> ## Documentation Index
> Fetch the complete documentation index at: https://docs.labellerr.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Body Pose Keypoint Tracking

> Learn how Labellerr's Body Pose Keypoint Tracking supports up to 33 points to create detailed skeletal annotations for full-body pose estimation.

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# Body Pose Keypoint Tracking

Human pose estimation is a fundamental building block for modern computer vision systems. Labellerr introduces **Body Pose Keypoint Tracking** with support for up to **33 body keypoints**, helping teams create richer skeletal annotations for full-body understanding tasks.

This enables more structured human pose labeling and better representation of body movement, alignment, posture, and positional relationships.

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## Key Features

* **33-Point Pose Annotation:** Maps all critical body joints, including shoulders, elbows, wrists, hips, knees, ankles, and facial landmarks (eyes, nose, ears).
* **Connected Skeleton Structure:** Visualizes spatial and anatomical relationships directly on the labeling canvas.
* **Pose Presets:** Quickly start labeling with pre-configured skeletal structures tailored for pose estimation workflows.
* **Enhanced Precision:** Designed to handle complex human body movements, varied camera angles, and distance variations.

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## Step-by-Step Guide: Using Body Pose Keypoint Tracking

### 1. Set Up the Pose Template

* Go to the **Label Configuration** settings of your project.
* Click **Add Object**, name the label (e.g., `person` or `pose`), and select **Keypoint** as the tool type.
* Choose the **Body Pose Preset (33 Points)** to instantly load the standardized skeleton layout.
* Click **Save** to confirm.

### 2. Placing the Pose Skeleton

* Select the `pose` label from the annotation sidebar.
* Click on the person in the image/frame to overlay the skeleton.
* Drag and place individual keypoint nodes (such as the elbows, knees, or shoulders) onto their corresponding anatomical landmarks.

### 3. Verification & Fine-Tuning

* Toggle connecting lines to verify that limbs and joints are connected correctly.
* Use **Attribute Filtering** to isolate and audit pose annotations across large datasets, ensuring metadata consistency.

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## Use Cases

* **Pose Estimation Training:** Building datasets to train deep learning models on human joint detection.
* **Fitness & Movement Analysis:** Analyzing body alignment, squats, or yoga poses in fitness coaching apps.
* **Sports Performance Tracking:** Tracking athlete movements, posture, and techniques for analytics.
* **Safety Monitoring:** Detecting falls or unsafe postures in industrial and healthcare environments.
* **Human Behavior Analysis:** Enhancing security and retail analytics through posture and movement interpretation.