A Multi-Modal Dataset for Ground Reaction Force Estimation Using Consumer Wearable Sensors
2026-04-01T08:51:38Z•5d34d19f8aa36d9988a655c4cdfaaf2970d7ce096eab0e49a5ee397fe7fdc150
6GApple WatchCC BY 4.0CL-KLFRISIMUSA-cGANTransformerXL-MIMOactive vibrationdatasetforce platehybrid compressionliquid identificationnear-field channel estimationopen datapeak power forecasting','Temporal Fusion Transformer','AFDM','EPreconfigurable intelligent surfacessemantic communicationsemantic sensingsignal processingsmartphone sensingvGRFviscosity estimationwearable sensors
What happened
ArXiv eess.sp RSS feed (2026-04-01) listing multiple new research contributions across sensing, communications, and signal processing. Highlights include: an open multi-modal dataset for estimating vertical ground reaction force (vGRF) from Apple Watch IMUs with force-plate ground truth (CC BY 4.0) enabling reproducible wearable biomechanics research; a smartphone-based active vibration method to identify liquids via viscosity estimation; a compressed-covariance near-field channel estimator (Curvature-Learning KL, CL-KL) for hybrid XL-MIMO front-ends; exact statistical characterization and性能分析
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_eess_sp
- Record identifier
- 5d34d19f8aa36d9988a655c4cdfaaf2970d7ce096eab0e49a5ee397fe7fdc150
- Enrichment time
- 2026-04-01T08:51:38Z
- AI-assisted enrichment
- Yes
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