GAITEX

Wearable inertial measurement units (IMUs) offer a cost-effective and scalable means to assess human movement quality in

clinical and everyday settings. However, the development of robust sensor-based classification models for physiotherapeutic

exercises and gait analysis requires large, diverse datasets, which are costly and time-consuming to collect. Here, we present a

multimodal dataset of physiotherapeutic exercises - including correct and clinically relevant variants - and gait-related exercises

- including both normal and impaired gait patterns - recorded from 19 participants using synchronized IMUs and marker-based

motion capture (MoCap). The dataset includes raw data from nine IMUs and thirty-five optical markers capturing full-body

kinematics. Each IMU is additionally equipped with four optical markers, enabling precise comparison between IMU-derived

orientation estimates and reference values from the MoCap system. To support further analysis, we also provide processed IMU

orientations aligned with common segment coordinate systems, subject-specific OpenSim models, inverse kinematics results,

and tools for visualizing IMU orientations in the musculoskeletal context. Detailed annotations of movement execution quality

and time-stamped segmentations support diverse analysis goals. This dataset supports the development and benchmarking

of machine learning models for tasks such as automatic exercise evaluation, gait analysis, temporal activity segmentation,

and biomechanical parameter estimation. To facilitate reproducibility, we provide code for postprocessing, sensor-to-segment

alignment, inverse kinematics computation, and technical validation. This resource is intended to accelerate research in

machine learning-driven human movement analysis.

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