  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.
