Datasets:

velvet-pine-22
/

PULSE

For what purpose was the dataset created? To enable research on long-horizon, multi-modal daily activity understanding where complementary sensor channels (whole-body motion, muscle activation, gaze, limb kinematics, contact forces) interact during compositional tasks. Existing datasets typically cover only one or two of these channels and focus on short isolated actions; PULSE is designed to fill that gap with hardware-time- synchronized 100 Hz recordings.

Who created the dataset? Anonymous Authors (NeurIPS 2026 Evaluations & Datasets submission, double-blind).

Who funded the creation of the dataset? Not disclosed at submission time.

Composition

What do the instances represent? Each instance is one recording = one execution of one of 8 daily-activity scenarios by one of 40 volunteers. A recording contains up to 5 time-synchronized sensor streams plus annotations.

How many instances are there in total?

304 task recordings (S1–S8), totaling ~7.0 hours
33 isolated motion-primitive recordings (S9), totaling ~2.9 hours
282 of the 304 task recordings (from 36 volunteers) carry dense action-segment annotations totaling 7,789 segments

Does the dataset contain all possible instances or is it a sample? Sample. 40 volunteers were recruited from a single university campus; not every volunteer completed every scenario, and equipment availability / sensor dropout caused some recordings to lack one or more modalities. This is documented in modality_coverage.xlsx and batch_alignment_summary.json.

What data does each instance consist of? Raw and aligned features at 100 Hz:

MoCap: 422-dim raw → 620-dim processed (hip-relative + per-joint velocity)
EMG: 8-channel surface EMG (band-pass 20–450 Hz, rectified)
EyeTrack: 24-dim core numeric features; v1/s1 and v14/s8 have one-eye channels unavailable and are excluded from EyeTrack benchmarks. The raw eye-image video is not included in the benchmark, only the scene-cam
IMU: 160-dim from 10 wearable units (accel + gyro + mag + orientation quaternion per unit)
Pressure: 50-channel fingertip pressure (25 per hand)
Auxiliary: scene-cam video (1280×720, 25 fps, MP4)

Per-segment annotation: start/end time (1 s resolution), motor primitive (taxonomy of 18), hand (left/right/both), object (scene-specific whitelist), description + 4 paraphrases.

Is there a label or target associated with each instance? Yes — at two levels: (L1) one of 8 scene labels per recording; (L2) dense action segments per recording.

Is any information missing from individual instances? Yes. EMG ≈99.3%, IMU ≈98.4%, EyeTrack ≈92.8%, MoCap ≈80.9%, Pressure ≈69.7%. All five simultaneously available for ~65.1% of task recordings (198/304). 22 recordings lack dense annotations and are released as raw aligned streams.

Are there recommended data splits? Yes — two subject-independent splits:

Headline split (T1 scene + T2 action): 31 train / 5 test (v14, v30, v34, v38, v41)
Full-modality split (T4 missing-modality robustness): same train pool / 4 test (v3, v25, v26, v27)

Are there errors, sources of noise, or redundancies in the dataset? Action-segment boundaries are at 1 s semantic resolution. Annotation produced by a VLM-assisted pipeline; ~15% independently re-annotated by a human expert (action-primitive κ=0.713, object κ=0.916, hand-label accuracy=0.863, boundary mean IoU=0.906; reported in the paper appendix). v14 lacks MoCap and pressure for all sessions. Two recordings (v1/s1 and v14/s8) have one eye's pupil-size and event channels unavailable and are excluded from EyeTrack-based benchmark training and evaluation.

Is the dataset self-contained? Yes. No external links or third-party content are required to use the released data.

Collection process

How was the data acquired? In an instrumented laboratory furnished to resemble common real-world environments (office desk, kitchen counter, dining table, wardrobe area, packing station). Volunteers performed each scenario in their own style and at their own pace after a brief verbal description of the task.

What mechanisms or procedures were used to collect the data?

Qualisys optical motion-capture system (master clock, hardware trigger)
Myo Armband (8-channel surface EMG)
Dikablis Glasses 3 (binocular eye tracking + scene-cam)
10 wearable IMUs
Custom 50-channel fingertip pressure array

Over what time-frame was the data collected? 2025–2026.

Were the individuals from whom the data was collected notified about the data collection? Yes. Each participant received a written informed-consent document covering the study purpose, procedure, risks, and data-use terms before each session.

Did the individuals consent to the collection and use of their data? Yes — written informed consent, plus a separate written media-release agreement explicitly authorizing release of synchronized scene-camera and eye-tracking videos. The released videos contain no audio track and no visible faces.

If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future? Yes — participants were informed they could pause or withdraw at any point; participants who withdrew received the full session compensation. Post-release withdrawal: corresponding author contact (to be disclosed in the camera-ready) handles takedown requests.

Has an analysis of the potential impact of the dataset and its use on data subjects been conducted? Yes — see Section "Broader Impact" of the paper. The released videos contain no audio track and no visible faces, but first-person video remains privacy-sensitive auxiliary material. Biometric-attribute inference risks are addressed through the license and use restrictions.

Preprocessing / cleaning / labeling

Was any preprocessing/cleaning/labeling of the data done? Yes:

Hardware synchronization at session start; residual drift corrected via inertial × MoCap cross-correlation (<10 ms)
All streams resampled to 100 Hz (anti-aliased decimation)
MoCap → hip-relative coordinates + appended per-joint velocity (422 → 620 dim)
EMG band-pass filtered (20–450 Hz) and rectified
EyeTrack kept as the full 24-dim numeric feature vector; v1/s1 and v14/s8 are excluded from EyeTrack-based benchmark training and evaluation
For benchmark modeling: further downsampled to 20 Hz; per-channel z-score normalization using training-split statistics only
Variable-length sequences zero-padded and masked at batch level
Action-segment annotation via VLM-assisted pipeline with scene-constrained prompt; 15% independently re-annotated by a human expert

Was the "raw" data saved in addition to the preprocessed/cleaned data? The released aligned data are at 100 Hz with the preprocessing above already applied. The original per-modality manufacturer outputs (Qualisys TSV, Myo SDK, Dikablis exports) are retained internally but not part of the public release.

Uses

Has the dataset been used for any tasks already? Yes — six benchmark tasks (T1–T6) defined in the accompanying paper, evaluated with a baseline suite covering three backbone architectures, nine fusion strategies, seven published baselines, SyncFuse, and DailyActFormer. These results should be read as baselines and diagnostics for open challenges rather than as a primary model contribution.

Is there a repository that links to any or all papers or systems that use the dataset? Initially the HuggingFace dataset page; a full citation list will be maintained post-release.

What (other) tasks could the dataset be used for? Long-horizon multi-modal action segmentation, sub-task anticipation, hierarchical imitation learning, sensorimotor coordination analysis, myoelectric prosthetic control, fingertip-pressure-conditioned dexterous manipulation, ergonomic/cognitive workplace studies.

Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?

Single-site cohort limits demographic generalization
25 fps scene-cam cannot recover the sub-frame anticipatory coordination signals visible at 100 Hz — vision-only models will miss the dataset's distinguishing temporal signal
1 s annotation boundary resolution
Downstream users of biometric-class modalities for sensitive-attribute inference must obtain their own ethics review

Are there tasks for which the dataset should not be used? Yes — covert worker surveillance, employee monitoring, biometric re-identification, or any application without freely given and revocable consent of the new subjects. See LICENSE for the binding restrictions.

Distribution

Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? Yes — public release on HuggingFace Datasets (URL pending; anonymized mirror used for review).

How will the dataset be distributed? HuggingFace Datasets repository with Git LFS, plus a Croissant 1.0 metadata file.

When will the dataset be distributed? Anonymous review release at submission time; permanent public release upon acceptance.

Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? CC BY-NC 4.0 (LICENSE) with additional use restrictions covering re-identification and surveillance applications. Code is released under MIT (CODE_LICENSE).

Have any third parties imposed IP-based or other restrictions on the data associated with the instances? No.

Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? None known.

Maintenance

Who will be supporting/hosting/maintaining the dataset? The authors (institution to be disclosed in the camera-ready). HuggingFace Datasets provides the hosting infrastructure.

How can the owner/curator/manager of the dataset be contacted? Corresponding author email — to be disclosed in the camera-ready. During review, contact via the OpenReview submission system.

Is there an erratum? Will be maintained as a section of the HuggingFace dataset page after release.

Will the dataset be updated? Bug-fix patches will be issued as new minor versions with a changelog. Major schema changes will be issued as new major versions.

If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances? No fixed retention limit beyond what the participant media-release agreement allows. Withdrawal requests handled by the corresponding author.

Will older versions of the dataset continue to be supported/hosted/maintained? Yes — older versions remain available as Git LFS history for reproducibility.

If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? Yes — pull requests / discussions on the HuggingFace Datasets page after release.