---
license: cc-by-4.0
pretty_name: Egocentric 3-Camera Array (Head + Both Wrists)
task_categories:
- robotics
- video-classification
tags:
- egocentric
- first-person
- multi-view
- wrist-camera
- imu
- bimanual
- manipulation
- embodied-ai
- sensor-fusion
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Egocentric 3-Camera Array (Head + Both Wrists)

Three long-form household and warehouse tasks recorded **simultaneously from three body-mounted cameras** — head, left wrist and right wrist — each with **per-frame timestamps** and its own **high-rate gyroscope and accelerometer**.

This is a bimanual manipulation dataset: the wrist cameras see what each hand is doing at close range while the head camera carries the scene context.

<video controls width="900" src="https://huggingface.co/datasets/humyn-labs/Egocentric-3-Camera-Array/resolve/main/preview/card_sample_3cam_montage.mp4"></video>

*Preview: 45 s of the Cooking task, all three views at the same instant. Left panel = **head**, middle = **left wrist**, right = **right wrist**. Built for this card only; the repo ships each view as a separate file.*

---

## At a glance

| | |
| --- | --- |
| Tasks | 3 (Sort & Repack · Cooking · Packing T-shirts) |
| Cameras per task | 3 (head, left wrist, right wrist) |
| Activity duration | 60.9 min |
| Total video | **182.6 min** across 9 files |
| Resolution | 1440×1080 (4:3) @ 29.97 fps |
| Audio | none |
| Video frames | 328,129 (with per-frame timestamps) |
| Gyroscope samples | **17,522,016** (~1576–1621 Hz per device) |
| Accelerometer samples | 2,189,132 (~197–203 Hz per device) |
| Files | 36 = 3 tasks × 3 cameras × (video + times + gyro + accel) |

| Task | Duration | Frames (head/L/R) |
| --- | --- | --- |
| Sort & Repack | 20.11 min | 36141 / 36142 / 36142 |
| Cooking | 20.07 min | 36075 / 36075 / 36075 |
| Packing T-shirts | 20.68 min | 37159 / 37160 / 37160 |

---

## Synchronisation — read this first

**Each camera keeps its own clock, and every clock starts at 0.** There is no shared sync signal, no common
epoch, and no clapperboard event in the released files. Alignment is by the assumption that all three devices
started together.

That assumption holds well but not perfectly. Measured drift between the three clocks at end of recording:

| Task | Spread across the 3 cameras | In frames @ 29.97 fps |
| --- | --- | --- |
| Sort & Repack | 33.4 ms | ~1.0 |
| Cooking | 19.1 ms | ~0.6 |
| Packing T-shirts | 32.8 ms | ~1.0 |

So cross-camera alignment is good to **about one frame over a 20-minute recording**, and frame counts differ
by at most 1 between cameras. That is fine for action recognition and coarse fusion; it is **not** good enough
for anything needing sub-millisecond stereo-grade sync. Each device also runs a slightly different IMU rate
(1576 vs 1604 vs 1621 Hz), which is the same independent-oscillator effect.

Within a single camera, video and IMU **do** share a timebase — `video_times.csv`, `gyro.csv` and `accel.csv`
all use the same `t_seconds` column, so per-camera fusion is exact.

```python
import csv, bisect

times = [float(r["t_seconds"]) for r in csv.DictReader(open("timestamps/cooking_head_video_times.csv"))]
gyro  = [(float(r["t_seconds"]), float(r["gx"]), float(r["gy"]), float(r["gz"]))
         for r in csv.DictReader(open("imu/cooking_head_gyro.csv"))]

def gyro_at_frame(i):                      # nearest gyro sample to frame i
    t = times[i]
    k = bisect.bisect_left(gyro, (t,))
    return min(gyro[max(0, k-1):k+1], key=lambda g: abs(g[0] - t))
```

---

## Repository layout

```
data/train-*.parquet   # 720p previews of all three views + metadata (powers the viewer)
videos/*.mp4           # full-resolution 1440×1080 captures, 9 files
timestamps/*_video_times.csv   # frame_idx, t_seconds
imu/*_gyro.csv                 # t_seconds, gx, gy, gz
imu/*_accel.csv                # t_seconds, ax, ay, az
preview/               # 720p proxies; card_sample_3cam_montage.mp4 is the 3-up clip above
metadata.csv           # flat table
```

One row per **task**, not per camera — each row carries all three views and all nine sidecar files.

## Columns

| Column | Description |
| --- | --- |
| `video` | 720p preview of the **head** camera — plays in the viewer |
| `left_wrist_preview_video`, `right_wrist_preview_video` | 720p previews of the wrist cameras |
| `sample_id`, `task` | e.g. `cooking` / `Cooking` |
| `{cam}_video_path` | Full-resolution file, for `cam` in `head`, `left_wrist`, `right_wrist` |
| `{cam}_times_path`, `{cam}_gyro_path`, `{cam}_accel_path` | Sidecar CSVs |
| `{cam}_duration_seconds`, `{cam}_width`, `{cam}_height`, `{cam}_fps` | Probed from the media |
| `{cam}_frames` | Rows in that camera's `video_times.csv` |
| `{cam}_gyro_rows`, `{cam}_accel_rows` | IMU sample counts |
| `{cam}_gyro_hz`, `{cam}_accel_hz` | Measured rate, not nominal |
| `{cam}_gyro_columns`, `{cam}_accel_columns` | CSV headers |
| `{cam}_times_t_end`, `{cam}_gyro_t_end`, `{cam}_accel_t_end` | Last timestamp — use these to check drift |
| `{cam}_bytes`, `{cam}_sha256` | Size and integrity of the original |

## Units

Accelerometer is in **m/s²** and gyroscope in **rad/s**, both verified empirically rather than assumed:
resting accelerometer magnitude has a median of 9.83 across samples, and gyroscope magnitude sits at
3.5 rad/s (200 °/s) at the 99th percentile, which is the expected range for wrist motion. Note this differs
from the
[LATAM residential release](https://huggingface.co/datasets/humyn-labs/LATAM-Egocentric-Residential-IMU),
whose accelerometer is in **g** — do not mix the two without rescaling.

---

## Usage

```python
from datasets import load_dataset

ds = load_dataset("humyn-labs/Egocentric-3-Camera-Array", split="train")
r = ds[0]
print(r["task"], r["head_frames"], r["head_gyro_hz"], "Hz")
```

Full-resolution video and all IMU:

```python
from huggingface_hub import snapshot_download
snapshot_download("humyn-labs/Egocentric-3-Camera-Array", repo_type="dataset",
                  allow_patterns=["videos/*", "imu/*", "timestamps/*"])
```

## Intended uses

Bimanual manipulation · multi-view action recognition · hand-activity classification from wrist cameras ·
video + IMU sensor fusion · viewpoint-invariant representation learning · long-horizon procedural task
segmentation · imitation learning for two-armed robots.

## Limitations

- **Three recordings.** Long (~20 min each) but only three tasks, with no held-out split. The source data
  carries no subject identifiers, so subject diversity cannot be established from this release.
- **Cross-camera sync is implicit and drifts ~1 frame**, as described above.
- **No action labels, no annotations, no captions.** This release is raw sensor data only.
- **No magnetometer and no camera calibration**, so no absolute orientation and no metric 3D.
- **4:3 aspect at 1440×1080**, unlike the 16:9 clips elsewhere in this collection — check your resize path.
- **Wide-angle lenses** produce noticeable barrel distortion, uncorrected and with no distortion coefficients
  supplied.
- **No audio.**

## Provenance

Curated from the HumynLabs egocentric sample collection. All technical fields — durations, frame counts, IMU
rates and clock spans — were measured from the files rather than copied from the source sheet. All 36 files
resolved and downloaded with matching byte sizes; nothing was dropped.

## License

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Recorded with participant consent for research use.

**Privacy note specific to this release:** the wrist-mounted cameras point back toward the wearer for much of
each recording and **frequently capture the wearer's face**, which head-mounted egocentric footage does not.
Home and workplace interiors and incidental bystanders also appear. Please handle accordingly and do not
attempt to identify individuals.

## Citation

```bibtex
@misc{humynlabs2026egocentric3cam,
  title  = {Egocentric 3-Camera Array (Head + Both Wrists)},
  author = {HumynLabs},
  year   = {2026},
  url    = {https://huggingface.co/datasets/humyn-labs/Egocentric-3-Camera-Array}
}
```