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J.J.A.-G is a doctoral student from the Programa de Doctorado en Ciencias Biom&#233;dicas, Universidad Nacional Aut&#243;noma de M&#233;xico (UNAM), and has received SECIHTI fellowship (No. 1145816). Additionally, the authors wish to thank A. R. Aguilar V&#225;zquez, E.A. De los R&#237;os Arellano, and D. Gasca Mart&#237;nez for their technical support.
FUNDING: This research was funded by UNAM-PAPIIT (209325, IN227026) and Ciencia b&#225;sica y de Frontera grant number CBF-2025-G-35.

<ol>
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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

This study presents a low-cost, markerless approach for analyzing spontaneous locomotion and gait in freely moving mice using standard video acquisition and DeepLabCut-based pose estimation within an accessible workflow. The workflow allows the extraction of locomotor and gait-related variables from video recordings while preserving natural locomotor behavior and reducing experimental interference. In this sense, the protocol offers a practical and accessible alternative for preclinical studies of motor behavior.
<p ...

Gait is a complex motor process that emerges from the real-time interaction of motor, sensory, and cognitive systems, requiring coordinated processing of sensory feedback, postural control, motor planning, and muscle activation to generate stable, adaptive movement1. Because of this integrative nature, gait alterations are increasingly recognized as functional indicators of neurological dysfunction in both clinical practice and experimental research. In humans and animal models, gait analysis enables detection of changes in mobility, fall risk, frailty, and functional capacity, and has been proposed as a biomarker for disease progression and intervention efficacy across multiple conditions2,3,4.
In basic research, particularly in murine models, assessing spontaneous gait and locomotor activity represents a widely used strategy to characterize motor phenotypes and to evaluate the impact of physiological perturbations and therapeutic interventions. Objective quantification of locomotor parameters allows the detection of subtle motor alterations that are not reliably captured by visual inspection alone, a limitation that is especially relevant in preclinical studies targeting early or mild phenotypes5. In this context, gait analysis has emerged as a valuable tool for investigating alterations in motor behavior and identifying functional changes associated with different experimental conditions, such as neurological and neurodegenerative diseases6.
The analysis of motor behavior and gait in murine models has traditionally relied on specialized technological platforms designed to quantify spatial and temporal kinematic variables. Systems such as illuminated walkways, instrumented platforms, and commercial motion-tracking software are commonly used; however, their reliance on costly hardware and dedicated infrastructure significantly limits accessibility for many laboratories7,8,9,10. Beyond economic considerations, these approaches introduce additional experimental constraints. Several systems require animals to traverse narrow corridors or highly illuminated lanes, imposing forced locomotor patterns that reduce spontaneous behavior, while others depend on physical markers, paint spots, or adhesive tags attached to the limbs. Such interventions can induce stress, alter gait mechanics, and confound the physiological interpretation of motor outcomes11,12,13,14. Collectively, these limitations highlight the need for methodologies capable of capturing gait under natural, freely moving conditions without reliance on invasive procedures or expensive infrastructure.
Evaluating gait during unconstrained locomotion enables the observation of more natural motor patterns, avoiding external constraints that artificially regulate speed, trajectory, or posture. This approach facilitates the characterization of locomotor behavior under minimally constrained conditions. DeepLabCut is an open-source, deep-learning-based pose estimation framework that uses convolutional neural networks to track user-defined anatomical landmarks without the need for physical markers. By enabling anatomical point tracking from standard video recordings, DeepLabCut allows the extraction of kinematic parameters while substantially reducing cost and experimental complexity15,16,17,18. Importantly, markerless tracking minimizes experimental interference, making it suitable for studies of spontaneous behavior. This single-camera markerless approach is most suitable for studies requiring accessible, non-invasive quantification of spontaneous locomotor activity and two-dimensional gait-related features under freely moving conditions. However, instrumented gait platforms or three-dimensional motion capture systems are required when direct biomechanical variables, such as ground reaction forces, plantar pressure, joint angles, or full three-dimensional limb kinematics, are needed.
The objective of this article is to present a low-cost, markerless workflow based on DeepLabCut for the non-invasive quantification of gait and spontaneous locomotion in freely moving mice, integrating single-camera video acquisition, anatomical landmark tracking, and extraction of spatiotemporal locomotor parameters within an accessible workflow that does not require specialized hardware or invasive manipulation. This protocol focuses on the methodological implementation and representative outputs of the workflow.

<table><tbody><tr><td>70% ethanol</td><td>Generic</td><td>N/A</td><td>Arena cleaning between sessions</td></tr><tr><td>Adjustable white-light lamps</td><td>Generic</td><td>N/A</td><td>Maintain constant illumination (100&amp;ndash;150 lx)</td></tr><tr><td>Black curtains/glare control</td><td>Generic</td><td>N/A</td><td>Reduce external light/reflections</td></tr><tr><td>Checkerboard grid template</td><td>Generic</td><td>N/A</td><td>Spatial calibration reference image</td></tr><tr><td>DeepLabCut (v3.0.0rc13)</td><td>Open-source</td><td>N/A</td><td>Pose estimation software</td></tr><tr><td>Digital video camera (HD, 16:9; &amp;ge;60 fps)</td><td>Generic</td><td>N/A</td><td>Fixed settings across sessions</td></tr><tr><td>Nvidia GPU workstation</td><td>Custom build</td><td>N/A</td><td>Hardware for model training/inference</td></tr><tr><td>Lux meter</td><td>Generic</td><td>N/A</td><td>Verify illumination at the arena level</td></tr><tr><td>PyTorch</td><td>Pytorch Foundation</td><td>N/A</td><td>Framework required by DeepLabCut</td></tr><tr><td>Transparent acrylic open-field arena (42 &amp;times; 42 &amp;times; 42 cm)</td><td>Custom-made / In-house</td><td>N/A</td><td>Open-field arena</td></tr><tr><td>Under-arena camera mount (90&amp;deg; view)</td><td>Custom-made / In-house</td><td>N/A</td><td>Stable mount for bottom recording</td></tr><tr><td>CUDA</td><td>Nvidia corporation</td><td>N/A</td><td>GPU-accelerated parrellel proccesing</td></tr><tr><td>R programming language</td><td>R Foundation</td><td>N/A</td><td>Programming language for data analysis</td></tr></tbody></table>

a low-cost markerless deeplabcut-based workflow for spontaneous gait and locomotion analysis in freely moving mice

A Low-Cost Markerless DeepLabCut-Based Workflow for Spontaneous Gait and Locomotion Analysis in Freely Moving Mice

Gait is a widely used functional biomarker for detecting motor alterations across various diseases and conditions, as it reflects changes in coordination, strength, balance, and sensorimotor integration. However, traditional methods to analyze gait in animal models often require expensive equipment, complex setups, or invasive procedures that can alter natural behavior. Here, we present a low-cost, markerless workflow based on DeepLabCut, an open-source pose estimation software, for the quantitative analysis of gait and spontaneous locomotion in freely moving mice. The method relies on single-camera video acquisition, markerless tracking of anatomical landmarks, and extraction of spatiotemporal locomotor parameters, without the need for physical markers or specialized hardware. To demonstrate the protocol&#39;s applicability, it was implemented in the triple transgenic mouse model of Alzheimer&#8217;s disease (3xTg-AD) as a representative example of application. This approach preserves free movement and minimizes handling-related stress, enabling non-invasive assessment of motor behavior. The protocol is compatible with standard behavioral testing environments. Overall, this method provides an accessible and non-invasive framework for quantitative analysis of gait and locomotion in preclinical research.

Following application of the tracking and analysis workflow, automated processing reconstructed spontaneous open field locomotion using the defined body center landmark and generated quantitative outputs suitable for downstream analysis. Representative trajectory plots illustrate how the protocol captures spatial displacement across the arena, temporal occupancy, and speed dynamics during free exploration. The open field variables shown in this section were calculated from the full recording, and representative recording...

We present a low-cost, markerless workflow using DeepLabCut to quantify spontaneous gait and locomotion in freely moving mice. This protocol integrates single-camera video acquisition, markerless tracking of anatomical landmarks, and extraction of spatiotemporal locomotor parameters, providing a non-invasive and accessible framework for motor behavior analysis without specialized equipment.

Gait is a widely used functional biomarker for detecting motor alterations across various diseases and conditions, as it reflects changes in coordination, ...

a-low-cost-markerless-deeplabcut-based-workflow-for-spontaneous-gait

Research

JoVE Journal

Neuroscience

In this video, we describe the footprint testing procedure and related analysis to assess gait and motor coordination in mice.

Assessing Gait by Footprint Analysis: A Method to Test Motor Coordination in Mice

FIM is a novel, cost effective imaging system designed to track small moving objects such as C. elegans, planaria or Drosophila larvae. The accompanying FIMTrack program is designed to deliver fast and efficient data analysis. Together, these tools allow high-throughput analysis of behavioral traits.

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

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.

Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis

The precise assessment of pain response in a neuropathic animal model is critical to investigate the pathophysiology of pain diseases and develop new analgesics. We present a sensitive and objective method to determine the sensory function of the rodent hind paw by an automated gait analysis system.

Automated Gait Analysis in Mice with Chronic Constriction Injury

This protocol describes a flexible, low-cost system for measuring mouse ambulation in an open field activity assay. We show that a 6-minute ambulation assay based on this system detects a decrease in voluntary movement in mdx mice, and accurately distinguishes improvement in a muscle-specific rescue of these animals.

A Simple and Low-cost Assay for Measuring Ambulation in Mouse Models of Muscular Dystrophy

Presented here is a protocol to collect and analyze three-dimensional kinematics of quadrupedal locomotion in rodents for preclinical studies.

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

Footprint analysis is a low-cost alternative to digitized gait analysis programs for researchers quantifying movement abnormalities in mice. Because of its speed, simplicity, and longitudinal potential, it is ideal for behavioral phenotyping of mouse models.

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease

The present protocol describes three-dimensional motion tracking/evaluation to depict gait motion alteration of rats after exposure to a simulated disuse environment.

Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

An experimental pipeline to quantitatively describe the locomotor pattern of freely walking mice using the MouseWalker (MW) toolbox is provided, ranging from initial video recordings and tracking to post-quantification analysis. A spinal cord contusion injury model in mice is employed to demonstrate the usefulness of the MW system.

Using the MouseWalker to Quantify Locomotor Dysfunction in a Mouse Model of Spinal Cord Injury

This protocol establishes a rat hindlimb kinematic evaluation pipeline using a markerless treadmill test with deep learning-driven multi-joint trajectory auto-labeling, which enables reproducible motion quantification.

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Universidad Nacional Autónoma de México.  We present a low-cost, markerless workflow using DeepLabCut to quantify spontaneous gait and locomotion in freely moving mice. This protocol integrates single-camera video acquisition, markerless tracking of anatomical landmarks, and extraction of spatiotemporal locomotor parameters, providing a non-invasive and accessible framework for motor behavior analysis without specialized equipment.

A Low-Cost Markerless DeepLabCut-Based Workflow for Spontaneous Gait and Locomotion Analysis in Freely Moving Mice

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