Detail publikačního výsledku

Synergy of Eye and Hand: Multimodal Biomarkers for Cognitive Assessment

GAVENČIAK, M.; MUCHA, J.; MEKYSKA, J.; FAÚNDEZ ZANUY, M.; FERRER-RAMOS, P.

Originální název

Synergy of Eye and Hand: Multimodal Biomarkers for Cognitive Assessment

Anglický název

Synergy of Eye and Hand: Multimodal Biomarkers for Cognitive Assessment

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Traditional handwriting analysis for neurological assessment captures motor output but largely misses the guiding cognitive processes like visuospatial planning and attention. This study introduces a multimodal approach, combining online handwriting kinematics with concurrent eye-tracking data from 48 older adults performing the Pentagon Copy Test (PCT). We extracted novel feature sets, including Hand-Eye Coupling (HEC) and Fractional Derivative (FD) biomarkers, and used an XGBoost classifier with Recursive Feature Elimination (RFE) to predict a binarized PCT performance score. Our final model, integrating all features, achieved a Balanced Accuracy (BACC) of 90%, significantly outperforming a model trained on baseline features alone (79% BACC). The findings demonstrate that integrating eye-tracking data with advanced handwriting analysis provides a powerful and holistic tool for objectively assessing cognitive-motor performance, highlighting its potential as a sensitive digital biomarker.

Anglický abstrakt

Traditional handwriting analysis for neurological assessment captures motor output but largely misses the guiding cognitive processes like visuospatial planning and attention. This study introduces a multimodal approach, combining online handwriting kinematics with concurrent eye-tracking data from 48 older adults performing the Pentagon Copy Test (PCT). We extracted novel feature sets, including Hand-Eye Coupling (HEC) and Fractional Derivative (FD) biomarkers, and used an XGBoost classifier with Recursive Feature Elimination (RFE) to predict a binarized PCT performance score. Our final model, integrating all features, achieved a Balanced Accuracy (BACC) of 90%, significantly outperforming a model trained on baseline features alone (79% BACC). The findings demonstrate that integrating eye-tracking data with advanced handwriting analysis provides a powerful and holistic tool for objectively assessing cognitive-motor performance, highlighting its potential as a sensitive digital biomarker.

Klíčová slova

Digital Biomarkers, Eyetracking, Fractional Calculus, Handwriting, Pentagon Copy Test

Klíčová slova v angličtině

Digital Biomarkers, Eyetracking, Fractional Calculus, Handwriting, Pentagon Copy Test

Autoři

GAVENČIAK, M.; MUCHA, J.; MEKYSKA, J.; FAÚNDEZ ZANUY, M.; FERRER-RAMOS, P.

Rok RIV

2026

Vydáno

03.11.2025

Nakladatel

IEEE

Místo

Florence, Italy

ISBN

979-8-3315-7675-2

Kniha

2025 17th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

Strany od

278

Strany do

283

Strany počet

5

URL

Plný text v Digitální knihovně

BibTex

@inproceedings{BUT199887,
  author="Michal {Gavenčiak} and Ján {Mucha} and Jiří {Mekyska} and Marcos {Faúndez Zanuy} and  {}",
  title="Synergy of Eye and Hand: Multimodal Biomarkers for Cognitive Assessment",
  booktitle="2025 17th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)",
  year="2025",
  pages="278--283",
  publisher="IEEE",
  address="Florence, Italy",
  doi="10.1109/ICUMT67815.2025.11268615",
  isbn="979-8-3315-7675-2",
  url="https://ieeexplore.ieee.org/document/11268615"
}