Abstract
Personal health monitoring devices may detect physiological changes but can lack environmental context to determine a cause, while environmental alert systems may rely on generic, non-personalized data. Systems and methods are described that can algorithmically fuse multiple data streams. These streams can include real-time physiological data from a wearable computing device (e.g., a smart watch, fitness tracker), such as heart rate variability; hyperlocal environmental data, such as air quality or noise levels; and contextual data, such as a user's location and activity level. This fusion of data may be used to compute a personalized risk index. The index may then be used to generate dynamic, context-aware alerts that can differentiate between physiological responses to benign activities, such as exercise, and those potentially triggered by environmental stressors, which can provide more actionable and personalized risk assessments.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Shevde, Sumukh and Chauhan, Sudeep, "Personalized Environmental Risk Alerting via Fusion of Physiological, Environmental, and Contextual Data", Technical Disclosure Commons, (March 11, 2026)
https://www.tdcommons.org/dpubs_series/9500