This should be a new biomechanical testing tool for analyzing joint properties after different treatments. Additionally, a robot can be applied for measurements of dissipated energy as a frictional characteristic in an entire joint [ 3 ]. Osteoarthritis is a common degenerative joint disease, which leads to a loss of the excellent frictional properties of synovial joints such as the knee. The abrasive processes over a lifetime or arthritic inflammatory diseases can lead to cartilage surface degeneration. The result is increased wear and energy dissipation during daily movements [ 4 ].
As a result of the rising number of patients suffering from osteoarthritis, there is a need for further development of effective therapeutic approaches. Clinical, histological and imaging tests indirectly described the mechanical improvement of joint cartilage after osteoarthritis treatment.
Therefore, tribological tests for mechanical characterization of the cartilage are also required. Currently, three related tribological methods are prevalent. The second method was the pendulum. Here, cadaver knees were used as the fulcrum of a pendulum and the amplitude decay was used to calculate the friction coefficient [ 6 , 7 ]. In the third method, a robot was used to measure friction coefficients in rabbit stifle joints by applying a linear movement [ 8 ]. A new method to measure dissipated energy as a friction characteristic in an entire joint was developed in our group [ 3 , 9 , 10 ].
The physiological joint flexion was determined by a passive path. To simplify the measurement method, we used a constant axial load and flexion velocity during the knee flexion [ 3 ], but under real conditions, the axial force and also the flexion velocity altered during stance and swing phases in a gait cycle. Taylor et al. The aim of this study was to develop and evaluate an algorithm for a robot [ 3 ] in order to reproduce the complex in vivo joint dynamics in ovine knee specimens using the data of Taylor et al.
In vitro simulation of in vivo dynamics will provide a new biomechanical testing tool for analyzing joint properties after different treatments. Experimental setup. Black arrows represent the tibia coordinate system where X, Y and Z denote medio-lateral, posterior-anterior and axial directions respectively.
An ovine knee specimen was flexed by the robot around the medio-lateral X axis. The femoral epicondyles of the knee joint were palpated and marked with a black felt pen. The initial origin of the robotic coordinate systems of the base tibia, fixed and tool femur, moving were placed in the midpoint between the lateral and medial epicondyle.
For the tibial and femoral local coordinate systems, the x-axes were defined from medial to lateral, the y-axes from posterior to anterior and the longitudinal z-axes from distal to proximal. Tibiofemoral movement was recorded in the tibia base coordinate system. Similar to the previous study [ 3 ], passive knee flexion was recorded with seven different increments 0. Passive knee flexion of another three specimens was recorded with the optimal increment.
The optimal increment was determined as a minimal increment with which the desired flexion velocity could be performed by the robot.
Passive knee flexion was characterized in a way that it follows the path of minimal resistance. It has been shown that this path of passive flexion was unique for each knee joint and therefore describes the individual unloaded motion of the joint [ 12 , 13 ]. Thus, for instance, after recording passive knee flexion with an increment of 0. In order to optimize the robot movement around the knee flexion axis, the origins of the tibial base and femoral tool coordinate systems were newly defined in the knee rotation center. Therefore, passive knee flexion was performed twice: the first time using a manually determined knee rotation center and the second time using the calculated knee rotation center.
The knee rotation center was calculated as a minimal amplitude point [ 16 ] during tibiofemoral movement of the first recorded passive knee flexion. Ehrig et al. Thus, the robotic flexion axis was corrected to the calculated knee rotation center which corresponded to that optimized axis and the passive knee flexion was recorded again. Data interpolation for the robot. In order to program the robot movement, knee flexion data were interpolated for the robot diagram A , points and squares using the flexion data from Taylor et al. If two adjacent, interpolated points at local minima or maxima had the same flexion angle, the midpoint square in diagram A marked with a black arrow was taken instead of both points for more information, see the text.
Interpolation of axial force data diagram B , points and squares was performed using the axial force data from Taylor et al. The axial contact force values in body weight BW for each of the seven increments were interpolated from the knee contact force values published by Taylor et al. The flexion angle, angular velocity and axial force data sets were vectors of the same length.
These three vectors of data comprised dynamics information for the simulation of knee flexion over one gait cycle.
The loaded knee flexion path of the robot movement consisted of points spatial positions of the robot from the previously recorded passive flexion path. In order to reproduce the knee flexion during the gait cycle, we substituted the values of the interpolated flexion angle data for indexes of the corresponding points of the passive flexion path. The robot performed the loaded knee flexion path in a superimposed force torque control mode. Only the axial force was actively simulated controlled by the robot.
Medio-lateral and posterior-anterior forces resulted as a backlash of knee joint structures. The in vivo loads were simulated for gait cycles on each of the four knee specimens. The 60 initial gait cycles were considered as pre-conditional cycles, while the last gait cycles were used for further data analysis. This allowed work with the robot without a protective fence. F ij was the simulated force vector and F invivo was the in vivo force vector corresponding to Taylor et al. Both force vectors consisted of elements in body weight units. Like x 1.
Ottertron , Jun 4, Based on how long it takes the companies who use IDW designs to actually get them out there, we should be good for another few years at least before they start drying up. ProtectronPrime , Jun 4, Like x 3. A lot of companies are still making designs from earlier Generations. I'd like to see someone tackle Nick Roche's Springer design even though Hasbro's figure is near-perfect! Like x 2. Bumblemus Prime , Jun 4, Acteon , Jun 4, The New Guy , Jun 4, I'd guess that how much the comics ending hurts the market is going to depend on what the robots in the new IDW series look like.
If the new series has really impressive new designs then I expect we'll start to see toys based off them, which will likely have at least a small slowdown effect on figures inspired by the current series. To compare available vs. Analyses were performed in R [ 20 ].
Take-off velocities were lower with more compliant perches. Resultant velocities produced during take-off from aluminum and wood perches were lower, and not statistically different from each other. Birds produced a velocity of 0.
The duration of time over which forces were applied did not change with substrate compliance, indicating that birds did not adjust timing of force application to improve take-off velocity in response to perch compliance. In all instances, the bird lost contact with the perch before it recoiled S1 Video. Likewise, the first downstroke was initiated immediately after losing contact with the perch, regardless of substrate.
Birds may have compensated in flight for velocity lost on compliant substrates during take-off, but this trend was not significant in flight velocities. By the third wingbeat in flight, velocity not produced by the legs due to perch compliance remained statistically significant: doves taking off from a wood perch had an in-flight velocity of 1. Birds taking off from wood and aluminum perches recovered an average of 0.