Abstract This paper deals with the identification of Volterra models that capture the dynamics of smooth pursuit eye movements recorded by an eye tracker. The framework is motivated by neurological applications but can also be useful in biometrics. In healthy subjects, ocular dynamics are predominantly linear, while neurological conditions inflict nonlinearity on smooth pursuit eye movements. Besides overparameterization, Volterra models may also exhibit functional dependence among the model coefficients. A combination of sparse estimation and Principal Component Analysis is shown to be instrumental in estimating parsimonious Volterra models from eye-tracking data. The efficacy of the approach is demonstrated on experimental data collected from Parkinsonian patients as well as healthy controls.

中文翻译：

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通过具有函数依赖参数的稀疏 Volterra 模型对人类平滑追踪进行建模

摘要 本文讨论了 Volterra 模型的识别，该模型捕获了眼动仪记录的平滑追踪眼动的动态。该框架受神经学应用的启发，但也可用于生物识别。在健康受试者中，眼动力学主要是线性的，而神经系统疾病对平滑追踪眼球运动造成非线性。除了过参数化之外，Volterra 模型还可能表现出模型系数之间的函数依赖性。稀疏估计和主成分分析的组合被证明有助于从眼动数据中估计简约的 Volterra 模型。从帕金森病患者和健康对照收集的实验数据证明了该方法的有效性。