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.

我们提出了一种数据驱动的方法，从动作捕捉数据中学习逼真的解剖约束上肢运动范围界限。此方法采用了一种高效的超参数调整方案，通过拟合一类支持向量机模型来实现。我们的方法在有效运动范围学习方面优于类似的研究，并提出了一种功能指数评估指标，用于比较健康和受损手臂的能力/损伤。我们通过将健康受试者物理约束模拟为偏瘫和不同残疾程度的中风患者，对该指标进行验证。

学习健康与受损人类手臂的关节空间范围分析的逼真边界