DIODEM – A Diverse Inertial and Optical Dataset of kinEmatic chain Motion

TL;DR

DIODEM is a new dataset combining optical and inertial data to study motion tracking challenges in biomechanics and autonomous systems.

Contribution

DIODEM introduces a controlled dataset with diverse joint types and motion artifacts for systematic IMT evaluation.

Findings

DIODEM includes 46 minutes of synchronized data from two kinematic chain configurations.

The dataset supports study of sparse sensor setups and motion artifact compensation.

It enables algorithm development for biomechanics and autonomous systems applications.

Abstract

Inertial Motion Tracking (IMT) faces critical challenges including magnetometer-free sensing, sparse sensor configurations, sensor-to-segment alignment, and motion artifact compensation. Current IMT algorithms require systematic evaluation across combinations of these challenges in controlled environments with accurate ground truth data. This paper presents DIODEM–a comprehensive dataset comprising 46 minutes of synchronized optical and inertial data from five-segment Kinematic Chains (KCs). The dataset features 20 markers and ten IMUs (both rigidly and foam-attached) across two distinct kinematic configurations: an “arm” chain with hinge and spherical joints, and a “gait” chain with hinge and saddle joints. The KCs perform diverse motions including random movements at various speeds, pick-and-place tasks, and gait-like patterns. Key technical contributions include: (1) mechanically controlled setup with known kinematics, (2) systematic inclusion of motion artifacts through foam-attached IMUs, (3) diverse joint types including 1D, 2D, and 3D joints, and (4) comprehensive motion variety supporting sparse sensing scenarios. The dataset enables researchers to systematically study individual and combined IMT challenges, facilitating algorithm development for applications ranging from biomechanics to autonomous systems.

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