Problem
Support seizure-state detection and behavior analysis from visual observations.
Context
The current portfolio identifies pose estimation, seizure-state prediction, and analysis of induced and natural conditions.
My role
Developed system components across pose and prediction workflows.
Constraints
- Biomedical data and privacy requirements.
- Small or specialized datasets may limit generalization.
- Public descriptions must avoid sensitive protocol details and private biomedical data.
Architecture
The workflow combines visual observation, pose-estimation features, temporal modeling, and behavior-state prediction.
Technical decisions
The work emphasizes interpretable visual features, temporal consistency, and careful validation for specialized biomedical settings.
Trade-offs
The core trade-offs involve sensitivity, specificity, interpretability, dataset scale, and responsible deployment in biomedical contexts.
Results
Active research work focused on pose-driven behavior analysis and seizure-state prediction.
Public note
Visual examples are omitted from the public portfolio pending privacy and publication review.
Related links
Related public references can be listed as they become available.