Abstract
Navigating long-form media to locate scenes of a particular emotional intensity may be inefficient using conventional methods that rely on static thumbnails or broad metadata. Disclosed systems and methods can generate crowd-sourced affective metadata from physiological signals. These techniques may involve collecting anonymized physiological data, such as heart rate, from a population of consenting users via their computing devices (e.g., a smartphone, smart watch, wearable device, augmented reality glasses, etc.) during media consumption. This data can be processed and aggregated to generate a time-series representation of the collective emotional intensity for a media asset. The resulting data may then be presented visually within a media player interface, for instance as a color-coded graphic on a progress bar, to provide a guide to the content's emotional pacing and assist with navigation.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Agarwal, Nikita, "Generating Affective Metadata from Physiological Signals for Media Navigation", Technical Disclosure Commons, (July 28, 2026)
https://www.tdcommons.org/dpubs_series/11200