# Datasheet for PULSE Following the *Datasheets for Datasets* template (Gebru et al., 2021). Refer to `croissant.json` for the machine-readable equivalent. --- ## Motivation **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.