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

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Academic year: 2021

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Fig. 1: Overview of the proposed approach: (left plot) First, facial landmarks are detected using Active Appearance Model (AAM) on each video frame and velocities are computed as the displacement of the coordinates between two consecutive frames
Fig. 2: Example images from the UNBC-McMaster Shoulder Pain Archive in (a) and (c). In (b) and (d) their corresponding landmark coordinates and velocities, respectively (best viewed in color) [16].
TABLE I: Distribution of the VAS pain scores in the UNBC- UNBC-McMaster Shoulder Pain Archive
TABLE III: Comparison of our method with state-of-the-art results

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