Project
TemPredict
Wearable and multimodal data infrastructure for predictive-health research and clinical support.
Overview
TemPredict sits near the boundary of scientific platforms and health analytics. The public description should stay focused on the data systems rather than implying clinical claims.
Problem
Wearable and multimodal datasets produce heterogeneous, high-frequency signals that need to be organized for analysis, reproducibility, and collaboration.
Approach
The UCSF study describes wearable sensing for predicting symptom onset such as fever, cough, and fatigue, using an Oura Ring and related smartphone app data. That aligns with the platform work in this project record.
Contributions
- Multimodal data ingestion
- Platform support for predictive-health research
- Scientific analytics workflow support
Placeholder notes
This should be presented as a data-platform and research-enablement effort rather than a clinical-claims page.
Project details
Problem: How do wearable and clinical data streams get structured so they can support reproducible analysis without overfitting the platform to one study?
Approach: Supported continuous wearable-sensor ingestion, harmonization, monitoring, data-quality control, and analytical workflows.
Contributions
- Supported scalable ingestion for wearable and multimodal data.
- Contributed to analytics-oriented data organization.
- Helped enable predictive-health research workflows.
Outcomes
- Enabled a published conference paper and a large-scale wearable-sensing study.
- Connected platform work to symptom-prediction and monitoring workflows.