Abstract
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.
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Data availability
The dataset is hosted on Figshare40 under a CC-BY 4.0 license and is available at https://doi.org/10.6084/m9.figshare.31030252.
Code availability
A code repository at https://github.com/RogerPallares/reaching-dataset provides Python and MATLAB scripts for computing derived metrics from measured data and tutorials for loading and visualizing the dataset. The repository includes Python (version 3.10) scripts that demonstrate how to compute derived metrics provided in the dataset. Specifically, the scripts show how to compute: (i) arm speed profiles and the onsets and offsets of reaching-cycle events from motion capture data, (ii) tremor power and tremor onsets and offsets from accelerometer data, and (iii) RMS EMG amplitude envelopes from raw EMG signals. The repository also includes two tutorials—one in Python (version 3.10) and one in MATLAB (version R2024b)—that show how to load the HDF5 dataset, navigate its structure, and visualize time series and event data. The tutorials demonstrate how to overlay tracked points on ultrasound video frames and save the annotated video. Additionally, the scripts used to time-synchronize the different data modalities rely on our Python library (pysampled) and they will be made available upon reasonable request.
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Acknowledgements
This work was carried out in part through the use of MIT.nano Immersion Lab’s facilities. We thank Armin Kappacher for sharing his insights related to soft tissue movements with PN and LD through dance lessons. We thank Mariia Smyk, Andrea Leang, Kelly Wu, and Isabel Waitz for assistance with data collection and annotation. We thank Vincent Chen and Charles Williams for assistance with data annotation. We thank Micaela Amaral for assistance with figure illustrations. We thank all the participants for volunteering their time. Professional editing services were not used in the preparation of this manuscript. Large language models were used to make minor improvements to the writing and were not used to generate any of the ideas presented in this work. Funding - Sekisui House. NCSOFT. Fulbright U.S. Student Program and Fulbright Commission Portugal. European Health and Digital Executive Agency (ID 101136376). Foundation for Science and Technology (Grant No. UID/04923). “la Caixa” Foundation (ID 100010434) fellowship LCF/BQ/EU22/11930097. National Science Foundation Graduate Research Fellowship (Grant No. 2141064). MIT Bose Fellows Program.
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Conceptualization: P.N. Investigation: R.P.L., U.M.S., E.R., P.N. Data curation: R.P.L., D.F., U.M.S., P.N. Software: R.P.L., D.F., M.F.A., P.N. Visualization: R.P.L., D.F., J.R., P.N. Funding acquisition: R.P.L., D.F., J.R., H.G., L.D., B.A., P.N. Project administration: H.G., L.D., B.A., P.N. Supervision: P.N. Writing – original draft: R.P.L., P.N. Writing – review & editing: R.P.L., D.F., J.R., P.N. All authors reviewed the manuscript.
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Pallarès-López, R., Folgado, D., Magana-Salgado, U. et al. A multimodal biomechanics dataset with synchronized kinematics and internal tissue motions during reaching. Sci Data (2026). https://doi.org/10.1038/s41597-026-07019-3
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DOI: https://doi.org/10.1038/s41597-026-07019-3
