健常者と障害者の上肢可動域解析のための現実的な関節空間境界の学習
Learning Realistic Joint Space Boundaries for Range of Motion Analysis of Healthy and Impaired Human Arms
モーションキャプチャデータから上肢の解剖学的制約を満たす可動域境界をワンクラスSVMで学習し、健常者と障害者の能力を定量評価する障害指数を提案した。
著者: Shafagh Keyvanian, Michelle J. Johnson, Nadia Figueroa
分類: cs.RO, cs.LG
原文アブストラクト
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]
