Upload folder using huggingface_hub

.claude/settings.local.json

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+{
+  "permissions": {
+    "allow": [
+      "Read(//home/dschneider/workspace/PROJECTS/omnifall/hf/**)",
+      "Read(//lsdf/users/dschneider-kf3609/workspace/PROJECTS/omnifall/hf/**)",
+      "Read(//lsdf/users/dschneider-kf3609/workspace/PROJECTS/wanfall/labels/**)",
+      "Bash(source ~/miniconda3/bin/activate ccode)"
+    ],
+    "deny": [],
+    "ask": []
+  }
+}

.gitignore

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+export_via_to_csv.py
+create_splits.py
+

README.md

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----
-license: cc-by-nc-4.0
----
+---
+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].

labels/label2id.csv

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+label,id
+walk,0
+fall,1
+fallen,2
+sit_down,3
+sitting,4
+lie_down,5
+lying,6
+stand_up,7
+standing,8
+other,9
+kneel_down,10
+kneeling,11
+squat_down,12
+squatting,13
+crawl,14
+jump,15

splits/test.csv

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