  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.
