Stepping Movement Analysis

Stepping movement analysis is the systematic study of how organisms coordinate limb or body movements to produce locomotion. It examines movement timing, step sequence, trajectory, speed, and coordination, often by recording motion and comparing changes across repeated steps or experimental conditions. In biology, this analysis helps reveal how the nervous system, muscles, joints, and sensory feedback interact to control posture and movement. Researchers apply it to investigate locomotor development, behavior, motor disorders, and responses to environmental challenges, providing measurable outcomes for comparing species, assessing function, and evaluating changes in movement performance.

Stepping Movement Analysis - Related Videos

Research

JoVE Journal - Neuroscience

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)

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Cited by 9 •

2012

In this protocol we show how to condition harnessed honey bees to tactile stimuli and introduce a 2D motion capture technique for analyzing the kinematics of fine-scale antennal sampling pattern.

One-step Extraction and Zymographic Analysis of Bacterial Gelatinases

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2025

Proteinases are vital for bacterial pathogenesis, enabling invasion and immune evasion. Single-step extraction of cytoplasmic and membrane proteins of bacteria in their active form is harrowing. This video article demonstrates the extraction of whole cellular proteins of Leptospira for zymography analysis to detect and characterize gelatinases.

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

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Cited by 21 •

2017

This manuscript describes the method to test the concurrent validity of kinematic measures recorded by the smartphone application in comparison to a 3D motion capture system in the sagittal plane. This protocol will enable clinicians to set up smartphones for video capture of human movement.

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Cited by 2 •

2020

The purpose of this protocol is to utilize pre-built convolutional neural nets to automate behavior tracking and perform detailed behavior analysis. Behavior tracking can be applied to any video data or sequences of images and is generalizable to track any user-defined object.

Movement Retraining using Real-time Feedback of Performance

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Cited by 4 •

2013

Retraining abnormal movement patterns following injury or disease is a key component of physical rehabilitation. Recent advances in technology have permitted accurate assessment of movement during a variety of tasks, with near instantaneous quantification of results. This provides new opportunities for modification of faulty movement patterns in real time.

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