| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pretty_name: PULSE |
| size_categories: |
| - 10M<n<100M |
| task_categories: |
| - time-series-forecasting |
| - video-classification |
| - other |
| tags: |
| - multi-modal |
| - human-activity-recognition |
| - motion-capture |
| - emg |
| - eye-tracking |
| - imu |
| - fingertip-pressure |
| - wearable-sensing |
| - long-horizon |
| - action-segmentation |
| - human-robot-interaction |
| - myoelectric |
| configs: |
| - config_name: emg |
| data_files: |
| - split: train |
| path: "data/v[0-9]*/s[0-9]*/aligned_emg_100hz.csv" |
| - config_name: imu |
| data_files: |
| - split: train |
| path: "data/v[0-9]*/s[0-9]*/aligned_imu_100hz.csv" |
| - config_name: mocap |
| data_files: |
| - split: train |
| path: "data/v[0-9]*/s[0-9]*/aligned_mocap_100hz.csv" |
| - config_name: eyetrack |
| data_files: |
| - split: train |
| path: "data/v[0-9]*/s[0-9]*/aligned_eyetrack_100hz.csv" |
| - config_name: pressure |
| data_files: |
| - split: train |
| path: "data/v[0-9]*/s[0-9]*/aligned_pressure_100hz.csv" |
| - config_name: action_segments |
| data_files: |
| - split: train |
| path: "annotations_flat/segments.csv" |
| extra_gated_prompt: | |
| By accessing PULSE you agree to the CC BY-NC 4.0 license and the |
| additional use restrictions in LICENSE: no commercial redeployment, no |
| attempts at re-identification of participants, and no use for covert worker |
| surveillance or biometric identification without freely given consent. |
| --- |
| |
| <div align="center"> |
|
|
| # PULSE |
|
|
| **A Synchronized Five-Modality Dataset for Multi-Modal Daily Activity Understanding** |
|
|
| *MoCap · EMG · Eye Tracking · IMU · Fingertip Pressure — all hardware-synced at 100 Hz* |
|
|
| `NeurIPS 2026 Evaluations & Datasets` · `under double-blind review` · `CC BY-NC 4.0` |
|
|
| </div> |
|
|
| --- |
|
|
| ## At a glance |
|
|
| | 40 | 9 | 5 | 7,789 | |
| |:---:|:---:|:---:|:---:| |
| | **volunteers** | **scenarios** (S1–S8 + S9 motion primitives) | **modalities** @ 100 Hz | **dense action segments** | |
| | 337 total recordings<br>(304 task + 33 S9) | ~9.7 h total (S1–S8: ~7.0 h) | <10 ms cross-modal drift | 6 benchmark tasks | |
| | 282 annotated | 36 annotated volunteers | 17 motor primitives observed | 57 unique objects | |
|
|
| > **Status — anonymous review release.** Author names, institution, and the |
| > permanent dataset URL will be revealed in the camera-ready version. |
| > Submission-time artifacts are available: companion code at |
| > [`velvet-pine-22/PULSE-code`](https://huggingface.co/velvet-pine-22/PULSE-code), a |
| > small representative sample at |
| > [`velvet-pine-22/PULSE-sample`](https://huggingface.co/datasets/velvet-pine-22/PULSE-sample), |
| > and Croissant 1.0 metadata with RAI fields in `croissant.json`. |
|
|
| > **Tip.** The **Dataset Viewer** at the top of this page is fully interactive |
| > — switch between the `emg / imu / mocap / eyetrack / pressure / action_segments` |
| > subsets via the dropdown, and hover any column to see its per-value |
| > distribution. The `action_segments` subset is the flat per-segment table |
| > (7,789 rows): hover the `primitive`, `hand`, or `object` columns to browse |
| > the label distributions interactively. The static figures further down |
| > summarise cross-tab statistics (per-modality coverage, per-volunteer |
| > matrix) the column-wise viewer cannot compute. |
|
|
| --- |
|
|
| ## Quick start |
|
|
| Load any single modality directly through `datasets`: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| emg = load_dataset("velvet-pine-22/PULSE", "emg", split="train") |
| # Available configs: emg, imu, mocap, eyetrack, pressure, action_segments. |
| # Each per-modality CSV has a consistent schema across volunteers. |
| # (volunteer, scenario) is recovered from the source file path; the |
| # per-modality CSVs themselves do not carry id columns. |
| |
| # Flat action-segments table (also browsable in the Dataset Viewer above): |
| seg = load_dataset("velvet-pine-22/PULSE", "action_segments", split="train") |
| # 7,789 rows × 15 columns: volunteer, scenario, primitive, hand, object, ... |
| ``` |
|
|
| Inspect one recording on disk: |
|
|
| ```text |
| data/v1/s1/ |
| ├── aligned_emg_100hz.csv |
| ├── aligned_eyetrack_100hz.csv |
| ├── aligned_imu_100hz.csv |
| ├── aligned_mocap_100hz.csv |
| ├── aligned_pressure_100hz.csv |
| ├── aligned_myo_pose_100hz.csv # auxiliary |
| ├── aligned_myo_quat_100hz.csv # auxiliary |
| ├── alignment_metadata.json |
| ├── raw/ # opt-in: original Qualisys TSV (~40 MB) |
| │ └── aligned_v1s1_s_Q.tsv |
| └── videos/ # opt-in: scene cam + eye-tracking video (~200 MB; no audio or visible faces) |
| ├── trimmed_v1s1_*Scene Cam.mp4 |
| └── trimmed_v1s1_*Eye Tracking Video.mp4 |
| ``` |
|
|
| Want only the benchmark streams? Skip the heavy bits at download: |
|
|
| ```bash |
| hf download velvet-pine-22/PULSE --repo-type dataset \ |
| --exclude 'data/v*/s*/raw/**' \ |
| --exclude 'data/v*/s*/videos/**' |
| # ~10 GB instead of ~86 GB |
| ``` |
|
|
| --- |
|
|
| ## Dataset statistics |
|
|
| ### Modality availability |
|
|
| Realistic missing-modality pattern across the 304 S1–S8 task recordings: |
| EMG (99.3%), IMU (98.4%), EyeTrack (92.8%), and Scene Cam (93.1%) are |
| present on >90% of task recordings; **MoCap (80.9%) and Pressure (69.7%) |
| are the two limiting modalities**. All five sensors are simultaneously |
| available on **198 / 304 task recordings (65.1%)** — drives the T4 |
| missing-modality benchmark. |
|
|
| <div align="center"> |
| <img src="https://huggingface.co/datasets/velvet-pine-22/PULSE/resolve/main/assets/modality_coverage.png" alt="Per-modality availability across 304 S1-S8 task recordings" width="800" /> |
| </div> |
|
|
| ### Recordings and durations |
|
|
| Scenarios are well-balanced (33–40 recordings each). Recording length is |
| right-skewed with a long tail of sessions up to ~9 minutes; median 77 s, |
| mean 104 s. |
|
|
| <div align="center"> |
| <img src="https://huggingface.co/datasets/velvet-pine-22/PULSE/resolve/main/assets/recording_stats.png" alt="Recordings per scenario and recording-duration histogram" width="900" /> |
| </div> |
|
|
| ### Per-volunteer modality coverage and label distributions |
|
|
| The matrix below shows the missing-modality structure is **systematic, not |
| random**: early volunteers (`v1`–`v22`) have intermittent MoCap and Pressure |
| coverage; from `v23` onwards almost all five modalities are present on every |
| recording. Bimanual segments dominate (45%), and the manipulated-object |
| distribution has a heavy head (top 8 of 57 objects account for ~50% of all |
| segments). |
|
|
| <div align="center"> |
| <img src="https://huggingface.co/datasets/velvet-pine-22/PULSE/resolve/main/assets/coverage_and_labels.png" alt="Per-volunteer modality matrix; segments by hand; top manipulated objects" width="900" /> |
| </div> |
|
|
| ### Motor-primitive distribution |
|
|
| 7,789 dense segments across 17 motor primitives (taxonomy of 18; `stabilize` |
| is the rarest at 0.3%, `roll` does not appear in the released split). The |
| top three primitives — **grasp / move / place** — together account for 63% of |
| all segments, reflecting the manipulation-heavy nature of daily activities. |
|
|
| <div align="center"> |
| <img src="https://huggingface.co/datasets/velvet-pine-22/PULSE/resolve/main/assets/primitive_distribution.png" alt="Distribution of motor primitives across 7,789 dense action segments" width="800" /> |
| </div> |
|
|
| --- |
|
|
| ## The five modalities |
|
|
| | Modality | Sensor | Channels / dim | Notes | |
| |---|---|---|---| |
| | **MoCap** | Qualisys optical | 56 joints incl. all 10 fingertips · 422 raw / 620 processed dim | Hardware-trigger master clock | |
| | **EMG** | Myo Armband | 8 channels, 20–450 Hz BP | Dominant forearm | |
| | **EyeTrack** | Dikablis Glasses 3 | 24-dim | Binocular numeric features; v1/s1 and v14/s8 are excluded from EyeTrack benchmarks because one eye's channels are unavailable | |
| | **IMU** | 10 wearable units | 160 dim total (acc / gyro / mag / quat per unit) | Body-distributed | |
| | **Pressure** | Fingertip pressure array | 50 channels (25/hand) | Quantitative grip force | |
|
|
| **Auxiliary** — first-person scene-camera video (1280×720, 25 fps). Released videos contain **no audio track and no visible faces**. They remain privacy-sensitive auxiliary material and are **not part of the T1–T6 benchmark protocol**. |
|
|
| **Synchronization** — Qualisys hardware trigger as master; sub-frame (<10 ms) cross-modal residual drift, corrected by post-hoc cross-correlation. |
|
|
| --- |
|
|
| ## Annotations |
|
|
| Two-level scheme produced by a VLM-assisted pipeline with a 15% subset |
| independently re-annotated by a human expert. The validation subset achieves |
| action-primitive κ=0.713, object κ=0.916, hand-label accuracy=0.863, and |
| boundary mean IoU=0.906 (reported in the paper appendix). |
|
|
| - **L1 — scenario label** (one of 8 + S9 motion-primitive protocol) per recording. |
| - **L2 — dense action segments** (mean 2.5 s, 1 s semantic boundary resolution). Each segment carries: |
| - **motor primitive** (taxonomy of 18; 17 observed in this release) |
| - **hand** (left / right / both) |
| - **manipulated object** (scene-specific whitelist; 57 distinct objects observed) |
| - **natural-language description** with **4 paraphrased variants** for language-grounded use |
|
|
| Annotations are released in two complementary forms: |
| - `annotations/v*/s*.json` — one nested JSON per recording (canonical structure, referenced by `croissant.json`). |
| - `annotations_flat/segments.csv` — flat table with one row per segment (used by the Dataset Viewer `action_segments` subset; 7,789 rows × 15 columns). |
|
|
| Summary tables at `metadata/annotations/`. |
|
|
| --- |
|
|
| ## Six benchmark tasks |
|
|
| | | Task | Headline modality use | |
| |---|---|---| |
| | **T1** | Scene recognition (8-way) | All five | |
| | **T2** | Fine-grained action recognition (motor primitive × object × hand) | All five | |
| | **T3** | Grasp onset anticipation | EMG + IMU lead | |
| | **T4** | Missing-modality robustness | Trained on all, evaluated under modality dropout | |
| | **T5** | Tactile-driven grasp state recognition | Pressure-centered sensorimotor windows | |
| | **T6** | Cross-modal pressure prediction | EMG / hand MoCap → pressure | |
|
|
| **DailyActFormer** is provided as a strong T2 baseline: it has the highest mean |
| headline score, but margins over the strongest published baselines are small and |
| confidence intervals overlap. T3 and T6 are intended as open challenge tasks |
| rather than solved benchmarks. Full results, ablations, and the grasp-phase timing analysis are in the |
| accompanying NeurIPS submission (linked here after double-blind review). |
|
|
| --- |
|
|
| ## Train / test splits (subject-independent) |
|
|
| | Split | Train | Test held out | Notes | |
| |---|---|---|---| |
| | **Headline** (T1, T2) | 31 volunteers · 242 recordings · 6,582 segments | `v14, v30, v34, v38, v41` · 40 recordings · 1,207 segments | `v14` contributes only EMG, EyeTrack, IMU | |
| | **Full-modality** (T4) | same training pool | `v3, v25, v26, v27` | Restricted to recordings with full MoCap+EMG+EyeTrack+IMU | |
|
|
| 40 volunteers, IDs `v1`–`v41` with `v7` not assigned. S9 motion-primitive recordings are present for a subset of volunteers and have **no** MoCap or scene-camera video. |
|
|
| --- |
|
|
| ## Repository layout |
|
|
| ```text |
| PULSE/ |
| ├── README.md ← this file |
| ├── DATASHEET.md ← Gebru-style data card |
| ├── LICENSE ← CC BY-NC 4.0 + use restrictions |
| ├── CODE_LICENSE ← MIT, applies to (separately released) helper code |
| ├── croissant.json ← Croissant 1.0 machine-readable card with RAI fields |
| ├── .gitattributes ← LFS rules (do not delete before re-upload) |
| ├── assets/ ← static figures used by this README |
| ├── annotations/ |
| │ └── v*/s*.json ← per-recording nested JSON (canonical) |
| ├── annotations_flat/ |
| │ └── segments.csv ← flat per-segment table (Dataset Viewer subset `action_segments`) |
| ├── data/ |
| │ └── v*/s*/ |
| │ ├── aligned_<modality>_100hz.csv (5 core + 2 Myo aux) |
| │ ├── alignment_metadata.json |
| │ ├── raw/ → opt-in raw Qualisys TSV (~40 MB / file, ~12 GB total) |
| │ └── videos/ → opt-in scene-cam + eye-tracking MP4 (~200 MB / pair) |
| ├── metadata/ |
| │ ├── modality_coverage.xlsx |
| │ ├── batch_alignment_summary.json |
| │ └── annotations/ |
| │ ├── _run_summary.json |
| │ ├── segment_counts.{csv,xlsx} |
| │ └── logs/ |
| └── docs/ |
| └── upload_to_huggingface.md |
| ``` |
|
|
| --- |
|
|
| ## Ethics, privacy, and intended use |
|
|
| - IRB / ethics committee approval obtained from the authors' institution (institution name withheld for double-blind review). |
| - Written informed consent + separate media-release agreement for the scene-cam and eye-tracking videos; released videos contain no audio track and no visible faces. |
| - The release **does not include** participant demographic attributes (sex, age, height, weight) collected for screening. |
| - **Use restrictions:** see `LICENSE`. Surveillance, employee monitoring, and biometric re-identification applications without freely given consent are **explicitly disallowed**. |
| - Downstream use of biometric-class modalities (EMG, eye tracking, pressure) for inference of sensitive attributes (fatigue, stress, medical conditions) on new subjects requires the deploying institution's own ethics review. |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{anonymous2026pulse, |
| title = {PULSE: A Synchronized Five-Modality Dataset for Multi-Modal Daily Activity Understanding}, |
| author = {Anonymous Authors}, |
| booktitle = {Submitted to NeurIPS 2026 Evaluations and Datasets Track}, |
| year = {2026}, |
| note = {Under double-blind review} |
| } |
| ``` |
|
|
