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Evaluating Handwriting Skills through Human-Machine Interaction : A New Digitalized System for Parameters Extraction

Provenzale, Cecilia ; Sparaci, Laura ; Fantasia, Valentina LU ; Bonsignori, Chiara ; Formica, Domenico and Taffoni, Fabrizio (2022) 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) In Annual International Conference of the IEEE Engineering in Medicine and Biology Society 44. p.5128-5131
Abstract
Handwriting is an important component of academic curricula and grapho-motor skills (GMS) support learning, reading, memory and self-confidence. Teachers and clinicians report increase in children experiencing problems with acquiring fluid and legible handwriting. To date gold-standard tests evaluating children's GMS, mostly rely on pen and paper tests, requiring extensive coding time and subject to high inter-rater variability. This work presents preliminary data on a new digital platform for Grapho-motor Handwriting Evaluation & Exercise (GHEE), attempting to overcome limitations of available digitalized methods for GMS evalution. In fact, contrary to previous systems, GHEE design originated from comparisons among multiple... (More)
Handwriting is an important component of academic curricula and grapho-motor skills (GMS) support learning, reading, memory and self-confidence. Teachers and clinicians report increase in children experiencing problems with acquiring fluid and legible handwriting. To date gold-standard tests evaluating children's GMS, mostly rely on pen and paper tests, requiring extensive coding time and subject to high inter-rater variability. This work presents preliminary data on a new digital platform for Grapho-motor Handwriting Evaluation & Exercise (GHEE), attempting to overcome limitations of available digitalized methods for GMS evalution. In fact, contrary to previous systems, GHEE design originated from comparisons among multiple standardized tests and was based on a human-machine interaction approach. GHEE hardware and software is presented as well as data on preliminary testing. Cursive handwriting data from six adult volunteers was analyzed according to six parameters of relevance, both automatically (i.e., using GHEE software) and manually (i.e., by a human coder). Comparisons among machine and human data sets allowed parsing out parameters to be extracted automatically and parameters requiring human-machine interaction. Results confirmed platform efficacy and feasibility of the proposed approach. (Less)
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author
; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
series title
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
volume
44
article number
36086649
pages
5128 - 5131
publisher
IEEE Press
conference name
44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
conference location
Glasgow, United Kingdom
conference dates
2022-07-11 - 2022-07-15
external identifiers
  • pmid:36086649
  • scopus:85138128547
ISSN
2694-0604
ISBN
978-1-7281-2782-8
DOI
10.1109/EMBC48229.2022.9871538
language
English
LU publication?
yes
additional info
PubMed ID: 36086649
id
bff1be31-7175-449c-a2ec-e9c468a33189
date added to LUP
2022-09-27 20:48:11
date last changed
2023-03-28 04:03:12
@inproceedings{bff1be31-7175-449c-a2ec-e9c468a33189,
  abstract     = {{Handwriting is an important component of academic curricula and grapho-motor skills (GMS) support learning, reading, memory and self-confidence. Teachers and clinicians report increase in children experiencing problems with acquiring fluid and legible handwriting. To date gold-standard tests evaluating children's GMS, mostly rely on pen and paper tests, requiring extensive coding time and subject to high inter-rater variability. This work presents preliminary data on a new digital platform for Grapho-motor Handwriting Evaluation & Exercise (GHEE), attempting to overcome limitations of available digitalized methods for GMS evalution. In fact, contrary to previous systems, GHEE design originated from comparisons among multiple standardized tests and was based on a human-machine interaction approach. GHEE hardware and software is presented as well as data on preliminary testing. Cursive handwriting data from six adult volunteers was analyzed according to six parameters of relevance, both automatically (i.e., using GHEE software) and manually (i.e., by a human coder). Comparisons among machine and human data sets allowed parsing out parameters to be extracted automatically and parameters requiring human-machine interaction. Results confirmed platform efficacy and feasibility of the proposed approach.}},
  author       = {{Provenzale, Cecilia and Sparaci, Laura and Fantasia, Valentina and Bonsignori, Chiara and Formica, Domenico and Taffoni, Fabrizio}},
  booktitle    = {{2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}},
  isbn         = {{978-1-7281-2782-8}},
  issn         = {{2694-0604}},
  language     = {{eng}},
  pages        = {{5128--5131}},
  publisher    = {{IEEE Press}},
  series       = {{Annual International Conference of the IEEE Engineering in Medicine and Biology Society}},
  title        = {{Evaluating Handwriting Skills through Human-Machine Interaction : A New Digitalized System for Parameters Extraction}},
  url          = {{http://dx.doi.org/10.1109/EMBC48229.2022.9871538}},
  doi          = {{10.1109/EMBC48229.2022.9871538}},
  volume       = {{44}},
  year         = {{2022}},
}