论文标题
通过可解释的运动动力学表示自我报告的疼痛自动估算
Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics
论文作者
论文摘要
我们提出了一种自动方法，用于视频的疼痛强度测量。对于每个视频，使用面部运动的动力学使用66个面部点测量疼痛强度。革兰氏矩阵公式用于对称固定等级的对称阳性半明确矩阵的riemannian歧管上的面部点轨迹表示。然后使用曲线拟合和时间对齐来平滑提取的轨迹。然后，对载体回归模型进行了训练，以将提取的轨迹编码为十个疼痛强度水平，与视觉模拟量表进行疼痛强度测量一致。使用UNBC McMaster肩部疼痛存档评估了所提出的方法，并将其与相同数据的最新方法进行了比较。使用5倍的交叉验证和一项受试者的交叉验证，我们的结果在最先进的方法方面具有竞争力。
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.
