---
license: cc-by-nc-4.0
task_categories:
- video-classification
language:
- en
tags:
- synthetic
- activity-recognition
- fall-detection
pretty_name: 'WanFall: A Synthetic Activity Recognition Dataset'
size_categories:
- 10K<n<100K

configs:
  - config_name: labels
    data_files:
      - labels/wanfall.csv
    default: true
    description: "Temporal segment labels for all videos. Load splits to get train/val/test paths."

  - config_name: default
    data_files:
      - split: train
        path: "splits/train.csv"
      - split: validation
        path: "splits/val.csv"
      - split: test
        path: "splits/test.csv"
    description: "Default random 80/10/10 train/val/test split (seed 42)"
---
[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)

# WanFall: A Synthetic Activity Recognition Dataset

This repository contains temporal segment annotations for WanFall, a synthetic activity recognition dataset focused on fall detection and related activities of daily living.

## Overview

WanFall is a large-scale synthetic dataset designed for activity recognition research, with emphasis on fall detection and posture transitions. The dataset features computer-generated videos of human actors performing various activities in controlled virtual environments.

**Key Features:**
- **~12,000 video clips** with dense temporal annotations
- **16 activity classes** including falls, posture transitions, and static states
- **5.0625 seconds** per video clip (81 frames @ 16 fps)
- **Synthetic generation** enabling diverse scenarios and controlled variation
- **Dense temporal segmentation** with frame-level precision

## Dataset Statistics

- **Total videos**: 12,000
- **Total temporal segments**: 19,062
- **Annotation format**: Temporal segmentation (start/end timestamps)
- **Video duration**: 5.0625 seconds per clip
- **Frame count**: 81 frames per video
- **Frame rate**: 16 fps
- **Default split**: 80/10/10 train/val/test (seed 42)
  - Train: 9,600 videos
  - Validation: 1,200 videos
  - Test: 1,200 videos

## Activity Categories

The dataset includes **16 activity classes** organized into dynamic actions and static states:

### Dynamic Actions (Transitions)
- **0. walk** - Walking movement, including jogging and running
- **1. fall** - Falling down action (from any previous state)
- **3. sit_down** - Transitioning from standing to sitting
- **5. lie_down** - Intentionally lying down (not falling)
- **7. stand_up** - Rising from sitting/lying to standing
- **10. kneel_down** - Transitioning to kneeling position
- **12. squat_down** - Transitioning to squatting position
- **14. crawl** - Crawling movement on hands and knees
- **15. jump** - Jumping action

### Static States
- **2. fallen** - Person in fallen state (on ground after fall)
- **4. sitting** - Stationary sitting posture
- **6. lying** - Stationary lying posture (after intentional lie_down)
- **8. standing** - Stationary standing posture
- **11. kneeling** - Stationary kneeling posture
- **13. squatting** - Stationary squatting posture

### Other
- **9. other** - Actions not fitting above categories

## Structure

The repository is organized as follows:

- `labels/` - CSV files containing temporal segment annotations
  - `wanfall.csv` - All temporal segments for the dataset
  - `label2id.csv` - Mapping of activity names to integer IDs
- `splits/` - Train/validation/test split definitions
  - `train.csv` - Training set video paths (80%)
  - `val.csv` - Validation set video paths (10%)
  - `test.csv` - Test set video paths (10%)

### Label Format

The `labels/wanfall.csv` file follows this format:

```
path,label,start,end,subject,cam,dataset
```

Where:
- `path`: Relative path to the video (without .mp4 extension, e.g., "fall/fall_ch_001")
- `label`: Class ID (0-15) corresponding to one of the 16 activity classes
- `start`: Start time of the segment in seconds
- `end`: End time of the segment in seconds
- `subject`: Subject ID (`-1` for synthetic data without subject tracking)
- `cam`: Camera view ID (`-1` for single view/no camera variation)
- `dataset`: Dataset name (`wanfall`)

### Split Format

Split files in the `splits/` directory list the video paths included in each partition:

```
path
fall/fall_ch_001
fall/fall_ch_002
...
```

## Usage Examples

### Load Default Split

```python
from datasets import load_dataset
import pandas as pd

# Load the datasets
print("Loading WanFall dataset...")

# Labels are always in 'train' split by default in HuggingFace
labels = load_dataset("YOUR_USERNAME/wanfall", "labels")["train"]

# Load default train/val/test splits
default_split = load_dataset("YOUR_USERNAME/wanfall", "default")

# Convert to pandas DataFrames
labels_df = pd.DataFrame(labels)
print(f"Labels dataframe shape: {labels_df.shape}")
print(f"Total temporal segments: {len(labels_df)}")

# Process each split
for split_name, split_data in default_split.items():
    # Convert to DataFrame
    split_df = pd.DataFrame(split_data)

    # Join with labels on 'path'
    merged_df = pd.merge(split_df, labels_df, on="path", how="left")

    # Print statistics
    print(f"\n{split_name} split:")
    print(f"  Videos: {len(split_df)}")
    print(f"  Temporal segments: {len(merged_df)}")
    print(f"  Unique labels: {merged_df['label'].nunique()}")
```

### Analyze Label Distribution

```python
from datasets import load_dataset
import pandas as pd

# Load labels
labels = load_dataset("YOUR_USERNAME/wanfall", "labels")["train"]
labels_df = pd.DataFrame(labels)

# Load label names
label_map = {
    0: 'walk', 1: 'fall', 2: 'fallen', 3: 'sit_down',
    4: 'sitting', 5: 'lie_down', 6: 'lying', 7: 'stand_up',
    8: 'standing', 9: 'other', 10: 'kneel_down', 11: 'kneeling',
    12: 'squat_down', 13: 'squatting', 14: 'crawl', 15: 'jump'
}

# Add label names
labels_df['label_name'] = labels_df['label'].map(label_map)

# Segment-level distribution
print("Temporal Segment Distribution:")
segment_counts = labels_df['label_name'].value_counts().sort_index()
for label_name, count in segment_counts.items():
    print(f"  {label_name:15s}: {count:5d} segments")

# Video-level distribution (primary activity from path)
labels_df['primary_activity'] = labels_df['path'].str.split('/').str[0]
print("\nVideo Distribution by Primary Activity:")
video_counts = labels_df['primary_activity'].value_counts()
for activity, count in video_counts.items():
    print(f"  {activity:15s}: {count:5d} segments")
```

### Iterate Over Split

```python
from datasets import load_dataset
import pandas as pd

# Load data
labels = load_dataset("YOUR_USERNAME/wanfall", "labels")["train"]
labels_df = pd.DataFrame(labels)

splits = load_dataset("YOUR_USERNAME/wanfall", "default")
train_df = pd.DataFrame(splits["train"])

# Merge to get train labels
train_labels = pd.merge(train_df, labels_df, on="path", how="left")

print(f"Training set: {len(train_labels)} temporal segments")

# Iterate over videos
for video_path in train_df['path'][:5]:
    # Get all segments for this video
    video_segments = train_labels[train_labels['path'] == video_path]

    print(f"\n{video_path}:")
    print(f"  Segments: {len(video_segments)}")

    for _, seg in video_segments.iterrows():
        duration = seg['end'] - seg['start']
        print(f"    {seg['start']:.3f}s - {seg['end']:.3f}s ({duration:.3f}s): "
              f"label {seg['label']}")
```

## Technical Properties

### Video Specifications
- **Resolution**: Variable (synthetic generation)
- **Duration**: 5.0625 seconds (consistent across all videos)
- **Frame count**: 81 frames
- **Frame rate**: 16 fps
- **Format**: MP4 (not included in this dataset, videos must be obtained separately)

### Annotation Properties
- **Temporal precision**: Sub-second (timestamps with decimal precision)
- **Coverage**: Most frames are labeled, with some gaps
- **Overlap handling**: Segments are annotated chronologically
- **Activity sequences**: Natural transitions (e.g., walk → fall → fallen → stand_up)

## Motion Types

Activities are classified into two main motion types:

**Dynamic motions** (e.g., `walk`, `fall`, `stand_up`):
- Labeled from the first frame where the motion begins
- End when the person reaches a resting state

**Static states** (e.g., `fallen`, `sitting`, `lying`):
- Begin when person comes to rest in that posture
- Continue until next motion begins

## Label Sequences

Videos often contain natural sequences of activities:
- **Fall sequence**: walk → fall → fallen → stand_up
- **Sit sequence**: walk → sit_down → sitting → stand_up
- **Lie sequence**: walk → lie_down → lying → stand_up

Not all transitions include static states (e.g., a person might stand_up immediately after falling without a `fallen` state).

## Future Extensions

This dataset is designed to support additional metadata and splits:
- **Demographics**: Age groups, ethnicity (to be added)
- **Cross-demographic splits**: Train on one demographic, test on another
- **Scenario variations**: Different environments, lighting, occlusions

## Citation

If you use WanFall in your research, please cite:

```bibtex
@misc{wanfall2025,
  title={WanFall: A Synthetic Activity Recognition Dataset},
  author={TODO},
  year={2025},
}
```

## License

The annotations and split definitions in this repository are released under [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/).

The video data is synthetic and must be obtained separately from the original source.

## Contact

For questions about the dataset, please contact [TODO].