| ---
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| license: other
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| license_name: rights-reserved-evaluation-sample
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| language:
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| - en
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| pretty_name: "Keystep Annotation and IMU Boundary Evaluation (Field Note #05)"
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| tags:
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| - robotics
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| - physical-ai
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| - egocentric
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| - imu
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| - action-segmentation
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| - annotation
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| - evaluation
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| size_categories:
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| - n<1K
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| configs:
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| - config_name: default
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| data_files:
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| - split: train
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| path: boundary_motion_results.csv
|
| ---
|
|
|
| # Keystep Annotation and IMU Boundary Evaluation
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|
|
| **Origin Data Lab · Technical Field Note #05 · 24 October 2026**
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|
|
| [](https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html)
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| 
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| 
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|
|
| > **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**.
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|
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| **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)
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|
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| **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
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|
|
| | Metric | Sample A — knife preparation | Sample B — seasonings and stirring |
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| |---|---:|---:|
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| | Clip duration | 66.0 s | 65.967 s |
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| | Frames (30 fps) | 1,979 | 1,978 |
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| | Human-labelled keysteps / phases | **13 / 3** | **12 / 3** |
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| | Gaps / overlaps | 0 / 0 | 0 / 0 |
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| | Missing required fields | 0 | 0 |
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| | Out-of-vocabulary verbs | 0 | 0 |
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| | Reported IMU-to-video alignment offset | 188 ms | 200 ms |
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|
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| **Native IMU sampling rate:** 410.6 Hz. All findings are from two clips from one operator, not a population-level benchmark.
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| 
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| *Human-labelled spans alongside the measured gyroscope signal; labels and motion do not always align one-to-one.*
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|
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| ## What the boundary test shows
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|
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| 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.
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|
| | Boundary set | n | Labelled score (°/s) | Random mean | Random p95 | p |
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| |---|---:|---:|---:|---:|---:|
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| | A · all | 12 | **21.9** | 7.2 | 14.7 | 0.005 |
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| | B · all | 11 | **29.9** | 10.0 | 20.1 | 0.002 |
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| | A · task-to-task | 8 | 3.3 | 4.1 | 6.8 | **0.67** |
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| | B · task-to-task | 7 | **10.2** | 3.9 | 7.5 | 0.003 |
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| 
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| **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.
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|
|
| ## Evaluation files
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|
|
| | Path | Contents |
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| |---|---|
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| | `boundary_motion_results.csv` | Four observed-versus-random boundary comparisons; suitable for tabular inspection |
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| | `annotation_example.jsonl` | **One** documented keystep annotation (`ks_A_01`, 0.0–7.5 s) |
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| | `figures/` | Five figures from the technical note, including the timeline and boundary test |
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| 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.
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|
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| ## Annotation format
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| 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`.
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| 
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| *Representative frames illustrate the action sequence; they are not a replacement for source video or full annotations.*
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|
|
| ## What this evaluation does not verify
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|
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| - **Semantic correctness of all labels.** Structural QA verifies completeness and consistency, not whether each action was labelled correctly.
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| - **Fine-grained hand-action boundaries.** A wearer-mounted IMU may miss subtle hand manipulations or register unrelated torso/head movement.
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| - **Generalization.** Two clips from one operator cannot establish reliability across people, tasks, devices or environments.
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| - **A complete training dataset.** This release contains one annotation example and four boundary-test rows, not 25 downloadable keystep records.
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|
|
| ## Cite
|
|
|
| ```bibtex
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| @misc{origindatalab2026fieldnote05,
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| author = {{Origin Data Lab}},
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| title = {Keystep Annotation and IMU Boundary Evaluation},
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| howpublished = {Technical Field Note \#05},
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| year = {2026},
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| url = {https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html}
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| }
|
| ```
|
|
|
| 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).
|
|
|
