Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)

TL;DR

This paper introduces VIDIMU, a new multimodal dataset combining affordable video and inertial sensors to facilitate human activity recognition and kinematic analysis in daily life, with clinical relevance.

Contribution

The dataset uniquely integrates clinical movement relevance, affordable multimodal sensors, and advanced data processing tools for real-world activity analysis.

Findings

Validated that real-life data collection yields comprehensive joint angle data.

Demonstrated the feasibility of using affordable sensors for clinical biomechanics.

Provided a new resource for out-of-lab human activity research.

Abstract

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.