| --- |
| 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} |
| } |
| ``` |
|
|
