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README.md

@@ -93,12 +93,20 @@ from datasets import load_dataset
 
 # Random split with temporal segments (default)
 dataset = load_dataset("simplexsigil2/wanfall", "random")
+print(f"Train: {len(dataset['train'])} segments")  # 15,344 segments
 
 # Random split with frame-wise labels (81 per video)
 dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
+print(f"Train: {len(dataset['train'])} videos")    # 9,600 videos
 
 # Cross-demographic evaluation
 cross_age = load_dataset("simplexsigil2/wanfall", "cross_age")
+
+# 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"Demographics: {example['age_group']}, {example['race_ethnicity_omb']}")
 ```
 
 ## Activity Classes
@@ -227,53 +235,97 @@ dataset = load_dataset("simplexsigil2/wanfall", "cross_bmi")
 
 ## Usage
 
-### Loading Modes
+The dataset provides flexible loading options depending on your use case. The key distinction is between **segment-level** and **video-level** samples.
+
+### Loading Modes Overview
+
+| Mode | Sample Unit | Has start/end? | Has frame_labels? | Random Split Train Size |
+|------|-------------|----------------|-------------------|------------------------|
+| **Temporal Segments** | Segment | ✅ Yes | ❌ No | 15,344 segments (9,600 videos) |
+| **Frame-Wise Labels** | Video | ❌ No | ✅ Yes (81 labels) | 9,600 videos |
+
+### 1. Temporal Segments (Default)
+
+Load temporal segment annotations where **each sample is a segment** with start/end times. Multiple segments can come from the same video.
 
-**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
 
+# Each example is a SEGMENT (not a video)
 example = dataset['train'][0]
-# {'path': 'fall/fall_ch_001', 'label': 1, 'start': 0.0, 'end': 1.006, ...}
+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
+print(f"Unique videos: {len(set([ex['path'] for ex in dataset['train']]))}") # 9,600
 ```
 
-**Frame-Wise Labels** - Each sample is a video with 81 frame labels:
+**Use case:** Training models on activity classification where you want to extract and process only the relevant video segment for each activity.
+
+### 2. Frame-Wise Labels
+
+Load dense frame-level labels where **each sample is a video** with 81 frame labels. Each video appears exactly once.
+
 ```python
 dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
-# Train: 9,600 videos with 81 labels each
-# One sample per video
 
+# Each example is a VIDEO (not a segment)
 example = dataset['train'][0]
-# {'path': 'fall/fall_ch_001', 'frame_labels': [1, 1, 1, ..., 11, 11], ...}
+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
 ```
 
-**Additional Configs:**
+**Use case:** Training sequence models (e.g., temporal action segmentation) that process entire videos and predict frame-level labels.
+
+**Key features:**
+- Works with all split configs: Add `framewise=True` to any split
+- Efficient: 348KB compressed archive, automatically cached
+- Complete metadata: All demographic attributes included
+
+### 3. Additional Configurations
+
 ```python
-# All segments (no splits)
+# All segments without train/val/test splits
 dataset = load_dataset("simplexsigil2/wanfall", "labels")  # 19,228 segments
 
-# Video metadata only
+# Video metadata only (no labels)
 dataset = load_dataset("simplexsigil2/wanfall", "metadata")  # 12,000 videos
 
-# Paths only (minimal)
+# Paths only (minimal memory footprint)
 dataset = load_dataset("simplexsigil2/wanfall", "random", paths_only=True)
 ```
 
-### Usage Examples
+### Practical Examples
+
+#### Label Conversion
+
+Labels are stored as integers (0-15) but can be converted to strings:
 
-**Label Conversion:**
 ```python
 dataset = load_dataset("simplexsigil2/wanfall", "random")
 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
-all_labels = label_feature.names           # List all labels
+
+# Access all label names
+all_labels = label_feature.names           # ['walk', 'fall', 'fallen', ...]
 ```
 
-**Filter by Demographics:**
+#### Filter by Demographics
+
 ```python
 dataset = load_dataset("simplexsigil2/wanfall", "labels")
 segments = dataset['train']
@@ -283,21 +335,91 @@ elderly_falls = [
     ex for ex in segments
     if ex['age_group'] == 'elderly_65_plus' and ex['label'] == 1
 ]
+print(f"Found {len(elderly_falls)} elderly fall segments")
+
+# Filter by multiple demographics
+indoor_male_falls = [
+    ex for ex in segments
+    if ex['environment_category'] == 'indoor'
+    and ex['gender_presentation'] == 'male'
+    and ex['label'] == 1
+]
 ```
 
-**Cross-Demographic Evaluation:**
+#### Cross-Demographic Evaluation
+
 ```python
+# Train on young adults, test on children and elderly
 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
+# Train contains only: young_adults_18_34, middle_aged_35_64
+for example in cross_age['train'][:5]:
+    print(f"Train video: {example['path']}, age: {example['age_group']}")
+
+# Test contains: children_5_12, toddlers_1_4, elderly_65_plus
+for example in cross_age['test'][:5]:
+    print(f"Test video: {example['path']}, age: {example['age_group']}")
+```
+
+#### Training Loop Example
+
+```python
+from datasets import load_dataset
+import torch
+
+# Load dataset with frame-wise labels
+dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
+
+for epoch in range(num_epochs):
+    for example in dataset['train']:
+        video_path = example['path']
+        frame_labels = torch.tensor(example['frame_labels'])  # (81,)
+
+        # Load video frames (user must implement)
+        # frames = load_video(video_root / f"{video_path}.mp4")  # (81, H, W, 3)
+
+        # Forward pass
+        # outputs = model(frames)
+        # loss = criterion(outputs, frame_labels)
+        # loss.backward()
 ```
 
 ## 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.
+### Temporal Precision
+
+Annotations use sub-second accuracy with decimal timestamps (e.g., `start: 0.0, end: 1.006`). Most frames in videos are labeled, with minimal gaps between activities.
+
+### Activity Sequences
+
+Videos contain natural transitions between activities. Common sequences include:
+
+```
+walk → fall → fallen → stand_up
+walk → sit_down → sitting → stand_up
+walk → lie_down → lying → stand_up
+standing → squat_down → squatting → stand_up
+```
+
+Not all transitions include static states. For example, a person might `stand_up` immediately after falling without a `fallen` state.
+
+### Motion Types
+
+**Dynamic Actions** (transitions and movements):
+- Labeled from the **first frame** where the motion begins
+- End when the person reaches a **resting state** or begins a new action
+- If one motion is followed by another, the transition occurs at the first frame showing movement not explained by the previous action
+
+**Static States** (stationary postures):
+- Begin when person **comes to rest** in that posture
+- Continue until the next motion begins
+- Example for `sitting`: Does not start when the body touches the chair, but when the body loses its tension and settles into the seated position
+
+### Label Boundaries
+
+- **Dynamic → Dynamic**: Transition at first frame of new motion
+- **Dynamic → Static**: Static begins when movement stops and body settles
+- **Static → Dynamic**: Dynamic begins at first frame of movement
 
 ## Demographic Distribution
 
@@ -312,11 +434,36 @@ Rich demographic and scene metadata enables bias analysis and cross-demographic
 - Camera angles: 4 elevations × 4 azimuths × 2 distances
 - Shot types: Static wide and medium-wide
 
-## Vide  Data
+## Video Data
+
+**Videos are NOT included in this repository.** This dataset contains only annotations and metadata.
+
+### Video Specifications
+
+- **Duration:** 5.0625 seconds per clip
+- **Frame count:** 81 frames
+- **Frame rate:** 16 fps
+- **Format:** MP4 (H.264)
+- **Resolution:** Variable (synthetic generation)
+
+### Accessing Videos
+
+Videos will be released at a later point in time. Information about access will be provided here when available.
+
+When videos become available, they should be organized with the following structure:
+```
+video_root/
+├── fall/
+│   ├── fall_ch_001.mp4
+│   ├── fall_ch_002.mp4
+│   └── ...
+├── fallen/
+│   ├── fallen_ch_001.mp4
+│   └── ...
+└── ...
+```
 
-**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)
+The `path` field in the CSV corresponds to the relative path without the `.mp4` extension (e.g., `"fall/fall_ch_001"` → `video_root/fall/fall_ch_001.mp4`).
 
 ## License