PDF(Pubmed)
Abstract：
Light exposure fundamentally influences human physiology and behavior, with light being the most important zeitgeber of the circadian system. Throughout the day, people are exposed to various scenes differing in light level, spectral composition and spatio-temporal properties. Personalized light exposure can be measured through wearable light loggers and dosimeters, including wrist-worn actimeters containing light sensors, yielding time series of an individual\'s light exposure. There is growing interest in relating light exposure patterns to health outcomes, requiring analytic techniques to summarize light exposure properties. Building on the previously published Python-based pyActigraphy module, here we introduce the module pyLight. This module allows users to extract light exposure data recordings from a wide range of devices. It also includes software tools to clean and filter the data, and to compute common metrics for quantifying and visualizing light exposure data. For this tutorial, we demonstrate the use of pyLight in one example dataset with the following processing steps: (1) loading, accessing and visual inspection of a publicly available dataset, (2) truncation, masking, filtering and binarization of the dataset, (3) calculation of summary metrics, including time above threshold (TAT) and mean light timing above threshold (MLiT). The pyLight module paves the way for open-source, large-scale automated analyses of light-exposure data.
摘要：
光暴露从根本上影响人类的生理和行为，光是昼夜节律系统中最重要的时代特征。一整天,人们暴露在不同光线水平的各种场景下，光谱组成和时空特性。可以通过可穿戴的光记录仪和剂量计测量个性化的光暴露，包括包含光传感器的手腕佩戴的肌动仪，产生一个人的光暴露的时间序列。人们越来越感兴趣的是将光暴露模式与健康结果联系起来，需要分析技术来总结曝光特性。基于以前发布的基于Python的pyActigraphy模块，这里我们介绍一下pyLight模块。该模块允许用户从各种设备中提取曝光数据记录。它还包括清理和过滤数据的软件工具，并计算用于量化和可视化曝光数据的通用指标。对于本教程，我们通过以下处理步骤演示了在一个示例数据集中使用pyLight：(1)加载，访问和视觉检查公开可用的数据集，(2)截断，掩蔽,数据集的过滤和二值化，(3)汇总指标的计算，包括高于阈值的时间(TAT)和高于阈值的平均光照时间(MLiT)。pyLight模块为开源铺平了道路，曝光数据的大规模自动分析。
