---
license: other
license_name: rights-reserved-evaluation-sample
language:
- en
pretty_name: "Keystep Annotation and IMU Boundary Evaluation (Field Note #05)"
tags:
- robotics
- physical-ai
- egocentric
- imu
- action-segmentation
- annotation
- evaluation
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: boundary_motion_results.csv
---

# Keystep Annotation and IMU Boundary Evaluation

**Origin Data Lab · Technical Field Note #05 · 24 October 2026**

[![Field Note](https://img.shields.io/badge/Field_Note-%2305-0B1220?style=for-the-badge)](https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html)
![Evaluation](https://img.shields.io/badge/Boundary-Evaluation-3776AB?style=for-the-badge)
![Scope](https://img.shields.io/badge/2_Clips-25_Keysteps-16A34A?style=for-the-badge)

> **Short answer.** Structural checks found **zero reported gaps, overlaps, missing required fields or out-of-vocabulary verbs** across 25 human-labelled keysteps in two egocentric cooking clips. A body-worn IMU showed measurable motion changes at some boundaries, but **not all task-to-task boundaries were distinguishable from random positions**. IMU motion is a useful review signal, **not proof of fine hand-action boundary accuracy**.

**Full article:** [Technical Field Note #05 — Keystep Annotation for Egocentric Robot-Learning Data](https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html)

**Code and supporting evidence:** [GitHub — origin-data-lab/egocentric-keystep-annotation](https://github.com/origin-data-lab/egocentric-keystep-annotation) · [Hugging Face files](https://huggingface.co/datasets/origin-data-lab/egocentric-keystep-annotation/tree/main)

---

## Key results

| Metric | Sample A — knife preparation | Sample B — seasonings and stirring |
|---|---:|---:|
| Clip duration | 66.0 s | 65.967 s |
| Frames (30 fps) | 1,979 | 1,978 |
| Human-labelled keysteps / phases | **13 / 3** | **12 / 3** |
| Gaps / overlaps | 0 / 0 | 0 / 0 |
| Missing required fields | 0 | 0 |
| Out-of-vocabulary verbs | 0 | 0 |
| Reported IMU-to-video alignment offset | 188 ms | 200 ms |

**Native IMU sampling rate:** 410.6 Hz. All findings are from two clips from one operator, not a population-level benchmark.

![Keystep-labelled gyroscope timeline for the two evaluated clips](figures/keystep-timeline-gyro.webp)

*Human-labelled spans alongside the measured gyroscope signal; labels and motion do not always align one-to-one.*

## What the boundary test shows

The test compares motion changes around labelled keystep boundaries against **2,000 random boundary sets** per comparison. The motion score uses gyroscope magnitude in windows around each boundary. Two comparisons include all boundaries; two exclude transitions involving non-task segments.

| Boundary set | n | Labelled score (°/s) | Random mean | Random p95 | p |
|---|---:|---:|---:|---:|---:|
| A · all | 12 | **21.9** | 7.2 | 14.7 | 0.005 |
| B · all | 11 | **29.9** | 10.0 | 20.1 | 0.002 |
| A · task-to-task | 8 | 3.3 | 4.1 | 6.8 | **0.67** |
| B · task-to-task | 7 | **10.2** | 3.9 | 7.5 | 0.003 |

![Labelled versus random-boundary motion comparison](figures/boundary-motion-test.webp)

**Interpretation.** Sample A's task-to-task boundaries were **not distinguished from random positions** by this test (p=0.67), while Sample B's were (p=0.003). Strong coarse-motion changes should not be mistaken for reliable detection of every hand-action transition.

## Evaluation files

| Path | Contents |
|---|---|
| `boundary_motion_results.csv` | Four observed-versus-random boundary comparisons; suitable for tabular inspection |
| `annotation_example.jsonl` | **One** documented keystep annotation (`ks_A_01`, 0.0–7.5 s) |
| `figures/` | Five figures from the technical note, including the timeline and boundary test |

The public release is a **limited evaluation sample**. It does **not** contain the original videos, raw IMU signals or complete annotation files for all 25 keysteps.

## Annotation format

The JSONL example describes an observed action span using `id`, `t_start`, `t_end`, `phase`, `verb`, `object`, `tool`, `active_hand`, `support_hand`, `instruction`, `state_before`, `state_after` and `outcome`.

![Representative keystep frames for Sample A](figures/keystep-frames-knife.webp)

*Representative frames illustrate the action sequence; they are not a replacement for source video or full annotations.*

## What this evaluation does not verify

- **Semantic correctness of all labels.** Structural QA verifies completeness and consistency, not whether each action was labelled correctly.
- **Fine-grained hand-action boundaries.** A wearer-mounted IMU may miss subtle hand manipulations or register unrelated torso/head movement.
- **Generalization.** Two clips from one operator cannot establish reliability across people, tasks, devices or environments.
- **A complete training dataset.** This release contains one annotation example and four boundary-test rows, not 25 downloadable keystep records.

## Cite

```bibtex
@misc{origindatalab2026fieldnote05,
  author       = {{Origin Data Lab}},
  title        = {Keystep Annotation and IMU Boundary Evaluation},
  howpublished = {Technical Field Note \#05},
  year         = {2026},
  url          = {https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html}
}
```

Or:

```text
Origin Data Lab (2026). Keystep Annotation and IMU Boundary Evaluation. Technical Field Note #05.
https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html
```

## License

This evaluation package is marked **rights reserved**. It is **not** licensed under the CC BY-NC 4.0 terms used by some earlier field-note repositories. Request permission before redistributing or reusing the text, results or figures beyond applicable exceptions.

## Related

- [All Technical Field Notes](https://origindatalab.io/technical-field-notes/)
- [한국어 — 키스텝 어노테이션 검증 (Field Note #05)](https://origindatalab.io/ko/technical-field-notes/keystep-annotation-egocentric-robot-learning.html)
- [#04 Stereo Calibration Validation](https://origindatalab.io/technical-field-notes/stereo-calibration-check.html) · [dataset card](https://huggingface.co/datasets/origindatalab/stereo-calibration-validation)

---

**Origin Data Lab** — real-world data production for Physical AI and robotics · [origindatalab.io](https://origindatalab.io) · [contact@origindatalab.io](mailto:contact@origindatalab.io)

Need evaluation-ready egocentric data with annotation and QC requirements tailored to your project? [Contact us](https://origindatalab.io/contact.html).