with complex pathologies.

Tendon mineralization is accelerated bilaterally and creep of contralateral tendons is increased after unilateral needle injury of murine achilles tendons

Heterotopic mineralization may result in tendon weakness, but effects on other biomechanical responses have not been reported. We used a needle injury, which accelerates spontaneous mineralization of murine Achilles tendons, to test two hypotheses: that injured tendons would demonstrate altered biomechanical responses; and that unilateral injury would accelerate mineralization bilaterally. Mice underwent left hind (LH) injury (I; n = 11) and were euthanized after 20 weeks along with non‐injured controls (C; n = 9). All hind limbs were examined by micro computed tomography followed by biomechanical testing (I = 7 and C = 6). No differences were found in the biomechanical responses of injured tendons compared with controls. However, the right hind (RH) tendons contralateral to the LH injury exhibited greater static creep strain and total creep strain compared with those LH tendons (p ≤ 0.045) and RH tendons from controls (p ≤ 0.043). RH limb lesions of injured mice were three times larger compared with controls (p = 0.030). Therefore, despite extensive mineralization, changes to the responses we measured were limited or absent 20 weeks postinjury. These results also suggest that bilateral occurrence should be considered where tendon mineralization is identified clinically. This experimental system may be useful to study the mechanisms of bilateral new bone formation in tendinopathy and other conditions.

Technical Issues in Using Robots to Reproduce Joint Specific Gait

Reproduction of the in vivo motions of joints has become possible with improvements in robot technology and in vivo measuring techniques. A motion analysis system has been used to measure the motions of the tibia and femur of the ovine stifle joint during normal gait. These in vivo motions are then reproduced with a parallel robot. To ensure that the motion of the joint is accurately reproduced and that the resulting data are reliable, the testing frame, the data acquisition system, and the effects of limitations of the testing platform need to be considered. Of the latter, the stiffness of the robot and the ability of the control system to process sequential points on the path of motion in a timely fashion for repeatable path accuracy are of particular importance. Use of the system developed will lead to a better understanding of the mechanical environment of joints and ligaments in vivo.

An instrumented spatial linkage for measuring knee joint kinematics

BACKGROUND In this study, the design and development of a highly accurate instrumented spatial linkage (ISL) for kinematic analysis of the ovine stifle joint is described. The ovine knee is a promising biomechanical model of the human knee joint. METHODS The ISL consists of six digital rotational encoders providing six degrees of freedom (6-DOF) to its motion. The ISL makes use of the complete and parametrically continuous (CPC) kinematic modeling method to describe the kinematic relationship between encoder readings and the relative positions and orientation of its two ends. The CPC method is useful when calibrating the ISL, because a small change in parameters corresponds to a small change in calculated positions and orientations and thus a smaller optimization error, compared to other kinematic models. The ISL is attached rigidly to the femur and the tibia for motion capture, and the CPC kinematic model is then employed to transform the angle sensor readings to relative motion of the two ends of the linkage, and thereby, the stifle joint motion. RESULTS The positional accuracy for ISL after calibration and optimization was 0.3±0.2mm (mean +/- standard deviation). The ISL was also evaluated dynamically to ensure that accurate results were maintained, and achieved an accuracy of 0.1mm. CONCLUSIONS Compared to the traditional motion capture methods, this system provides increased accuracy, reduced processing time, and ease of use. Future work will be on the application of the ISL to the ovine gait and determination of in vivo joint motions and tissue loads. CLINICAL RELEVANCE Accurate measurement of knee joint kinematics is essential in understanding injury mechanisms and development of potential preventive or treatment strategies.

Inter-insertional distance is a poor correlate for ligament load: analysis from in vivo gait kinetics data.

In many analytic models of the knee joint, inter-insertional distance is used as the measure to define the load in a ligament. In addition, the direction of the load is taken to be the direction between the two insertions. Our in vivo data on the ovine ligament loads during gait, however, indicate that a wide range of forces is possible in the ligament for any specified inter-insertional distance. To understand the complex relationship between the bone orientations and ligament load better, an artificial neural network (ANN) model was developed. The six degree-of-freedom (6-DOF) in vivo kinematics of femur relative to tibia (joint kinematics) was used as input, and the magnitude of the anterior cruciate ligament (ACL) load was used as output/target. While the trained network was able to predict peak ligament loads with remarkable accuracy (R-square=0.98), an explicit relationship between joint kinematics and ACL load could not be determined. To examine the experimental and ANN observations further, a finite element (FE) model of the ACL was created. The geometry of the FE model was reconstructed from magnetic resonance images (MRI) of an ACL, and an isotropic, hyperelastic, nearly incompressible constitutive model was implemented for the ACL. The FE simulation results also indicate that a range of loads is possible in the ACL for a given inter-insertional distance, in concordance with the experimental/ANN observations. This study provides new insights for models of the knee joint; a simple force-length relationship for the ligament is not exact, nor is a single point to single point direction. More detailed microstructure-function data is required.