Datasets:

origin-data-lab
/

egocentric-keystep-annotation

@@ -1,69 +1,131 @@
 ---
 license: other
 license_name: rights-reserved-evaluation-sample
-pretty_name: Origin Data Lab Keystep Annotation Evaluation Samples
 tags:
-- egocentric
 - robotics
 - imu
 - annotation
 - evaluation
 ---
-# Keystep Annotation — Evaluation Samples
-[![Field Note](https://img.shields.io/badge/Origin_Data_Lab-Field_Note_05-155e75)](https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html) ![Physical AI](https://img.shields.io/badge/Physical_AI-Egocentric-334155) ![QA](https://img.shields.io/badge/Structural_QA-0_errors-16a34a) ![Scope](https://img.shields.io/badge/Scope-2_clips_%7C_25_keysteps-475569)
-**Origin Data Lab · Technical Field Note #05**
-**[Technical field note](https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html) · [GitHub](https://github.com/origin-data-lab/egocentric-keystep-annotation) · [Hugging Face](https://huggingface.co/datasets/origin-data-lab/egocentric-keystep-annotation)**
-> **Limited evaluation release:** one documented annotation example and four boundary-test rows. **Not** the complete 25-keystep dataset.
-## Dataset overview
 | Metric | Sample A — knife preparation | Sample B — seasonings and stirring |
 |---|---:|---:|
-| Duration | 66.0 s | 65.967 s |
-| Frames | 1,979 | 1,978 |
-| Keysteps / phases | 13 / 3 | 12 / 3 |
 | Gaps / overlaps | 0 / 0 | 0 / 0 |
 | Missing required fields | 0 | 0 |
 | Out-of-vocabulary verbs | 0 | 0 |
-### What is included
-| File | Rows / scope | Description |
-|---|---|---|
-| `annotation_example.jsonl` | 1 example | `ks_A_01`, a documented annotation span |
-| `boundary_motion_results.csv` | 4 rows | Observed vs random-boundary motion scores |
-| `figures/` | 5 figures | Visualization of samples and evaluation |
-## Data fields
-**Annotation example:** `id`, `t_start`, `t_end`, `phase`, `verb`, `object`, `tool`, `active_hand`, `support_hand`, `instruction`, `state_before`, `state_after`, `outcome`.
-**Boundary CSV:** `sample`, `boundary_set`, `n`, `labelled_deg_per_s`, `random_mean_deg_per_s`, `random_p95_deg_per_s`, `p_value`. Angular rates are in degrees per second; `p_value` is the fraction of 2,000 random sets scoring at least as high as the observed set.
-## Evaluation results
-| Boundary set | n | Labelled Δ median (°/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 |
-![Keystep labels aligned with wearer motion](figures/keystep-timeline-gyro.webp)
-![Boundary-motion comparison](figures/boundary-motion-test.webp)
-## Intended use and limitations
-Suitable for inspecting a documented annotation format and reviewing a small, published evaluation table. **Not suitable** as a complete video dataset or a statistically representative benchmark.
-The study covers **two clips from one operator**, not a representative population. Structural QA checks formatting and consistency; it does **not** prove boundary accuracy. A body-worn IMU can reveal coarse wearer motion, but cannot reliably validate fine hand actions such as knife manipulation. **No raw videos, raw IMU streams, or complete 25-keystep annotation files are included in this public release.**
-## Provenance, rights and citation
-Source: [Origin Data Lab Field Note #05](https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html). All rights reserved unless Origin Data Lab grants separate permission. Contact [contact@origindatalab.io](mailto:contact@origindatalab.io) for permitted reuse or a larger evaluation package.

 ---
 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).