  A realistic human kinematic model that satisfies anatomical constraints is
essential for human-robot interaction, biomechanics and robot-assisted
rehabilitation. Modeling realistic joint constraints, however, is challenging
as human arm motion is constrained by joint limits, inter- and intra-joint
dependencies, self-collisions, individual capabilities and muscular or
neurological constraints which are difficult to represent. Hence, physicians
and researchers have relied on simple box-constraints, ignoring important
anatomical factors. In this paper, we propose a data-driven method to learn
realistic anatomically constrained upper-limb range of motion (RoM) boundaries
from motion capture data. This is achieved by fitting a one-class support
vector machine to a dataset of upper-limb joint space exploration motions with
an efficient hyper-parameter tuning scheme. Our approach outperforms similar
works focused on valid RoM learning. Further, we propose an impairment index
(II) metric that offers a quantitative assessment of capability/impairment when
comparing healthy and impaired arms. We validate the metric on healthy subjects
physically constrained to emulate hemiplegia and different disability levels as
stroke patients.
