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Authors: Stefania Bello 1 ; Alessia Monaco 2 ; Luca Musti 1 ; Giuseppe Pirlo 2 and Gianfranco Semeraro 2 ; 3

Affiliations: 1 Digital Innovation srl, 70125, Bari, Italy ; 2 Department of Computer Science, University of Studies of Bari “Aldo Moro”, Via Edoardo Orabona, 4, 70125 Bari, BA, Italy ; 3 University School for Advanced Studies IUSS Pavia, Palazzo del Broletto, Piazza Della Vittoria, 15, 27100 Pavia, PV, Italy

Keyword(s): Random Hybrid Strokes, Kinematic Theory, Handwriting, Early Dementia Identification, Bi-LSTM.

Abstract: This paper proposes an improvement to the data augmentation technique, Random Hybrid Stroke (RHS), widely used in handwriting analysis for the early detection of dementia. This improvement involves the appli- cation of a filtering method to handwriting time series, redefining the concept of a ’stroke’ based on insights derived from kinematic theory. Specifically, a trait is considered as the segment joining successive local mini- mum and local maximum points with respect to the lognormal velocity profile. Experimental evaluations were conducted using a dataset consisting of 23 different writing tasks (Mini-COG, MMSE, etc.) for the early de- tection of dementia using K-Fold cross-validation with K set to 10. The proposed improvement demonstrates promising results, showing an increase in performance over a wide range of writing tasks and representing a significant contribution, in particular, for the Mini-COG, MMSE and Trail Matrix Tests.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Bello, S.; Monaco, A.; Musti, L.; Pirlo, G. and Semeraro, G. (2024). Filtered Random Hybrid Strokes (Frhs): Filtering Time-Series Considerding Velocity Profile. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - NeroPRAI; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 961-968. DOI: 10.5220/0012567500003654

@conference{neroprai24,
author={Stefania Bello. and Alessia Monaco. and Luca Musti. and Giuseppe Pirlo. and Gianfranco Semeraro.},
title={Filtered Random Hybrid Strokes (Frhs): Filtering Time-Series Considerding Velocity Profile},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - NeroPRAI},
year={2024},
pages={961-968},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012567500003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - NeroPRAI
TI - Filtered Random Hybrid Strokes (Frhs): Filtering Time-Series Considerding Velocity Profile
SN - 978-989-758-684-2
IS - 2184-4313
AU - Bello, S.
AU - Monaco, A.
AU - Musti, L.
AU - Pirlo, G.
AU - Semeraro, G.
PY - 2024
SP - 961
EP - 968
DO - 10.5220/0012567500003654
PB - SciTePress