PULSE

A Synchronized Five-Modality Dataset for Multi-Modal Daily Activity Understanding

MoCap · EMG · Eye Tracking · IMU · Fingertip Pressure — all hardware-synced at 100 Hz

NeurIPS 2026 Datasets & Benchmarks  ·  under double-blind review  ·  CC BY-NC 4.0

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At a glance

40	9	5	7,768
volunteers	scenarios (S1–S8 + S9 motion primitives)	modalities @ 100 Hz	dense action segments
337 recordings	~9.7 h total (S1–S8: ~7.0 h)	<10 ms cross-modal drift	5 benchmark tasks
283 annotated	36 annotated volunteers	17 motor primitives observed	57 unique objects

Status — anonymous review release. Author names, institution, and the permanent dataset URL will be revealed in the camera-ready version.

Tip. The Dataset Viewer at the top of this page is fully interactive — switch between the emg / imu / mocap / eyetrack / pressure / action_segments subsets via the dropdown, and hover any column to see its per-value distribution. The action_segments subset is the flat per-segment table (7,768 rows): hover the primitive, hand, or object columns to browse the label distributions interactively. The static figures further down summarise cross-tab statistics (per-modality coverage, per-volunteer matrix) the column-wise viewer cannot compute.

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Quick start

Load any single modality directly through datasets:

from datasets import load_dataset

emg = load_dataset("lllwwwlll/PULSE", "emg", split="train")
# Available configs: emg, imu, mocap, eyetrack, pressure, action_segments.
# Each per-modality CSV has a consistent schema across volunteers.
# (volunteer, scenario) is recovered from the source file path; the
# per-modality CSVs themselves do not carry id columns.

# Flat action-segments table (also browsable in the Dataset Viewer above):
seg = load_dataset("lllwwwlll/PULSE", "action_segments", split="train")
# 7,768 rows × 15 columns: volunteer, scenario, primitive, hand, object, ...

Inspect one recording on disk:

data/v1/s1/
├── aligned_emg_100hz.csv
├── aligned_eyetrack_100hz.csv
├── aligned_imu_100hz.csv
├── aligned_mocap_100hz.csv
├── aligned_pressure_100hz.csv
├── aligned_myo_pose_100hz.csv     # auxiliary
├── aligned_myo_quat_100hz.csv     # auxiliary
├── alignment_metadata.json
├── raw/                           # opt-in: original Qualisys TSV (~40 MB)
│   └── aligned_v1s1_s_Q.tsv
└── videos/                        # opt-in: scene cam + eye-tracking video (~200 MB, contains PII)
    ├── trimmed_v1s1_*Scene Cam.mp4
    └── trimmed_v1s1_*Eye Tracking Video.mp4

Want only the benchmark streams? Skip the heavy bits at download:

hf download lllwwwlll/PULSE --repo-type dataset \
    --exclude 'data/v*/s*/raw/**' \
    --exclude 'data/v*/s*/videos/**'
# ~10 GB instead of ~86 GB

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Dataset statistics

Modality availability

Realistic missing-modality pattern: EMG, IMU, EyeTrack, and Scene Cam are present on >90% of recordings; MoCap (67.7%) and Pressure (68.8%) are the two limiting modalities. All five sensors are simultaneously available on 179 / 337 recordings (53.1%) — drives the T4 missing-modality benchmark.

Recordings and durations

Scenarios are well-balanced (33–40 recordings each). Recording length is right-skewed with a long tail of sessions up to ~9 minutes; median 77 s, mean 104 s.

Per-volunteer modality coverage and label distributions

The matrix below shows the missing-modality structure is systematic, not random: early volunteers (v1–v22) have intermittent MoCap and Pressure coverage; from v23 onwards almost all five modalities are present on every recording. Bimanual segments dominate (45%), and the manipulated-object distribution has a heavy head (top 8 of 57 objects account for ~50% of all segments).

Motor-primitive distribution

7,768 dense segments across 17 motor primitives (taxonomy of 18; stabilize is the rarest at 0.3%, roll does not appear in the released split). The top three primitives — grasp / move / place — together account for 63% of all segments, reflecting the manipulation-heavy nature of daily activities.

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The five modalities

Modality	Sensor	Channels / dim	Notes
MoCap	Qualisys optical	56 joints incl. all 10 fingertips · 422 raw / 620 processed dim	Hardware-trigger master clock
EMG	Myo Armband	8 channels, 20–450 Hz BP	Dominant forearm
EyeTrack	Dikablis Glasses 3	24-dim (14-dim benchmark subset)	Binocular numeric features
IMU	10 wearable units	160 dim total (acc / gyro / mag / quat per unit)	Body-distributed
Pressure	Fingertip pressure array	50 channels (25/hand)	Quantitative grip force

Auxiliary — first-person scene-camera video (1280×720, 25 fps). Contains PII (face, voice). Released under the same license but not part of the headline T1–T4 benchmark.

Synchronization — Qualisys hardware trigger as master; sub-frame (<10 ms) cross-modal residual drift, corrected by post-hoc cross-correlation.

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Annotations

Two-level scheme produced by a VLM-assisted pipeline with a 15% subset re-annotated by two human experts (agreement reported in paper).

• L1 — scenario label (one of 8 + S9 motion-primitive protocol) per recording.
• L2 — dense action segments (mean 1.7 s, 1 s boundary resolution). Each segment carries:
  • motor primitive (taxonomy of 18; 17 observed in this release)
  • hand (left / right / both)
  • manipulated object (scene-specific whitelist; 57 distinct objects observed)
  • natural-language description with 4 paraphrased variants for language-grounded use

Annotations are released in two complementary forms:

• annotations/v*/s*.json — one nested JSON per recording (canonical structure, referenced by croissant.json).
• annotations_flat/segments.csv — flat table with one row per segment (used by the Dataset Viewer action_segments subset; 7,768 rows × 15 columns).

Summary tables at metadata/annotations/.

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Five benchmark tasks

	Task	Headline modality use
T1	Scene recognition (8-way)	All five
T2	Fine-grained action recognition (motor primitive × object × hand)	All five
T3	Grasp onset anticipation	EMG + IMU lead
T4	Missing-modality robustness	Trained on all, evaluated under modality dropout
T5	Tactile-driven sub-second motion forecasting	Pressure → MoCap

The proposed DailyActFormer baseline establishes the leading result on T2. Full results, ablations, and the grasp-phase timing analysis are in the accompanying NeurIPS submission (linked here after double-blind review).

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Train / test splits (subject-independent)

Split	Train	Test held out	Notes
Headline (T1, T2)	31 volunteers · 242 recordings · 6,561 segments	v14, v30, v34, v38, v41 · 40 recordings · 1,207 segments	v14 contributes only EMG, EyeTrack, IMU
Full-modality (T4)	same training pool	v3, v25, v26, v27	Restricted to recordings with full MoCap+EMG+EyeTrack+IMU

40 volunteers, IDs v1–v41 with v7 not assigned. S9 motion-primitive recordings are present for a subset of volunteers and have no MoCap or scene-camera video.

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Repository layout

PULSE/
├── README.md            ← this file
├── DATASHEET.md         ← Gebru-style data card
├── LICENSE              ← CC BY-NC 4.0 + use restrictions
├── CODE_LICENSE         ← MIT, applies to (separately released) helper code
├── croissant.json       ← Croissant 1.0 machine-readable card with RAI fields
├── .gitattributes       ← LFS rules (do not delete before re-upload)
├── assets/              ← static figures used by this README
├── annotations/
│   └── v*/s*.json                    ← per-recording nested JSON (canonical)
├── annotations_flat/
│   └── segments.csv                  ← flat per-segment table (Dataset Viewer subset `action_segments`)
├── data/
│   └── v*/s*/
│       ├── aligned_<modality>_100hz.csv  (5 core + 2 Myo aux)
│       ├── alignment_metadata.json
│       ├── raw/      → opt-in raw Qualisys TSV (~40 MB / file, ~12 GB total)
│       └── videos/   → opt-in scene-cam + eye-tracking MP4 (~200 MB / pair)
├── metadata/
│   ├── modality_coverage.xlsx
│   ├── batch_alignment_summary.json
│   └── annotations/
│       ├── _run_summary.json
│       ├── segment_counts.{csv,xlsx}
│       └── logs/
└── docs/
    └── upload_to_huggingface.md

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Ethics, privacy, and intended use

• IRB / ethics committee approval obtained from the authors' institution (institution name withheld for double-blind review).
• Written informed consent + separate media-release agreement for the scene-cam video and audio streams.
• The release does not include participant demographic attributes (sex, age, height, weight) collected for screening.
• Use restrictions: see LICENSE. Surveillance, employee monitoring, and biometric re-identification applications without freely given consent are explicitly disallowed.
• Downstream use of biometric-class modalities (EMG, eye tracking, pressure) for inference of sensitive attributes (fatigue, stress, medical conditions) on new subjects requires the deploying institution's own ethics review.

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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}
}