Learning Realistic Joint Space Boundaries for Range of Motion Analysis of Healthy and Impaired Human Arms
This work addresses the need for accurate joint space modeling in human-robot interaction and rehabilitation, though it appears incremental as it builds on existing data-driven approaches for range of motion analysis.
The paper tackles the problem of modeling realistic anatomical constraints for human arm motion by proposing a data-driven method to learn range of motion boundaries from motion capture data, achieving superior performance compared to similar works and introducing an impairment index metric validated on healthy subjects emulating stroke patients.
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. [https://sites.google.com/seas.upenn.edu/learning-rom]