Gait Kinematics Decoding
Gait Kinematics Decoding - Related Videos
13:02
Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis
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Cited by 7 •
2017
Kinematic gait analysis in the sagittal plane yields highly precise information about how movement is executed. We describe the application of these techniques to identify gait deficits for mice subjected to autoimmune-mediated demyelination. These methods may also be used to characterize gait deficits for other mouse models featuring impaired locomotion.
10:19
3D Kinematic Gait Analysis for Preclinical Studies in Rodents
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Cited by 5 •
2019
Presented here is a protocol to collect and analyze three-dimensional kinematics of quadrupedal locomotion in rodents for preclinical studies.
11:41
Kinematics and Projectile Motion
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2023
Source: Ketron Mitchell-Wynne, PhD, Asantha Cooray, PhD, Department of Physics & Astronomy, School of Physical Sciences, University of California, Irvine, CA This experiment demonstrates the kinematics of motion in 1 and 2 dimensions. This lab will begin by studying motion in 1 dimension, under constant acceleration, by launching a projectile directly upward and measuring the maximum height reached. This lab will verify that the maximum height reached is consistent with the kinematic...
08:00
Decoding Natural Behavior from Neuroethological Embedding
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2025
This protocol gives an integrated framework based on advanced computational neuroethological methods to understand brain coding in naturalistic contexts.
11:45
Three-Dimensional Kinematic Characterization of an Object Pick-Up Task Using Motion Capture
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2026
This protocol presents a standardized full-body motion capture approach for assessing the object pick-up task, illustrating how distinct movement strategies and compensatory patterns can be quantitatively characterized during this common functional activity.
FAQs
The key analytical step is to transform movement traces into spatial and temporal measurements that can be compared across gait cycles. Body-part trajectories describe where movement occurs, while joint angles capture changes in limb configuration. Step timing and stride length add measures of pacing and distance. Together, these features quantify coordination, speed, and stability rather than relying only on visual descriptions.
Foot contact and lift-off provide temporal landmarks for dividing locomotion into successive gait cycles. This segmentation allows researchers to align movements and calculate features such as step timing and stride length consistently. Without common event markers, differences in the timing or position of body parts would be harder to interpret across cycles, animals, experimental conditions, or stages of motor performance.
Different feature types reflect different aspects of locomotor behavior. Joint angles and body-part trajectories reveal coordination, while step timing and stride length characterize how movement is organized in time and space. Examining these measures together helps separate changes in pacing from changes in limb configuration or overall movement consistency, supporting more specific interpretations of altered motor performance.
A typical workflow begins by measuring body-part trajectories, joint angles, step timing, and stride length during locomotion. The movement record is then organized across successive gait cycles, with foot contact and lift-off identified as key events. Researchers convert these observations into quantitative features and compare the resulting patterns between experimental conditions or behavioral groups.
This approach is useful when researchers need objective measures of locomotor differences rather than broad visual judgments. Quantified features can support comparisons between experimental conditions, reveal changes in speed or coordination, and assess motor performance. In behavioral studies, those comparisons help establish whether an intervention, environment, or other experimental factor is associated with a distinct movement pattern.
Movement features provide an intermediate level of analysis between visible locomotion and possible underlying causes. Changes in coordination, timing, stride length, or stability can be examined as behavioral signatures associated with neural, muscular, or environmental influences. The measurements do not replace investigation of those influences, but they provide a structured way to relate observable motor patterns to them.