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 tracking solutions for
remote 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 VIDIMU 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.

该研究致力于提供一种通过廉价视频和自定义传感器记录的数据集来实现人体姿态跟踪和运动重建的方法，以支持远程的日常生活活动识别和运动学分析等问题。通过对 54 名受试者进行研究，验证了该数据集具有良好的临床应用价值和相关的不扰动采集协议，并可以提供人体关节角度的全面图像。