From 2ec0af5f62d11b7621924bf10a2154f6fe6dff71 Mon Sep 17 00:00:00 2001 From: Michael Date: Tue, 10 Mar 2026 14:23:02 +0100 Subject: [PATCH] 2.1 and 2.2 written --- project_report.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/project_report.md b/project_report.md index 471fc6a..76533a1 100644 --- a/project_report.md +++ b/project_report.md @@ -31,6 +31,7 @@ Purpose: - Keep relevant columns (FACE_AUs and eye-tracking raw values) - Filter invalid samples (e.g., invalid level segments): Make sure not to drop rows where NaN is necessary for later feature creation, therefore use subset argument in dropNa()! - Export subject-level parquet files +- Before running the scripts: be aware that the whole dataset contains 30 files with around 900 Mbytes each, provide enough storage and expect this to take a while. ### 2.2 Feature Engineering (Offline) @@ -39,10 +40,14 @@ Main script: - `dataset_creation/combined_feature_creation.py` Behavior: -- Builds fixed-size sliding windows over subject time series +- Builds fixed-size sliding windows over subject time series (window size and step size can be adjusted) +- Uses prepared parquet files from 2.1 - Aggregates AU statistics per window (e.g., `FACE_AUxx_mean`) - Computes eye-feature aggregates (fix/sacc/blink/pupil metrics) -- Produces training-ready feature tables +- Produces training-ready feature tables = dataset +- Parameter ```MIN_DUR_BLINKS``` can be adjusted, although this value needs to make sense in combination with your sampling frequency +- With low videostream rates, consider to reevaluate the meaningfulness of some eye-tracking features, especially the fixations +- running the script requires a manual installation of pygaze Analyser library from [github](https://github.com/esdalmaijer/PyGazeAnalyser.git) ### 2.3 Online Camera + Eye + AU Feature Extraction