Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories in 3D Task-Space Captured by a Redundant Measurement System

TL;DR

This paper presents a comprehensive dataset of 3D hand transport trajectories captured in natural settings, using redundant optical and IMU sensors, to facilitate movement disorder analysis and modeling.

Contribution

It introduces a systematic, multi-sensor dataset of human hand movements during center-out tasks, including a portable IMU measurement system as an alternative to optical tracking.

Findings

Dataset includes 90 trajectories per hand from 31 participants.

IMU sensors provide a portable, low-cost alternative for movement data collection.

Data covers a wide age range, enabling diverse movement analysis.

Abstract

Neurological conditions are a major source of movement disorders. Motion modelling and variability analysis have the potential to identify pathology but require profound data. We introduce a systematic dataset of 3D center-out task-space trajectories of human hand transport movements in a natural setting. The transport tasks of this study consist of grasping a cylindric object from a unified start position and transporting it to one of nine target locations in unconstrained operational space. The measurement procedure is automatized to record ten trials per target location. With that, the dataset consists of 90 movement trajectories for each hand of 31 participants without known movement disorders. The participants are aged between 21 and 78 years, covering a wide range. Data are recorded redundantly by both an optical tracking system and an IMU sensor. As opposed to the stationary capturing system, the IMU can be considered as a portable, low-cost and energy-efficient alternative to be implemented on embedded systems.