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.

翻译：可穿戴惯性测量单元（IMUs）为临床及日常环境中的人体运动评估提供了一种经济有效的途径。然而，开发用于物理治疗训练与步态分析稳健评估的相关分类模型需要大规模、多样化的数据集，其采集过程成本高昂且耗时。本研究提出一个多模态物理治疗与步态相关训练数据集，包含正确动作及具有临床意义的变异动作，通过同步的IMUs与基于光学标记的运动捕捉系统（MoCap）从19名健康受试者采集获得。数据集包含9个IMUs与68个标记点记录的全身体运动学数据。每个IMU对应的四个标记点支持IMU与MoCap推算方向的直接对比。我们进一步提供经处理、对齐至通用肢体坐标系系统的IMU方向数据、受试者特异性OpenSim模型、逆向运动学输出结果，以及IMU方向可视化工具。数据集完整标注了运动质量评分与时间戳分割信息，支持多种机器学习任务，如训练动作评估、步态分类、时序分割及生物力学参数估计。为促进可重复性，本研究同时提供了后处理、数据对齐、逆向运动学及技术验证的相关代码。