Datasets:

simplexsigil2
/

wanfall

@@ -71,455 +71,255 @@ configs:
 # 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.
-**This dataset is currently under development and subject to change!**
-## 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**: 19,228
-- **Annotation format**: Temporal segmentation (start/end timestamps) with rich metadata
-- **Video duration**: 5.0625 seconds per clip
-- **Frame count**: 81 frames per video
-- **Frame rate**: 16 fps
-- **Annotation formats**: Temporal segments (start/end times) OR frame-wise labels (81 per video)
-- **Split configurations**: 4 split configs + framewise support
-  - `random`: 80/10/10 train/val/test split (seed 42) - 9,600/1,200/1,200 videos
-  - `cross_age`: Cross-age evaluation - 4,000/2,000/6,000 videos
-  - `cross_ethnicity`: Cross-ethnicity evaluation - 5,178/1,741/5,081 videos
-  - `cross_bmi`: Cross-BMI evaluation - 6,066/2,962/2,972 videos
-  - `framewise=True`: Add frame-wise labels (81 per video) to any split
-- **Metadata fields**: 12 demographic and scene attributes per video
-## 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), beginning with the moment of lost control and ending with a resting state or activity change.
-- **2. fallen** - Person in fallen state (on ground after fall)
-- **3. sit_down** - Transitioning from standing to sitting
-- **4. sitting** - Stationary sitting posture
-- **5. lie_down** - Intentionally lying down (not falling)
-- **6. lying** - Stationary lying posture (after intentional lie_down)
-- **7. stand_up** Getting up, either from fallen or lying into sitting or into standing position (not only get up to standing)
-- **8. standing** - Stationary standing posture
-- **9. other** - Actions not fitting above categories
-- **10. kneel_down** - Transitioning to kneeling position
-- **11. kneeling** - Stationary kneeling posture
-- **12. squat_down** - Transitioning to squatting position
-- **13. squatting** - Stationary squatting posture
-- **14. crawl** - Crawling movement on hands and knees
-- **15. jump** - Jumping action
-### Label Format
-The `labels/wanfall.csv` file contains temporal segments with rich metadata:
-```csv
-path,label,start,end,subject,cam,dataset,age_group,gender_presentation,monk_skin_tone,race_ethnicity_omb,bmi_band,height_band,environment_category,camera_shot,speed,camera_elevation,camera_azimuth,camera_distance
-```
-**Core Fields:**
-- `path`: Relative path to the video (without .mp4 extension, e.g., "fall/fall_ch_001")
-- `label`: Activity class ID (0-15)
-- `start`: Start time of the segment in seconds
-- `end`: End time of the segment in seconds
-- `subject`: Subject ID (`-1` for synthetic data)
-- `cam`: Camera view ID (`-1` for single view)
-- `dataset`: Dataset name (`wanfall`)
-**Demographic Metadata:**
-- `age_group`: One of 6 age categories
-  - toddlers_1_4, children_5_12, teenagers_13_17, young_adults_18_34, middle_aged_35_64, elderly_65_plus
-- `gender_presentation`: Visual gender presentation (male, female)
-- `monk_skin_tone`: [Monk Skin Tone scale](https://skintone.google/the-scale) (mst1-mst10)
-  - 10-point scale representing diverse skin tones from lightest to darkest
-  - Developed by Dr. Ellis Monk for inclusive representation
-- `race_ethnicity_omb`: [OMB race/ethnicity categories](https://www.census.gov/newsroom/blogs/random-samplings/2024/04/updates-race-ethnicity-standards.html)
-  - **white**: White/European American
-  - **black**: Black/African American
-  - **asian**: Asian
-  - **hispanic_latino**: Hispanic/Latino
-  - **aian**: American Indian and Alaska Native
-  - **nhpi**: Native Hawaiian and Pacific Islander
-  - **mena**: Middle Eastern and North African
-- `bmi_band`: Body type (underweight, normal, overweight, obese)
-- `height_band`: Height category (short, avg, tall)
-**Scene Metadata:**
-- `environment_category`: Scene location (indoor, outdoor)
-- `camera_shot`: Shot composition (static_wide, static_medium_wide)
-- `speed`: Frame rate (24fps_rt, 25fps_rt, 30fps_rt, std_rt)
-- `camera_elevation`: Camera height (eye, low, high, top)
-- `camera_azimuth`: Camera angle (front, rear, left, right)
-- `camera_distance`: Camera distance (medium, far)
-### 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
-The WanFall dataset provides a flexible Python API through the HuggingFace `datasets` library with multiple configurations and loading modes.
-### Quick Start
 ```python
 from datasets import load_dataset
-# Load with random 80/10/10 split (temporal segments, default)
 dataset = load_dataset("simplexsigil2/wanfall", "random")
-print(f"Train: {len(dataset['train'])} segments")
-print(f"Validation: {len(dataset['validation'])} segments")
-print(f"Test: {len(dataset['test'])} segments")
-# Access example
-example = dataset['train'][0]
-print(f"Video: {example['path']}")
-print(f"Activity: {example['label']} ({example['start']:.2f}s - {example['end']:.2f}s)")
-print(f"Age group: {example['age_group']}")
 ```
-### Dataset Configurations
-WanFall provides **7 configurations** for different use cases:
-**Key Distinction: Segment-Level vs Video-Level**
-| Configuration | Sample Unit | Train Size | Has start/end? | Has frame_labels? |
-|--------------|-------------|------------|----------------|-------------------|
-| `random` | **Segment** | 15,344 segments | ✅ Yes | ❌ No |
-| `random` + `framewise=True` | **Video** | 9,600 videos | ❌ No | ✅ Yes (81 labels) |
-| `cross_age` | **Segment** | 6,267 segments | ✅ Yes | ❌ No |
-| `cross_age` + `framewise=True` | **Video** | 4,000 videos | ❌ No | ✅ Yes (81 labels) |
-| `labels` | **Segment** | 19,228 segments | ✅ Yes | ❌ No |
-| `framewise` | **Video** | 12,000 videos | ❌ No | ✅ Yes (81 labels) |
-#### 1. **Temporal Segments** (Default)
-Load temporal segment annotations where **each sample is a segment** with start/end times:
 ```python
-# Default: random split with temporal segments
-dataset = load_dataset("simplexsigil2/wanfall")  # or "random"
-# Each example is a SEGMENT (not a video)
-example = dataset['train'][0]
-print(example['path'])      # "fall/fall_ch_001"
-print(example['label'])     # 1 (activity class ID)
-print(example['start'])     # 0.0 (start time in seconds)
-print(example['end'])       # 1.006 (end time in seconds)
-print(example['age_group']) # Demographic metadata
-# Dataset contains multiple segments per video
-print(f"Total segments in train: {len(dataset['train'])}")  # 15,344 segments
-print(f"Unique videos: {len(set([ex['path'] for ex in dataset['train']]))}") # 9,600 videos
 ```
-**Key characteristics:**
-- **Sample = Temporal Segment** (one video can have multiple segments)
-- Each segment has `start` and `end` times
-- Train: 15,344 segments from 9,600 videos
-- Val: 1,927 segments from 1,200 videos
-- Test: 1,957 segments from 1,200 videos
-**Available split configs:**
-- `random` - 80/10/10 split (15,344/1,927/1,957 segments)
-- `cross_age` - Cross-age evaluation (6,267/3,762/9,199 segments)
-- `cross_ethnicity` - Cross-ethnicity evaluation (8,267/2,762/8,199 segments)
-- `cross_bmi` - Cross-BMI evaluation (9,675/4,701/4,852 segments)
-#### 2. **Frame-Wise Labels**
-Load dense frame-level labels where **each sample is a video** with 81 labels:
-```python
-# Standalone: all 12,000 videos with frame-wise labels
-dataset = load_dataset("simplexsigil2/wanfall", "framewise")
-# With splits: random split with frame-wise labels
-dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
-# Each example is a VIDEO (not a segment)
-example = dataset['train'][0]
-print(example['path'])         # "fall/fall_ch_001"
-print(example['frame_labels']) # [1, 1, 1, ..., 11, 11] (81 labels)
-print(len(example['frame_labels']))  # 81 frames
-print(example['age_group'])    # Demographic metadata included
-# Dataset contains one sample per video
-print(f"Total videos in train: {len(dataset['train'])}")  # 9,600 videos
 ```
-**Key characteristics:**
-- **Sample = Video** (one sample per video, no segments)
-- Each video has 81 frame labels (no start/end times)
-- Train: 9,600 videos
-- Val: 1,200 videos
-- Test: 1,200 videos
-**Key features:**
-- **81 labels per video** (one per frame @ 16fps)
-- **Works with all split configs**: Add `framewise=True` to any split
-- **Efficient**: 348KB compressed archive, automatically cached
-- **Complete metadata**: All demographic attributes included
-#### 3. **Paths Only Mode**
-Load only video paths for custom video loading:
 ```python
-# Minimal loading: only video paths
-dataset = load_dataset("simplexsigil2/wanfall", "random", paths_only=True)
-# Only contains paths
-example = dataset['train'][0]
-print(example)  # {'path': 'fall/fall_ch_001'}
 ```
-#### 4. **All Segments** (No Splits)
-Load all 19,228 temporal segments without split partitions:
 ```python
-dataset = load_dataset("simplexsigil2/wanfall", "labels")
-all_segments = dataset['train']  # Single split with all segments
-print(f"Total segments: {len(all_segments)}")  # 19,228 segments
-# Each sample is a segment (like config 1, but no train/val/test split)
-example = all_segments[0]
-print(f"Path: {example['path']}")
-print(f"Segment: {example['start']:.2f}s - {example['end']:.2f}s")
-print(f"Label: {example['label']}")
 ```
-#### 5. **Video Metadata Only**
-Load only video-level metadata (12,000 videos):
 ```python
-dataset = load_dataset("simplexsigil2/wanfall", "metadata")
-metadata = dataset['train']  # 12,000 videos
-print(f"Columns: {metadata.column_names}")
-# ['path', 'dataset', 'age_group', 'gender_presentation', ...]
 ```
-### Complete Usage Examples
-#### Example 1: Training with Temporal Segments (Segment-Level)
-When using temporal segments, **each sample is a segment** with start/end times. Multiple segments can come from the same video.
 ```python
-from datasets import load_dataset
-# Load random split (segment-level samples)
 dataset = load_dataset("simplexsigil2/wanfall", "random")
-print(f"Training on {len(dataset['train'])} segments")  # 15,344 segments
-# Training loop - each iteration is ONE SEGMENT
-for example in dataset['train']:
-    video_path = example['path']
-    activity_label = example['label']  # 0-15
-    start_time = example['start']
-    end_time = example['end']
-    # Load only the frames for this segment
-    # frames = load_video_segment(video_path, start_time, end_time)
-    # model.train(frames, activity_label)
-# Note: The same video can appear multiple times with different segments
-# E.g., "fall/fall_ch_001" might have segments [0.0-1.0] and [1.0-5.0]
 ```
-#### Example 2: Training with Frame-Wise Labels (Video-Level)
-When using frame-wise labels, **each sample is a video** with 81 frame labels. Each video appears only once.
 ```python
-from datasets import load_dataset
-# Load random split with frame-wise labels (video-level samples)
 dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
-print(f"Training on {len(dataset['train'])} videos")  # 9,600 videos
-# Training loop - each iteration is ONE VIDEO
-for example in dataset['train']:
-    video_path = example['path']
-    frame_labels = example['frame_labels']  # 81 labels (one per frame)
-    # Load all frames from the video
-    # frames = load_video(video_path)  # Shape: (81, H, W, 3)
-    # model.train(frames, frame_labels)
-# Note: Each video appears exactly once with its 81 frame labels
 ```
-#### Example 3: Cross-Demographic Evaluation
 ```python
-from datasets import load_dataset
-# Train on young adults, test on elderly
-cross_age = load_dataset("simplexsigil2/wanfall", "cross_age", framewise=True)
-# Train
-for example in cross_age['train']:
-    age = cross_age['train'].features['age_group'].int2str(example['age_group'])
-    print(f"Training on {age}")  # "young_adults_18_34" or "middle_aged_35_64"
-# Test
-for example in cross_age['test']:
-    age = cross_age['test'].features['age_group'].int2str(example['age_group'])
-    print(f"Testing on {age}")  # "elderly_65_plus", "children_5_12", etc.
 ```
-#### Example 4: Filtering by Demographics
 ```python
-from datasets import load_dataset
-# Load all segments
 dataset = load_dataset("simplexsigil2/wanfall", "labels")
 segments = dataset['train']
-# Access label feature for conversion
-label_feature = segments.features['label']
-age_feature = segments.features['age_group']
 # Filter elderly fall segments
 elderly_falls = [
     ex for ex in segments
-    if age_feature.int2str(ex['age_group']) == 'elderly_65_plus'
-    and ex['label'] == 1  # fall
 ]
-print(f"Found {len(elderly_falls)} elderly fall segments")
 ```
-### Label Conversion
-Labels are stored as integers (0-15) but can be converted to strings:
 ```python
-dataset = load_dataset("simplexsigil2/wanfall", "random")
-# Get label feature
-label_feature = dataset['train'].features['label']
-# Convert integer to string
-label_name = label_feature.int2str(1)  # "fall"
-# Convert string to integer
-label_id = label_feature.str2int("walk")  # 0
-# Access all label names
-all_labels = label_feature.names
-print(all_labels)  # ['walk', 'fall', 'fallen', ...]
 ```
-### Cross-Demographic Evaluation Splits
-The dataset provides three cross-demographic split configurations for evaluating model robustness across different demographic groups:
-#### Cross-Age Split (`cross_age`)
-Evaluates model performance across different age groups:
-- **Train** (4,000 videos): Young adults (18-34) + Middle-aged (35-64)
-- **Validation** (2,000 videos): Teenagers (13-17)
-- **Test** (6,000 videos): Children (5-12) + Toddlers (1-4) + Elderly (65+)
-#### Cross-Ethnicity Split (`cross_ethnicity`)
-Evaluates model performance across different racial/ethnic groups with maximum phenotypic distance:
-- **Train** (5,178 videos): White + Asian + Hispanic/Latino
-- **Validation** (1,741 videos): American Indian and Alaska Native (AIAN)
-- **Test** (5,081 videos): Black + Middle Eastern/North African (MENA) + Native Hawaiian/Pacific Islander (NHPI)
-#### Cross-BMI Split (`cross_bmi`)
-Evaluates model performance across different body types:
-- **Train** (6,066 videos): Normal weight + Underweight
-- **Validation** (2,962 videos): Overweight
-- **Test** (2,972 videos): Obese
-## 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).
-## Demographic Diversity
-The dataset includes rich demographic and scene metadata for every video, enabling bias analysis and cross-demographic evaluation.
-However, while age and gender and ethnicity are quite reliable with consistent generation, the attributes were merely provided with the generation prompts and due to model biases, the resulting videos can deviate.
-### Overview
 ![Demographic Overview](figures/demographic_overview.png)
-### Scene Variations
-Beyond demographic diversity, the dataset includes:
-- **Environment**: Indoor and outdoor settings
-- **Camera Angles**: Multiple elevations (eye, low, high, top), azimuths (front, rear, left, right), and distances
-- **Camera Shots**: Static wide and medium-wide compositions
-- **Frame Rates**: Various speeds (24fps, 25fps, 30fps, standard real-time)
 ## 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, more information in the future.

 # WanFall: A Synthetic Activity Recognition Dataset
+Synthetic activity recognition dataset with 12,000 videos focused on fall detection and activities of daily living. Features rich demographic metadata and multiple evaluation protocols for bias analysis.
+**Status:** Under active development, subject to change.
 ## Dataset Statistics
+| Property | Value |
+|----------|-------|
+| **Videos** | 12,000 (5.0625s each) |
+| **Temporal Segments** | 19,228 |
+| **Activity Classes** | 16 |
+| **Frames per Video** | 81 frames @ 16fps |
+| **Annotation Formats** | Temporal segments OR frame-wise labels |
+| **Metadata Fields** | 12 (6 demographic + 6 scene) |
+## Quick Start
 ```python
 from datasets import load_dataset
+# Random split with temporal segments (default)
 dataset = load_dataset("simplexsigil2/wanfall", "random")
+# Random split with frame-wise labels (81 per video)
+dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
+# Cross-demographic evaluation
+cross_age = load_dataset("simplexsigil2/wanfall", "cross_age")
 ```
+## Activity Classes
+16 activity classes covering falls, posture transitions, and static states:
+```python
+LABEL_MAP = {
+    0: "walk",        # Walking movement, including jogging and running
+    1: "fall",        # Falling down action (loss of control)
+    2: "fallen",      # Person on ground after fall
+    3: "sit_down",    # Transition from standing to sitting
+    4: "sitting",     # Stationary sitting posture
+    5: "lie_down",    # Intentionally lying down (not falling)
+    6: "lying",       # Stationary lying posture
+    7: "stand_up",    # Getting up (to sitting or standing)
+    8: "standing",    # Stationary standing posture
+    9: "other",       # Unclassified activities
+    10: "kneel_down", # Transition to kneeling
+    11: "kneeling",   # Stationary kneeling posture
+    12: "squat_down", # Transition to squatting
+    13: "squatting",  # Stationary squatting posture
+    14: "crawl",      # Crawling movement on hands and knees
+    15: "jump",       # Jumping action
+}
+```
+**Motion Types:**
+- **Dynamic** (0-3, 5, 7, 9-10, 12, 14-15): Transitions and movements
+- **Static** (2, 4, 6, 8, 11, 13): Stationary postures
+## Data Format
+### CSV Columns (19 fields)
 ```python
+# Core annotation fields
+path                    # Video path (e.g., "fall/fall_ch_001")
+label                   # Activity class ID (0-15)
+start                   # Segment start time (seconds)
+end                     # Segment end time (seconds)
+subject                 # -1 (synthetic data)
+cam                     # -1 (single view)
+dataset                 # "wanfall"
+# Demographic metadata (6 fields)
+age_group               # toddlers_1_4, children_5_12, teenagers_13_17, young_adults_18_34, middle_aged_35_64, elderly_65_plus
+gender_presentation     # male, female
+monk_skin_tone          # mst1-mst10 (Monk Skin Tone scale)
+race_ethnicity_omb      # white, black, asian, hispanic_latino, aian, nhpi, mena (OMB categories)
+bmi_band                # underweight, normal, overweight, obese
+height_band             # short, avg, tall
+# Scene metadata (6 fields)
+environment_category    # indoor, outdoor
+camera_shot             # static_wide, static_medium_wide
+speed                   # 24fps_rt, 25fps_rt, 30fps_rt, std_rt
+camera_elevation        # eye, low, high, top
+camera_azimuth          # front, rear, left, right
+camera_distance         # medium, far
 ```
+**References:**
+- [Monk Skin Tone Scale](https://skintone.google/the-scale) - 10-point inclusive skin tone representation
+- [OMB Race/Ethnicity Standards](https://www.census.gov/newsroom/blogs/random-samplings/2024/04/updates-race-ethnicity-standards.html)
+## Split Configurations
+### 1. Random Split (80/10/10)
+Standard baseline with random video assignment (seed 42).
+| Split | Videos | Segments |
+|-------|--------|----------|
+| Train | 9,600 | 15,344 |
+| Val | 1,200 | 1,956 |
+| Test | 1,200 | 1,928 |
+```python
+dataset = load_dataset("simplexsigil2/wanfall", "random")
 ```
+### 2. Cross-Age Split
+Evaluates generalization across age groups. Train on adults, test on children and elderly.
+| Split | Videos | Age Groups |
+|-------|--------|------------|
+| **Train** | 4,000 | `young_adults_18_34` (2,000)<br>`middle_aged_35_64` (2,000) |
+| **Val** | 2,000 | `teenagers_13_17` (2,000) |
+| **Test** | 6,000 | `children_5_12` (2,000)<br>`toddlers_1_4` (2,000)<br>`elderly_65_plus` (2,000) |
 ```python
+dataset = load_dataset("simplexsigil2/wanfall", "cross_age")
 ```
+### 3. Cross-Ethnicity Split
+Evaluates generalization across racial/ethnic groups with maximum phenotypic distance. Train on White/Asian/Hispanic, test on Black/MENA/NHPI.
+| Split | Videos | Ethnicities |
+|-------|--------|-------------|
+| **Train** | 5,178 | `white` (1,709)<br>`asian` (1,691)<br>`hispanic_latino` (1,778) |
+| **Val** | 1,741 | `aian` (1,741) |
+| **Test** | 5,081 | `black` (1,684)<br>`mena` (1,680)<br>`nhpi` (1,717) |
 ```python
+dataset = load_dataset("simplexsigil2/wanfall", "cross_ethnicity")
 ```
+### 4. Cross-BMI Split
+Evaluates generalization across body types. Train on normal/underweight, test on obese.
+| Split | Videos | BMI Bands |
+|-------|--------|-----------|
+| **Train** | 6,066 | `normal` (3,040)<br>`underweight` (3,026) |
+| **Val** | 2,962 | `overweight` (2,962) |
+| **Test** | 2,972 | `obese` (2,972) |
 ```python
+dataset = load_dataset("simplexsigil2/wanfall", "cross_bmi")
 ```
+**Note:** All cross-demographic splits contain the same videos, just organized differently. Total unique videos: 12,000.
+## Usage
+### Loading Modes
+**Temporal Segments (default)** - Each sample is a segment with start/end times:
 ```python
 dataset = load_dataset("simplexsigil2/wanfall", "random")
+# Train: 15,344 segments from 9,600 videos
+# One video can have multiple segments
+example = dataset['train'][0]
+# {'path': 'fall/fall_ch_001', 'label': 1, 'start': 0.0, 'end': 1.006, ...}
 ```
+**Frame-Wise Labels** - Each sample is a video with 81 frame labels:
 ```python
 dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
+# Train: 9,600 videos with 81 labels each
+# One sample per video
+example = dataset['train'][0]
+# {'path': 'fall/fall_ch_001', 'frame_labels': [1, 1, 1, ..., 11, 11], ...}
+```
+**Additional Configs:**
+```python
+# All segments (no splits)
+dataset = load_dataset("simplexsigil2/wanfall", "labels")  # 19,228 segments
+# Video metadata only
+dataset = load_dataset("simplexsigil2/wanfall", "metadata")  # 12,000 videos
+# Paths only (minimal)
+dataset = load_dataset("simplexsigil2/wanfall", "random", paths_only=True)
 ```
+### Usage Examples
+**Label Conversion:**
 ```python
+dataset = load_dataset("simplexsigil2/wanfall", "random")
+label_feature = dataset['train'].features['label']
+label_name = label_feature.int2str(1)      # "fall"
+label_id = label_feature.str2int("walk")   # 0
+all_labels = label_feature.names           # List all labels
 ```
+**Filter by Demographics:**
 ```python
 dataset = load_dataset("simplexsigil2/wanfall", "labels")
 segments = dataset['train']
 # Filter elderly fall segments
 elderly_falls = [
     ex for ex in segments
+    if ex['age_group'] == 'elderly_65_plus' and ex['label'] == 1
 ]
 ```
+**Cross-Demographic Evaluation:**
 ```python
+cross_age = load_dataset("simplexsigil2/wanfall", "cross_age", framewise=True)
+# Train contains only young_adults_18_34 and middle_aged_35_64
+# Test contains children_5_12, toddlers_1_4, elderly_65_plus
 ```
+## Annotation Guidelines
+**Motion Types:**
+- **Dynamic** actions are labeled from first motion frame until resting state, if one motion is followed by another, the change occurs with the first frames which shows movement which is not explained by the previous action.
+- **Static** states begin when person comes to rest, continue until next motion. Example for sitting: It does not start when the body touches the chair, but when the body looses its tension and comes to rest.
+## Demographic Distribution
+Rich demographic and scene metadata enables bias analysis and cross-demographic evaluation.
 ![Demographic Overview](figures/demographic_overview.png)
+**Note:** Metadata represents generation prompts. Due to generative model biases, actual visual attributes may deviate, particularly for ethnicity and body type. Age and gender are generally more reliable.
+**Scene Variations:**
+- Environments: Indoor/outdoor settings
+- Camera angles: 4 elevations × 4 azimuths × 2 distances
+- Shot types: Static wide and medium-wide
+## Vide  Data
+**Videos will be released at a later point of time and are currently NOT included in this repository.**
+- **Video specs:** 5.0625s duration, 81 frames @ 16fps, MP4 format
+- **Access:** Videos must be obtained separately (information forthcoming)
 ## License
+[![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/)
+Annotations and metadata released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Video data is synthetic and subject to separate terms.