# EgoMM: Tri-Modal Egocentric Dataset (Video + Audio + IMU)

EgoMM is a large-scale tri-modal egocentric dataset combining Video, Audio, and IMU data from head-mounted Meta Aria glasses. Built from EgoLife and Ego-Exo4D sources.

## Dataset Summary

| Split | EgoLife | EgoExo4D | Total | Narration files |
|-------|---------|----------|-------|-----------------|
| **narrated** (val/test) | 4,689 | 17,367 | 22,056 | 19,933 |
| **raw** (train) | 27,119 | 6,357 | 33,476 | — |
| **Total** | 31,808 | 23,724 | **55,532** | 19,933 |

- **Clip duration**: 30 seconds (fixed)
- **Total hours**: 463h
- **Modalities**: Video (MP4) + Audio (MP3) + IMU left/right (NPZ)
- **Video resolution**: EgoLife 768×768, EgoExo4D 448×448

## Structure

```
EgoMM/
├── narrated/                    (val/test: clips with human annotations)
│   ├── egolife/{participant}/DAY1/{clip_id}/
│   │   ├── video.mp4           (768×768, 20fps, video-only)
│   │   ├── audio.mp3           (separate audio track)
│   │   ├── imu_left.npz        (800Hz, 6-axis: accel_xyz + gyro_xyz)
│   │   ├── imu_right.npz       (1000Hz, 6-axis)
│   │   └── narration.json      (clip-relative timestamps)
│   └── egoexo4d/{activity}/{take_name}/{clip_id}/
│       ├── video.mp4           (448×448, video-only)
│       ├── audio.mp3
│       ├── imu_left.npz
│       ├── imu_right.npz
│       └── narration.json
├── raw/                         (train: clips without annotations)
│   ├── egolife/{participant}/{DAY2-7}/{clip_id}/
│   │   ├── video.mp4
│   │   ├── audio.mp3
│   │   ├── imu_left.npz
│   │   └── imu_right.npz
│   └── egoexo4d/{activity}/{take_name}/{clip_id}/
│       └── ...
└── metadata/
    ├── egolife/                 (DenseCaption SRTs, Transcript, Caption/QA JSONs)
    └── egoexo4d/                (atomic descriptions, expert commentary, splits)
```

## Download

```python
from huggingface_hub import snapshot_download

# Download narrated set only (val/test, ~120 GB)
snapshot_download(
    repo_id="ldkong/EgoMM",
    repo_type="dataset",
    local_dir="./EgoMM",
    allow_patterns=["narrated/**"]
)

# Download raw set only (train, ~370 GB)
snapshot_download(
    repo_id="ldkong/EgoMM",
    repo_type="dataset",
    local_dir="./EgoMM",
    allow_patterns=["raw/**"]
)

# Download metadata only
snapshot_download(
    repo_id="ldkong/EgoMM",
    repo_type="dataset",
    local_dir="./EgoMM",
    allow_patterns=["metadata/**"]
)

# Download a specific activity
snapshot_download(
    repo_id="ldkong/EgoMM",
    repo_type="dataset",
    local_dir="./EgoMM",
    allow_patterns=["narrated/egoexo4d/cooking/**"]
)
```

## Narration Format

Each `narration.json` contains clip-relative annotations:

```json
{
  "clip_id": "upenn_0714_Cooking_7_2_0090000",
  "narrations": [
    {"timestamp": 2.4, "text": "C scrolls through the mobile phone screen", "type": "atomic"},
    {"timestamp": 5.1, "text": "C places the phone down", "type": "atomic"}
  ],
  "expert_commentary": [
    {"timestamp": 0.0, "text": "He needs to give himself more room...", "type": "expert"}
  ],
  "sequence_info": {
    "take_name": "upenn_0714_Cooking_7_2",
    "clip_index": 3,
    "total_clips_in_take": 12,
    "start_sec_in_take": 90.0,
    "end_sec_in_take": 120.0
  }
}
```

### Reconstructing Long Sequences

Clips can be combined into longer sequences using `sequence_info`:

```python
import json
from pathlib import Path

# Load all clips from a take
take_name = "upenn_0714_Cooking_7_2"
clips = sorted(Path("narrated/egoexo4d/cooking").glob(f"{take_name}/*/narration.json"))

# Reconstruct full timeline
for clip_path in clips:
    clip = json.loads(clip_path.read_text())
    offset = clip["sequence_info"]["start_sec_in_take"]
    for n in clip["narrations"]:
        abs_time = offset + n["timestamp"]
        print(f"{abs_time:.1f}s: {n['text']}")
```

## IMU Format

Each NPZ file contains 7 arrays at 800Hz (left) or 1000Hz (right):
- `timestamp`: int64 nanoseconds
- `accel_x`, `accel_y`, `accel_z`: float64, m/s²
- `gyro_x`, `gyro_y`, `gyro_z`: float64, rad/s

```python
import numpy as np
data = np.load("imu_left.npz")
# Duration: (data['timestamp'][-1] - data['timestamp'][0]) / 1e9 ≈ 30.0s
# Gravity magnitude: ~9.8 m/s²
```

## Activities (EgoExo4D)

| Activity | Narrated clips | Raw clips |
|----------|---------------|-----------|
| cooking | ~7,800 | ~3,600 |
| music | ~1,850 | ~1,030 |
| health | ~1,950 | ~560 |
| bike_repair | ~1,470 | ~260 |
| dance | ~1,200 | ~380 |
| rock_climbing | ~1,040 | ~295 |
| basketball | ~935 | ~316 |
| soccer | ~740 | ~244 |

## Participants (EgoLife)

| Participant | DAY1 (narrated) | DAY2-7 (raw) |
|-------------|-----------------|--------------|
| A1_JAKE | ~780 clips | ~4,500 clips |
| A2_ALICE | ~780 clips | ~4,200 clips |
| A3_TASHA | ~780 clips | ~4,800 clips |
| A4_LUCIA | ~780 clips | ~4,600 clips |
| A5_KATRINA | ~780 clips | ~4,100 clips |
| A6_SHURE | ~780 clips | ~4,900 clips |

## Hardware

All data recorded with Meta Aria glasses:
- Video: 1408×1408 fisheye RGB (downscaled to 768×768 for EgoLife, 448×448 for EgoExo4D)
- Audio: built-in microphone
- IMU: dual 6-axis sensors (left 800Hz, right 1000Hz)
- Same hardware across EgoLife and EgoExo4D — models transfer between datasets

## Sources

- [EgoLife](https://egolife-dataset.github.io/) — 6 participants × 7 days of continuous daily recording
- [Ego-Exo4D](https://ego-exo4d-data.org/) — 4,168 takes of skilled activities (8 categories)

## License

Please refer to the original dataset licenses:
- EgoLife: S-Lab License 1.0
- Ego-Exo4D: Ego-Exo4D Dataset License