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: lllwwwlll/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

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

@inproceedings{anonymous2026pulse,
  title     = {PULSE: A Synchronized Five-Modality Dataset for Multi-Modal Daily Activity Understanding},
  author    = {Anonymous Authors},
  booktitle = {Submitted to NeurIPS 2026 Datasets and Benchmarks Track},
  year      = {2026},
  note      = {Under double-blind review}
}