Using joint angles based on the international biomechanical standards for human action recognition and related tasks

TL;DR

This paper introduces a method to convert keypoint data into standardized joint angles based on biomechanical standards, enhancing interpretability and performance in human action recognition tasks.

Contribution

It presents a novel approach to transform keypoint data into joint angles using international biomechanical standards, improving model robustness and interpretability.

Findings

Joint angles improve action recognition accuracy in some cases.

The method offers viewpoint and subject independence.

A Python package for conversion is released.

Abstract

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.