---
annotations_creators:
- human-annotated
language:
- en
license: cc-by-4.0
pretty_name: "Human Activity Pose Dataset (Split Version)"
task_categories:
- keypoint-detection
- image-classification
task_ids:
- pose-estimation
- multi-class-classification
size_categories:
- 1K<n<10K
---

# 🧍 Human Activity Pose Dataset (Split Version)

This dataset contains human pose landmarks extracted with MediaPipe Pose,
annotated with activity labels and textual descriptions in English.

## Dataset structure
- `train/` — 80% of samples for training
- `validation/` — 20% of samples for validation

Each record includes:
- 33 pose keypoints (fields: `x`, `y`, `z`, `visibility`)
- `label`: activity name (e.g., `reading`, `dancing`, `office_work`)
- `description`: a short textual description of the action context

## Recommended usage
```python
from datasets import load_dataset
dataset = load_dataset("guillherms/human-activity-pose_v4")
train = dataset["train"]
val = dataset["validation"]
```

## Example labels

| label | description |
|-------|-------------|
| reading | A person reading or reviewing a document attentively. |
| waving  | A person waving their hand as a greeting or farewell gesture. |
| office_work | People working in an office, using laptops, papers, or mobile devices. |

## Source
Created by **Guilherme Santos** using MediaPipe and OpenCV.

## Notes
- The YAML front-matter at the top of this file is used by Hugging Face Cards to populate the repo card. It must start on the first line (`---`) with no preceding blank lines or spaces.
- License: CC-BY-4.0