Keypoint data has received a considerable amount of attention in machine learning for tasks like action detection and recognition. However, human experts in movement such as doctors, physiotherapists, sports scientists and coaches use a notion of joint angles standardised by the International Society of Biomechanics to precisely and efficiently communicate static body poses and movements. In this paper, we introduce the basic biomechanical notions and show how they can be used to convert common keypoint data into joint angles that uniquely describe the given pose and have various desirable mathematical properties, such as independence of both the camera viewpoint and the person performing the action. We experimentally demonstrate that the joint angle representation of keypoint data is suitable for machine learning applications and can in some cases bring an immediate performance gain. The use of joint angles as a human meaningful representation of kinematic data is in particular promising for applications where interpretability and dialog with human experts is important, such as many sports and medical applications. To facilitate further research in this direction, we will release a python package to convert keypoint data into joint angles as outlined in this paper.

翻译：关键点数据在动作检测与识别等机器学习任务中受到了广泛关注。然而，医生、物理治疗师、运动科学家和教练等运动学领域的专家，通常采用国际生物力学学会标准化的关节角度概念来精确高效地描述静态身体姿态与运动。本文介绍了基本的生物力学概念，并展示了如何利用这些概念将常见的关键点数据转换为关节角度——这种表示方法能够唯一描述给定姿态，并具备多种理想的数学特性，例如独立于摄像机视角和执行动作的个体。我们通过实验证明，关键点数据的关节角度表示适用于机器学习应用，在某些情况下能够直接带来性能提升。将关节角度作为运动学数据的人类可理解表示，在可解释性以及与人类专家对话至关重要的应用场景中尤其具有前景，例如众多体育和医疗应用领域。为促进该方向的进一步研究，我们将发布一个Python工具包，用于按照本文所述方法将关键点数据转换为关节角度。