CLTR brings together chronobiological context, light exposure, thermal variables, and physiological response into one analyzable workflow. The technical challenge is not only collecting those signals, but keeping timing, metadata, and transformations coherent enough that the resulting dataset can support downstream inference.
My contribution
I worked on the structure around the data rather than on isolated output files: synchronized preprocessing, quality-control checkpoints, feature extraction logic, and workflow organization for reusable analysis.
Technical focus
The project is centered on time alignment, validation of multimodal streams, and making environmental and physiological channels jointly analyzable. That means a workflow where raw inputs, derived features, and analysis decisions remain traceable.
Why it matters
CLTR sits close to the kind of infrastructure future human-aware systems will need: not just more signals, but better synchronized signals that remain meaningful when combined.