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BadmintonGRF dataset enables markerless ground reaction force estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced BadmintonGRF, a new multimodal dataset designed for estimating ground reaction forces in badminton without markers. The dataset pairs synchronized multi-view video with data from force plates and motion capture systems. It includes a benchmark task that maps 2D pose to GRF, along with preprocessing code and baseline models to facilitate research in this area. AI

IMPACT Provides a new dataset and benchmark for markerless estimation of ground reaction forces, potentially advancing research in sports biomechanics and AI-driven performance analysis.

RANK_REASON This is a research paper describing a new dataset and benchmark for a specific scientific task. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

BadmintonGRF dataset enables markerless ground reaction force estimation

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This is a research paper describing a new dataset and benchmark for a specific scientific task. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Kuoye Niu, Jianwei Li, Shengze Cai, Yong Ma, Mengyao Jia, Lishun Shen, Zhenheng Zhang, Yuxin Peng, Xian Song ·

    BadmintonGRF: A Multimodal Dataset and Benchmark for Markerless Ground Reaction Force Estimation in Badminton

    arXiv:2605.01876v1 Announce Type: new Abstract: Multimodal resources for non-periodic court sports with laboratory-grade sensing remain scarce: few publicly pair instrumented ground reaction force (GRF) with high-frame-rate multi-view video, limiting markerless load estimation in…

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