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

arXiv: https://arxiv.org/abs/2406.17443 · HTML: https://mlsanity.com/paper/2406.17443/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Quo Vadis, Skeleton Action Recognition ?](https://mlsanity.com/paper/2007.02072/) (2020)
- [Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/2105.01563/) (2021)
- [NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human Actions](https://mlsanity.com/paper/2101.11529/) (2021)
- [Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition](https://mlsanity.com/paper/2110.13008/) (2021)
- [GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2403.15212/) (2024)

## Built on by (most cited first, 3 of 3)

- [Fine-grained Motion Retrieval via Joint-Angle Motion Images and Token-Patch Late Interaction](https://mlsanity.com/paper/2603.09930/) (2026)
- [LingoMotion: An Interpretable and Unambiguous Symbolic Representation for Human Motion](https://mlsanity.com/paper/2603.13578/) (2026)
- [Encoder-Free Human Motion Understanding via Structured Motion Descriptions](https://mlsanity.com/paper/2604.21668/) (2026)

## Abstract (arXiv)

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.