README.md

---
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` &nbsp;·&nbsp; `under double-blind review` &nbsp;·&nbsp; `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}
}
```