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.

翻译：一个满足解剖学约束的真实人体运动学模型对于人机交互、生物力学和机器人辅助康复至关重要。然而，由于人体手臂运动受到关节极限、关节间和关节内依赖关系、自碰撞、个体能力以及难以表征的肌肉或神经约束的限制，对真实关节约束进行建模极具挑战性。因此，医生和研究人员常依赖简单的箱型约束，忽略了重要的解剖学因素。本文提出一种数据驱动方法，通过从运动捕捉数据中学习真实且受解剖学约束的上肢运动范围（RoM）边界。该方法通过高效超参数调优方案，将一类支持向量机拟合至上肢关节空间探索运动数据集，其性能优于现有针对有效运动范围学习的类似研究。此外，我们提出一种损伤指数（II）指标，可在比较健康与受损手臂时提供能力/损伤的量化评估。我们通过物理约束模拟偏瘫及不同残疾程度（如中风患者）的健康受试者对该指标进行验证。