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Leg mechanics contribute to establishing swing phase trajectories during memory-guided stepping movements in walking cats: a computational analysis.

Pearson KG, Arbabzada N, Gramlich R, Shinya M - Front Comput Neurosci (2015)

Bottom Line: However, an additional contribution of neuronal motor commands was indicated by the fact that the simulated slopes of paw trajectories were significantly less than the observed slopes.Previous studies have shown that a shift in paw position prior to stepping over a barrier changes the paw trajectory to be appropriate for the new paw position.Our data indicate that both mechanical and neuronal factors contribute to this updating process, and that any shift in leg position during the delay period modifies the working memory of barrier location.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, University of Alberta Edmonton, AB, Canada.

ABSTRACT
When quadrupeds stop walking after stepping over a barrier with their forelegs, the memory of barrier height and location is retained for many minutes. This memory is subsequently used to guide hind leg movements over the barrier when walking is resumed. The upslope of the initial trajectory of hind leg paw movements is strongly dependent on the initial location of the paw relative to the barrier. In this study, we have attempted to determine whether mechanical factors contribute significantly in establishing the slope of the paw trajectories by creating a four-link biomechanical model of a cat hind leg and driving this model with a variety of joint-torque profiles, including average torques for a range of initial paw positions relative to the barrier. Torque profiles for individual steps were determined by an inverse dynamic analysis of leg movements in three normal cats. Our study demonstrates that limb mechanics can contribute to establishing the dependency of trajectory slope on the initial position of the paw relative to the barrier. However, an additional contribution of neuronal motor commands was indicated by the fact that the simulated slopes of paw trajectories were significantly less than the observed slopes. A neuronal contribution to the modification of paw trajectories was also revealed by our observations that both the magnitudes of knee flexor muscle EMG bursts and the initial knee flexion torques depended on initial paw position. Previous studies have shown that a shift in paw position prior to stepping over a barrier changes the paw trajectory to be appropriate for the new paw position. Our data indicate that both mechanical and neuronal factors contribute to this updating process, and that any shift in leg position during the delay period modifies the working memory of barrier location.

No MeSH data available.


Four-link modeling of cat hind leg. (A) The cat hind leg was modeled by four uniform rods for the thigh, shank, paw, and toes connected at the hip joint (h), knee joint (k), ankle joint (a), and paw joint (p). The model was used to calculate torques at these four joints when the animal stepped over a remembered barrier (dotted rectangle), and to simulate leg movements when driven by different torque profiles. The barrier (filled rectangle) was lowered after the forelegs have stepped over it. The slope of the toe trajectory (dotted line) was calculated over distance of 2 cm soon after the swing phase commenced. (B) Parameters used to determine the equations of motion for a single segment (see text for details). This segment represents the first segment in a kinematic chain starting from the origin (O), which is the hip joint in the model. Note that for each segment the segment angle (θ) is measured in the anti-clockwise direction from the positive X-axis proximal to the segment. By convention, positive torques are in the anti-clockwise direction. The reaction torque and forces at the distal joint are in the opposite directions to those at the proximal joint.
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Figure 1: Four-link modeling of cat hind leg. (A) The cat hind leg was modeled by four uniform rods for the thigh, shank, paw, and toes connected at the hip joint (h), knee joint (k), ankle joint (a), and paw joint (p). The model was used to calculate torques at these four joints when the animal stepped over a remembered barrier (dotted rectangle), and to simulate leg movements when driven by different torque profiles. The barrier (filled rectangle) was lowered after the forelegs have stepped over it. The slope of the toe trajectory (dotted line) was calculated over distance of 2 cm soon after the swing phase commenced. (B) Parameters used to determine the equations of motion for a single segment (see text for details). This segment represents the first segment in a kinematic chain starting from the origin (O), which is the hip joint in the model. Note that for each segment the segment angle (θ) is measured in the anti-clockwise direction from the positive X-axis proximal to the segment. By convention, positive torques are in the anti-clockwise direction. The reaction torque and forces at the distal joint are in the opposite directions to those at the proximal joint.

Mentions: The four main segments of the cat hind leg (thigh, shank, paw, and toes) were modeled as uniform rigid rods (Figure 1A). This is an enormous simplification of the complex mechanics of the leg but it allows a straightforward determination of the equations of motion and provides a simple approach for estimating torques at the hip, knee, ankle, and paw joints. A similar simplification has been used to calculate joint torques in many other biomechanical studies of hind leg movements in the cat (Hoy and Zernicke, 1985; Hoy et al., 1985; Ekeberg and Pearson, 2005). From measurements on a number of animals, the values we used for the mass of each segment were: thigh—200 gms, shank—100 gms, paw—40 gms, toe—20 gms. The lengths of the thigh and shanks were set at 10 and 11 cm, respectively, and the lengths of the paw and toes were measured from video recordings, approximately 5 and 2 cm, respectively. To get an estimate of joint torques from an inverse dynamic analysis we first derived five equations of motion for each segment (see below) thus resulting in twenty equations of motion with 20 unknowns that included the torques at the hip, knee, ankle, and paw joints. These equations were solved using custom written software in the Matlab programming language.


Leg mechanics contribute to establishing swing phase trajectories during memory-guided stepping movements in walking cats: a computational analysis.

Pearson KG, Arbabzada N, Gramlich R, Shinya M - Front Comput Neurosci (2015)

Four-link modeling of cat hind leg. (A) The cat hind leg was modeled by four uniform rods for the thigh, shank, paw, and toes connected at the hip joint (h), knee joint (k), ankle joint (a), and paw joint (p). The model was used to calculate torques at these four joints when the animal stepped over a remembered barrier (dotted rectangle), and to simulate leg movements when driven by different torque profiles. The barrier (filled rectangle) was lowered after the forelegs have stepped over it. The slope of the toe trajectory (dotted line) was calculated over distance of 2 cm soon after the swing phase commenced. (B) Parameters used to determine the equations of motion for a single segment (see text for details). This segment represents the first segment in a kinematic chain starting from the origin (O), which is the hip joint in the model. Note that for each segment the segment angle (θ) is measured in the anti-clockwise direction from the positive X-axis proximal to the segment. By convention, positive torques are in the anti-clockwise direction. The reaction torque and forces at the distal joint are in the opposite directions to those at the proximal joint.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585078&req=5

Figure 1: Four-link modeling of cat hind leg. (A) The cat hind leg was modeled by four uniform rods for the thigh, shank, paw, and toes connected at the hip joint (h), knee joint (k), ankle joint (a), and paw joint (p). The model was used to calculate torques at these four joints when the animal stepped over a remembered barrier (dotted rectangle), and to simulate leg movements when driven by different torque profiles. The barrier (filled rectangle) was lowered after the forelegs have stepped over it. The slope of the toe trajectory (dotted line) was calculated over distance of 2 cm soon after the swing phase commenced. (B) Parameters used to determine the equations of motion for a single segment (see text for details). This segment represents the first segment in a kinematic chain starting from the origin (O), which is the hip joint in the model. Note that for each segment the segment angle (θ) is measured in the anti-clockwise direction from the positive X-axis proximal to the segment. By convention, positive torques are in the anti-clockwise direction. The reaction torque and forces at the distal joint are in the opposite directions to those at the proximal joint.
Mentions: The four main segments of the cat hind leg (thigh, shank, paw, and toes) were modeled as uniform rigid rods (Figure 1A). This is an enormous simplification of the complex mechanics of the leg but it allows a straightforward determination of the equations of motion and provides a simple approach for estimating torques at the hip, knee, ankle, and paw joints. A similar simplification has been used to calculate joint torques in many other biomechanical studies of hind leg movements in the cat (Hoy and Zernicke, 1985; Hoy et al., 1985; Ekeberg and Pearson, 2005). From measurements on a number of animals, the values we used for the mass of each segment were: thigh—200 gms, shank—100 gms, paw—40 gms, toe—20 gms. The lengths of the thigh and shanks were set at 10 and 11 cm, respectively, and the lengths of the paw and toes were measured from video recordings, approximately 5 and 2 cm, respectively. To get an estimate of joint torques from an inverse dynamic analysis we first derived five equations of motion for each segment (see below) thus resulting in twenty equations of motion with 20 unknowns that included the torques at the hip, knee, ankle, and paw joints. These equations were solved using custom written software in the Matlab programming language.

Bottom Line: However, an additional contribution of neuronal motor commands was indicated by the fact that the simulated slopes of paw trajectories were significantly less than the observed slopes.Previous studies have shown that a shift in paw position prior to stepping over a barrier changes the paw trajectory to be appropriate for the new paw position.Our data indicate that both mechanical and neuronal factors contribute to this updating process, and that any shift in leg position during the delay period modifies the working memory of barrier location.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, University of Alberta Edmonton, AB, Canada.

ABSTRACT
When quadrupeds stop walking after stepping over a barrier with their forelegs, the memory of barrier height and location is retained for many minutes. This memory is subsequently used to guide hind leg movements over the barrier when walking is resumed. The upslope of the initial trajectory of hind leg paw movements is strongly dependent on the initial location of the paw relative to the barrier. In this study, we have attempted to determine whether mechanical factors contribute significantly in establishing the slope of the paw trajectories by creating a four-link biomechanical model of a cat hind leg and driving this model with a variety of joint-torque profiles, including average torques for a range of initial paw positions relative to the barrier. Torque profiles for individual steps were determined by an inverse dynamic analysis of leg movements in three normal cats. Our study demonstrates that limb mechanics can contribute to establishing the dependency of trajectory slope on the initial position of the paw relative to the barrier. However, an additional contribution of neuronal motor commands was indicated by the fact that the simulated slopes of paw trajectories were significantly less than the observed slopes. A neuronal contribution to the modification of paw trajectories was also revealed by our observations that both the magnitudes of knee flexor muscle EMG bursts and the initial knee flexion torques depended on initial paw position. Previous studies have shown that a shift in paw position prior to stepping over a barrier changes the paw trajectory to be appropriate for the new paw position. Our data indicate that both mechanical and neuronal factors contribute to this updating process, and that any shift in leg position during the delay period modifies the working memory of barrier location.

No MeSH data available.