University of Valladolid, Valladolid, Spain.
University of Applied Sciences and Arts Western Switzerland (HES-SO) Valais-Wallis, Sierre, Switzerland.
Sci Data. 2023 Sep 22;10(1):648. doi: 10.1038/s41597-023-02554-9.
Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient gross motor tracking solutions for daily life activities recognition and kinematic analysis. The dataset includes 13 activities registered using a commodity camera and five inertial sensors. The video recordings were acquired in 54 subjects, of which 16 also had simultaneous recordings of inertial sensors. The novelty of dataset lies in: (i) the clinical relevance of the chosen movements, (ii) the combined utilization of affordable video and custom sensors, and (iii) the implementation of state-of-the-art tools for multimodal data processing of 3D body pose tracking and motion reconstruction in a musculoskeletal model from inertial data. The validation confirms that a minimally disturbing acquisition protocol, performed according to real-life conditions can provide a comprehensive picture of human joint angles during daily life activities.
人体活动识别和临床生物力学是物理远程康复医学中的难题。然而，大多数现有的人体运动公开数据集不能用于在实验室外的运动采集环境中同时研究这两个问题。VIDIMU 数据集的目标是为日常活动识别和运动学分析提供负担得起的患者总体运动跟踪解决方案铺平道路。该数据集包括使用商品相机和五个惯性传感器记录的 13 种活动。视频记录是在 54 名受试者中采集的，其中 16 名受试者还同时记录了惯性传感器的数据。该数据集的新颖之处在于：（i）所选运动的临床相关性，（ii）经济实惠的视频和定制传感器的组合使用，以及（iii）实施最先进的工具，用于从惯性数据对 3D 人体姿势跟踪和运动重建进行多模态数据处理，构建肌肉骨骼模型。验证结果证实，根据实际生活条件进行最小干扰的采集协议，可以全面了解日常生活活动中人体关节的角度。
