Abstract
This paper describes a biometric-to-video synchronization engine that pairs wearable physiological sensors with a distributed video platform for crowdsourced intensity mapping and normalized effort-based benchmarking. Fitness video content on open platforms typically relies on subjective difficulty labels assigned by creators, which fail to account for the physiological diversity of viewers. The synchronization engine establishes a temporal sync anchor between a wearable device's biometric data stream and the video playback timeline, employing clock-drift compensation to align UTC-stamped biometric packets with the relative playback head and a latency correction algorithm to account for physiological response delay. As anonymized, normalized heart rate data accumulates across a viewer population, the system generates an aggregate intensity heatmap overlaid on the video progress bar. A ghost benchmarking overlay renders a secondary biometric curve representing a target profile as a real-time visual pacer. For fair cross-user comparison, a Relative Effort Score is computed from each viewer's Heart Rate Reserve, decoupling absolute fitness level from measured exertion. This yields objective, population-verified difficulty quantification of any video segment without manual tagging.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
She, Alan, "Crowdsourced Biometric Synchronization for Video Intensity Mapping and Benchmarking", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10379