# Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics

arXiv: https://arxiv.org/abs/2006.13882 · HTML: https://mlsanity.com/paper/2006.13882/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices](https://mlsanity.com/paper/2209.01813/) (2022)
- [Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection](https://mlsanity.com/paper/2207.08811/) (2022)
- [QAFE-Net: Quality Assessment of Facial Expressions with Landmark Heatmaps](https://mlsanity.com/paper/2312.00856/) (2023)
- [PainNet: Statistical Relation Network with Episode-Based Training for Pain Estimation](https://mlsanity.com/paper/2504.06257/) (2025)

## Abstract (arXiv)

We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory representations on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. Curve fitting and temporal alignment were then used to smooth the extracted trajectories. A Support Vector Regression model was then trained to encode the extracted trajectories into ten pain intensity levels consistent with the Visual Analogue Scale for pain intensity measurement. The proposed approach was evaluated using the UNBC McMaster Shoulder Pain Archive and was compared to the state-of-the-art on the same data. Using both 5-fold cross-validation and leave-one-subject-out cross-validation, our results are competitive with respect to state-of-the-art methods.