# Learning Realistic Joint Space Boundaries for Range of Motion Analysis of Healthy and Impaired Human Arms

arXiv: https://arxiv.org/abs/2311.10653 · HTML: https://mlsanity.com/paper/2311.10653/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 17 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Swing-twist decomposition in Clifford algebra](https://mlsanity.com/paper/1506.05481/) (2015)
- [Data-Driven Approach to Simulating Realistic Human Joint Constraints](https://mlsanity.com/paper/1709.08685/) (2017)

## Built on by (most cited first, 1 of 1)

- [GRACE: Generalizing Robot-Assisted Caregiving with User Functionality Embeddings](https://mlsanity.com/paper/2501.17855/) (2025)

## Abstract (arXiv)

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. [https://sites.google.com/seas.upenn.edu/learning-rom]