Abstract: Techniques for calculating or managing pupillary curve data identified based on data associated with computing devices are discussed herein. For example, a machine learning (ML) model can be utilized to analyze sensor data collected by a camera associated with a computing device. The ML model can perform a comparison between a pupillary (e.g., a pupil response curve and previous pupillary curves (e.g., previous pupil response curves) utilizing classification information associated with the previous pupil response curves. The comparison can be utilized to identify a physiological condition associated with the pupil response curve. Information identifying the physiological condition can be presented by a display or transmitted to the computing device.

Abstract: Techniques for calculating or managing pupillary curve data identified based on data associated with computing devices are discussed herein. For example, a machine learning (ML) model can be utilized to analyze sensor data collected by a camera associated with a computing device. The ML model can perform a comparison between a pupillary (e.g., a pupil response curve and previous pupillary curves (e.g., previous pupil response curves) utilizing classification information associated with the previous pupil response curves. The comparison can be utilized to identify a physiological condition associated with the pupil response curve. Information identifying the physiological condition can be presented by a display or transmitted to the computing device.