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<p>Tissue motions within body segments, such as the relative movements of muscles, fascia, and bone, remain largely unexplored despite their relevance to movement dysfunction, force transmission, and motor skill. Here, we present a time-synchronized multimodal dataset that bridges this gap by capturing both internal tissue dynamics and conventional biomechanical measurements during arm reaching. Thirty-six participants across three expertise levels (world-class athletes, regional athletes, and untrained individuals) performed slow, rhythmic reaching movements while we recorded data using B-mode ultrasound imaging, motion capture, electromyography, and accelerometry. The dataset includes processed signals, derived parameters (segmented reach events, tissue boundary motion, arm kinematics, tremor events, and&#xa0;muscle activation levels), and metadata. Notably, using the DUSTrack point-tracking workflow, we provide trajectories for 11 points across approximately 300,000 ultrasound frames from the upper arm. This resource enables at least three primary applications: (1) supervised training and benchmarking of deep learning models for point tracking in ultrasound videos, (2) development of ultrasound-based metrics for characterizing soft tissue mechanics, and (3) biomechanical investigation of how tissue-level dynamics support motor performance. All data, processing code, and tutorials are provided in accessible formats with documentation.</p>

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A multimodal biomechanics dataset with synchronized kinematics and internal tissue motions during reaching

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

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  • Roger Pallarès-López,
  • Duarte Folgado,
  • Uriel Magana-Salgado,
  • Jessica Rosendorf,
  • Enya Ryu,
  • Micha Feigin-Almon,
  • Hugo Gamboa,
  • Luca Daniel,
  • Brian W. Anthony,
  • Praneeth Namburi

摘要

Tissue motions within body segments, such as the relative movements of muscles, fascia, and bone, remain largely unexplored despite their relevance to movement dysfunction, force transmission, and motor skill. Here, we present a time-synchronized multimodal dataset that bridges this gap by capturing both internal tissue dynamics and conventional biomechanical measurements during arm reaching. Thirty-six participants across three expertise levels (world-class athletes, regional athletes, and untrained individuals) performed slow, rhythmic reaching movements while we recorded data using B-mode ultrasound imaging, motion capture, electromyography, and accelerometry. The dataset includes processed signals, derived parameters (segmented reach events, tissue boundary motion, arm kinematics, tremor events, and muscle activation levels), and metadata. Notably, using the DUSTrack point-tracking workflow, we provide trajectories for 11 points across approximately 300,000 ultrasound frames from the upper arm. This resource enables at least three primary applications: (1) supervised training and benchmarking of deep learning models for point tracking in ultrasound videos, (2) development of ultrasound-based metrics for characterizing soft tissue mechanics, and (3) biomechanical investigation of how tissue-level dynamics support motor performance. All data, processing code, and tutorials are provided in accessible formats with documentation.