PULSE: A Synchronized Five-Modality Dataset for Sensorimotor Coordination in Long-Horizon Daily Activities

摘要

Existing human-activity datasets typically cover only one or two sensor channels and often focus on short, isolated actions, leaving long-duration compositional tasks largely unsupported as benchmarks for temporal cross-modal sensorimotor learning. We introduce PULSE ($\mathbf{P}$hysiological $\mathbf{U}$nified $\mathbf{L}$ong-duration $\mathbf{S}$ynchronized $\mathbf{E}$mbodiment), a synchronized five-modality dataset that enables benchmarking temporal cross-modal sensorimotor learning: how models recognize ongoing activities, anticipate future contact, reconstruct unobserved signals, and remain robust when physiological, motion, gaze, inertial, and tactile channels are partially observed. Collected from 40 volunteers across 8 ecologically valid daily-activity scenarios together with a dedicated motion-primitive collection, PULSE records full-body optical motion capture with finger-level hand articulation, surface EMG, binocular eye tracking, wearable IMU, and a fingertip pressure array recording quantitative grip force, all sampled at 100 Hz. Each scenario is a long-horizon compositional task containing many smaller sub-tasks, yielding over 7,700 annotated action segments. Each segment is tagged with a motor primitive, the hand involved, the manipulated object, and a natural-language description with four paraphrased variants. On top of the dataset, we define multiple benchmark tasks for temporal cross-modal sensorimotor learning: scene and fine-grained action recognition, grasp onset anticipation, missing-modality robustness, tactile-driven sub-second grasp-state prediction, cross-modal pressure reconstruction, etc. Together these tasks evaluate how information transfers across physiology, motion, gaze, and touch, rather than reducing the dataset to an activity-label catalog. We evaluate three backbone architectures, nine fusion strategies, seven published baselines, and task-specific models including SyncFuse and DailyActFormer. Detailed task definitions and results are in the Appendices. Data, annotations, and baseline code will be publicly released.