Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics
This addresses pain assessment for medical applications, but appears incremental as it builds on existing methods and datasets.
The researchers tackled automatic pain intensity measurement from video by analyzing facial movement dynamics using 66 facial points, achieving competitive results with state-of-the-art methods on the UNBC McMaster Shoulder Pain Archive dataset using cross-validation.
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.