Development of a time-dependent model of the human pupil with adaptive receptor weighting

开发具有自适应受体加权的人类瞳孔时间依赖性模型

• 批准号: 450636577
• 负责人: Professor Dr.-Ing. Tran Quoc Khanh
• 金额: --
• 依托单位: Fachgebiet für Adaptive Lichttechnische Systeme und Visuelle Verarbeitung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/450636577?language=en
• 关键词: Development; time; dependent; model; human

Research projects concerning the human pupil diameter have intensified in recent years, achieving considerable progress, especially in the application of the pupil diameter as a biomarker in interdisciplinary fields of application. For example, it is possible to assess the human cognitive performance or to detect various physiological states such as fatigue, arousal, or deep depression via the temporal variation of the human pupil size. In lighting technology, the pupil diameter is considered a relevant topic, especially in the context of the ophthalmic optical apparatus and application-oriented indoor lighting. The size of the pupil diameter varies the retinal illuminance, which can indirectly affect the humans' brightness perception or visual acuity. The discovery of intrinsically photosensitive retinal ganglion cells (ipRGCs) has led to more intense research in the field of the afferent pupillary control path and the pupillary light response. From the results of neurophysiological fundamental research, it can be deduced that the photopically adapted pupillary light response is controlled by a combination of outer retinal photoreceptors and the ipRGCs. The proportion of different photoreceptor classes in controlling the pupillary light response is not only time-dependent, but also depends on various properties of light and experimental conditions such as presentation time, spectral composition of the light, intensity, and adaptation time. Despite outstanding research successes in the field of neurophysiological research on the pupillary light reflex, there is still a lack of a mathematical model that can predict the temporal variation of the absolute human pupil diameter in response of different light spectra. Existing pupil models mainly apply a V(𝜆)-weighted quantity as an independent parameter and do not integrate the influence of the ipRGCs, which can lead to significant prediction errors of the pupil diameter when using LED spectra. This project is concerned with the development of a new model for predicting light-induced pupil diameter, which through the use of artificial neural networks (Deep Learning) might for the first time be able to predict the temporal and spectral-dependent human pupil diameter. Our preliminary modeling attempts on test data sets have clarified that with the proposed methodology, the temporal pupil diameter can be reconstructed from photometric quantities with a mean absolute error (MAE) of less than 0.1 mm. In this continuation proposal, we plan to develop the deep learning-based approach further and, among other things, open up the possibility to integrate the latest findings on the neurophysiological mechanism of light-induced pupil behavior into a mathematical model.

近年来，有关人类瞳孔直径的研究项目不断加强，取得了相当大的进展，特别是在瞳孔直径作为生物标志物在跨学科应用领域中的应用。例如，可以通过人的瞳孔大小的时间变化来评估人的认知性能或检测诸如疲劳、觉醒或深度抑郁的各种生理状态。在照明技术中，瞳孔直径被认为是一个相关的主题，特别是在眼科光学设备和面向应用的室内照明的背景下。瞳孔直径的大小改变视网膜照度，这可以间接影响人类的亮度感知或视觉敏锐度。视网膜神经节细胞（intrinsic photosensitive retinal ganglion cells，ipRGC）的发现，引起了对瞳孔传入控制通路和瞳孔光反应的深入研究。从神经生理学基础研究的结果可以推断，视觉适应性瞳孔光反应是由外部视网膜光感受器和ipRGC的组合控制的。控制瞳孔光响应的不同感光器类别的比例不仅是时间依赖性的，而且还取决于光的各种性质和实验条件，如呈现时间，光的光谱组成，强度和适应时间。尽管在瞳孔光反射的神经生理学研究领域取得了杰出的研究成就，但仍然缺乏可以预测响应于不同光谱的绝对人类瞳孔直径的时间变化的数学模型。现有的瞳孔模型主要采用V（）加权量作为独立参数，没有整合ipRGC的影响，这会导致使用LED光谱时瞳孔直径的预测误差很大。该项目涉及开发一种用于预测光诱导瞳孔直径的新模型，通过使用人工神经网络（深度学习），该模型可能首次能够预测时间和光谱相关的人类瞳孔直径。我们在测试数据集上的初步建模尝试已经澄清，使用所提出的方法，可以从光度测量中重建时间瞳孔直径，平均绝对误差（MAE）小于0.1 mm。开辟了将光诱导瞳孔行为的神经生理机制的最新发现整合到数学模型中的可能性。