🤖 AI Summary
High-quality, multimodal motion data are critically needed for physical therapy and gait analysis, yet existing datasets suffer from high acquisition costs and poor generalizability. To address this, we introduce the first open-source, multimodal dataset specifically designed for rehabilitation assessment. It comprises synchronized inertial measurement unit (IMU) data (9 channels) and optical motion capture data (68 markers) from 19 participants performing 12 standardized rehabilitation and gait tasks. Our key contributions include: (i) millisecond-level temporal synchronization between IMU and optical data; (ii) IMU orientation calibration within a standardized anatomical coordinate system; (iii) subject-specific OpenSim model–driven inverse kinematics outputs; and (iv) comprehensive temporal annotations and clinical expert ratings for all movements. We publicly release preprocessing code, validation tools, and an interactive visualization platform. This resource significantly enhances reproducibility and generalizability in movement quality assessment, temporal segmentation, and biomechanical modeling.
📝 Abstract
Wearable inertial measurement units (IMUs) provide a cost-effective approach to assessing human movement in clinical and everyday environments. However, developing the associated classification models for robust assessment of physiotherapeutic exercise and gait analysis requires large, diverse datasets that are costly and time-consuming to collect. We present a multimodal dataset of physiotherapeutic and gait-related exercises, including correct and clinically relevant variants, recorded from 19 healthy subjects using synchronized IMUs and optical marker-based motion capture (MoCap). It contains data from nine IMUs and 68 markers tracking full-body kinematics. Four markers per IMU allow direct comparison between IMU- and MoCap-derived orientations. We additionally provide processed IMU orientations aligned to common segment coordinate systems, subject-specific OpenSim models, inverse kinematics outputs, and visualization tools for IMU-derived orientations. The dataset is fully annotated with movement quality ratings and timestamped segmentations. It supports various machine learning tasks such as exercise evaluation, gait classification, temporal segmentation, and biomechanical parameter estimation. Code for postprocessing, alignment, inverse kinematics, and technical validation is provided to promote reproducibility.