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Datasets:
velvet-pine-22
/
PULSE-sample

Tasks:
Time Series Forecasting
Video Classification
Other
Modalities:
Video
Languages:
English
Size:
100M<n<1B
Tags:
multi-modal
daily-activity
wearable-sensors
motion-capture
electromyography
eye-tracking
License:
Dataset card Data Studio Files
xet
 Community
PULSE-sample
File size: 4,685 Bytes
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---
license: cc-by-nc-4.0
language:
  - en
pretty_name: PULSE-sample
size_categories:
  - 100M<n<1B
task_categories:
  - time-series-forecasting
  - video-classification
  - other
tags:
  - multi-modal
  - daily-activity
  - wearable-sensors
  - motion-capture
  - electromyography
  - eye-tracking
  - inertial-measurement-unit
  - tactile-sensing
  - sample
---

# PULSE-sample — Representative Single-Recording Subset

This is a small representative subset of the **PULSE** dataset, hosted as a separate
Hugging Face repository so that NeurIPS 2026 reviewers can inspect data quality and
schema without downloading the full 85 GB release.

> **Full dataset:** [`velvet-pine-22/PULSE`](https://huggingface.co/datasets/velvet-pine-22/PULSE)

## What this sample contains (~284 MB total)

A single complete recording — **`v1/s1`** (volunteer 1, scenario S1 "Office desk
organization", ~101 s) — with **all five non-visual sensor modalities** plus the
synchronized scene-camera video, action-segment annotations, and the global metadata
files needed to interpret everything.

```
PULSE-sample/
├── data/v1/s1/
│   ├── aligned_emg_100hz.csv             # 8-channel surface EMG @ 100 Hz
│   ├── aligned_eyetrack_100hz.csv        # 24-dim binocular gaze @ 100 Hz
│   ├── aligned_imu_100hz.csv             # 160-dim wearable IMU @ 100 Hz
│   ├── aligned_mocap_100hz.csv           # 56-joint optical motion capture @ 100 Hz
│   ├── aligned_pressure_100hz.csv        # 50-channel fingertip pressure @ 100 Hz
│   ├── aligned_myo_pose_100hz.csv        # forearm pose (auxiliary)
│   ├── aligned_myo_quat_100hz.csv        # forearm orientation (auxiliary)
│   ├── alignment_metadata.json           # per-recording sync diagnostics
│   ├── raw/                              # raw Qualisys MoCap stream (.tsv)
│   └── videos/                           # scene-cam + gaze-overlay (.mp4, 25 fps)
├── annotations/v1/s1.json                # dense segment annotations (action / hand / object / text)
├── annotations_flat/segments.csv         # the 30 segments of v1/s1, flattened
├── metadata/recordings.csv               # full 337-row recording manifest
├── metadata/modality_coverage.xlsx       # per-recording modality availability
├── LICENSE                               # CC BY-NC 4.0 (data)
└── CODE_LICENSE                          # MIT (code in companion repo)
```

## How to use this sample

```python
import pandas as pd

# Load all five modalities for the single recording
ROOT = "data/v1/s1"
emg      = pd.read_csv(f"{ROOT}/aligned_emg_100hz.csv")
eyetrack = pd.read_csv(f"{ROOT}/aligned_eyetrack_100hz.csv")
imu      = pd.read_csv(f"{ROOT}/aligned_imu_100hz.csv")
mocap    = pd.read_csv(f"{ROOT}/aligned_mocap_100hz.csv")
pressure = pd.read_csv(f"{ROOT}/aligned_pressure_100hz.csv")
print(f"Aligned shapes (T, D): {[x.shape for x in [emg, eyetrack, imu, mocap, pressure]]}")

# Load the dense segment annotations
import json
with open("annotations/v1/s1.json") as f: ann = json.load(f)
print(f"{len(ann['segments'])} action segments")
```

All time series are sub-frame aligned (<10 ms) on a shared 100 Hz timebase. The first
sample of every modality file corresponds to t = 0 of the trimmed scene-cam video;
total length matches `metadata/recordings.csv` row `v1s1` (`duration_sec`,
`n_samples_100hz`).

## How this sample was created

Selected by the dataset authors as a representative recording: scenario S1 "office
desk organization" was chosen because it contains a typical mix of grasp / move /
place / adjust primitives without unusually short or long sub-tasks; v1 was chosen
because it has all five modalities present and full-length scene-cam video.

The full 337-row `metadata/recordings.csv` is included so reviewers can see exactly
where this recording sits in the train/test split scheme and which other recordings
exist; the global Croissant metadata is on the main repo.

## License & attribution

Data is released under **CC BY-NC 4.0**. By accessing PULSE-sample you agree to the
license, including the prohibition on commercial redeployment, re-identification, and
worker-surveillance applications. See `LICENSE` for the full terms. Companion code is
released under MIT (see `CODE_LICENSE`).

## 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}
}
```