  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.
