DOI: 10.6084/m9.figshare.31030252
title: A Multimodal Biomechanics Dataset with Synchronized Kinematics and Internal Tissue Motions during Reaching

description: <b>Overview</b>This dataset provides multimodal, time-synchronized measurements collected during slow, rhythmic arm reaching. It bridges internal soft-tissue motion and conventional biomechanical measurements by combining B-mode ultrasound imaging of the upper arm with optical motion capture, surface electromyography, and tri-axial accelerometry.Data were collected from 36 healthy adult participants across three expertise levels (expert, intermediate, non-expert). Participants performed unconstrained reaching cycles paced by a visual metronome (1 cycle every 6 s) under two hand-orientation conditions (“give”/palm up; “touch”/palm down). All modalities are time-aligned, and the dataset includes processed signals, derived parameters, event annotations, and metadata to support both biomechanics and motor control analyses, as well as machine learning (ML) work on ultrasound tracking.<b>Key features include:</b>Synchronized motion capture (upper-limb marker trajectories), ultrasound videos, EMG and accelerometry (palm, biceps, triceps), and derived measures (e.g., reach-cycle events, arm kinematics/speed, tremor events/power, EMG amplitude)B-mode ultrasound videos (60 fps) capturing a transverse view of the triceps/brachialis region during reaching.Frame-level ultrasound point trajectories for 11 tracked points (including humerus, muscle boundary, and within muscle points) spanning ~300,000 frames across the dataset.<b>Citation.</b> Please visit the associated dataset descriptor paper for full details.<br>[citation]<br><b>Ethics & de-identification. </b>Data are de-identified and shared publicly under written informed consent and MIT COUHES approval (Protocol #2201000537).<b>Contents</b>The dataset is organized as follows:<b>README.txt</b>: high-level documentation and pointers.<b>dataset.csv</b>: one row per participant with demographics/anthropometrics, expertise level, experimental configuration, and paths to the corresponding data files.<b>SHA256SUMS.txt</b>: checksum manifest for integrity verification.<b>data.zip archive</b> (download to obtain all data files in a preserved folder structure), containing:<code>hdf5_files/</code>: 36 (1 per participant) <code>.h5</code> files with synchronized multimodal timeseries, derived signals, event annotations, and metadata.<code>us_videos/</code>: 36 (1 per participant) ultrasound <code>.mp4</code> videos (60 fps).<code>hdf5_structure.txt</code>: description of the HDF5 layout (groups/datasets/attributes).<code>exceptions.txt</code>: participant-specific notes/exceptions (e.g., missing channels or irregularities).<b>HDF5 organization (high-level).</b> Each per-participant HDF5 file follows a consistent structure with (i) a <code>metadata</code> group, (ii) an <code>events</code> group (trial/cycle/tremor annotations), and (iii) a <code>timeseries</code> group containing synchronized sensor streams and derived signals, including subgroups with accelerometry, motion capture, electromyography, ultrasound tracker trajectories, tremor-related measures, arm kinematics/speed, and EMG amplitude. See <code>hdf5_structure.txt</code> for details. For the complete definition of every recorded and derived variable (names, descriptions, units, and array shapes), see the manuscript Supplementary Materials, Section HDF5 File Description.<br><b>How to use</b><b>Start with the dataset.csv</b><b>.</b> Each row corresponds to one participant and includes file paths to the participant’s HDF5 file and ultrasound video.<b>Download and unzip the data.zip archive.</b> The archive preserves the folder structure for <code>hdf5_files/</code> and <code>us_videos/</code>.<b>Read exceptions.txt</b> before running analyses across all participants (it lists known participant-specific exceptions/notes).<b>Load data using the code repository.</b> Tutorials for loading/visualizing the data and reproducing derived measurements are provided in a companion GitHub repository: https://github.com/RogerPallares/reaching-dataset.<b>Potential use cases</b>Supervised training/benchmarking of ultrasound point-tracking models using the provided tracked point trajectories.Development of ultrasound-based metrics for characterizing soft-tissue motion during movement.Biomechanical and motor-control studies linking internal tissue motion to kinematics, tremor measures, muscle activation, and expertise level.Initial insights on expertise from this dataset are available in:Namburi, P., Pallarès-López, R., Folgado, D. <i>et al.</i> Efficient elastic tissue motions indicate general motor skill. <i>Sci Rep</i> <b>15</b>, 36532 (2025). https://doi.org/10.1038/s41598-025-17092-0