Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning - Dataset

This page contains the data collected for the paper: <em><strong>Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning </strong></em>(DOI:10.26717/BJSTR.2019.20.003446). The dataset consists of spectral and pupillometric data collected during three outdoor/indoor walks. The folders “raw”, “merged”, and “cleaned” contain data collected by the Konica Minolta CL-500A Illuminance Spectrophotometer and Tobii Pro Glasses 2 at three different stages in the data preparation process. The “raw” folder contains uncleaned and unsynchronized .csv/.json files. The “merged” folder contains uncleaned, but synchronized light and ocular data in .csv format. The “cleaned” folder contains a single .csv of cleaned and synchronized data with the derived variables: average pupil diameter and pupil diameter difference. The best choice of data files will depend on desired analysis. More guidance on how to handle this data can be found in the readMe files located in each subsequent folder. More information on the sensing devices used here can be found at the Minolta and Tobii information links below. <strong>Minolta Information</strong>: https://sensing.konicaminolta.us/uploads/cl-500a_instruction217a_eng-250cl60686.pdf <strong>Tobii Information</strong>: https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/tobii-pro-glasses-2-user-manual.pdf/?v=1.1.3 The codes used to prepare, analyze, and visualize this data is available in the LightOcular GitHub Repository linked below. <strong>LightOcular GitHub Repo</strong>: https://github.com/mi3nts/LightOcular