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.