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Reengineering and Application of the Gross Motor Skills Training System from the Artificial Intelligence and Assistive Technologies Research Group (GIIATA-U.P.S.)

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Motor skills are a fundamental component for the comprehensive development of children, enabling a healthy and autonomous lifestyle. They are essential for the successful execution of coordinated actions involving general body movements. In this context, we propose the adaptation of a gross and fine motor skills training system for children, based on a robotic tutor and a multisensory environment. This system is designed as a didactic resource tailored to the specific needs of the target group, encouraging exploration and participation as a novel learning approach. The training system is redesigned and developed following a V-model methodology (a standardized procedure for the development of ICT products and embedded system adaptation), structured in two stages: 1) the elicitation of requirements and specifications for scenario definition, system architecture, control logic, and compliance with design standards (ISO 8124 and IEC 62115), and 2) the functional validation of the prototype through expert evaluation of its technical features.

Original languageEnglish
Title of host publicationProceedings of the Future Technologies Conference, FTC 2025, Volume 4
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages558-573
Number of pages16
ISBN (Print)9783032079916
DOIs
StatePublished - 2026
EventFuture Technologies Conference, FTC 2025 - Munich, Germany
Duration: 6 Nov 20257 Nov 2025

Publication series

NameLecture Notes in Networks and Systems
Volume1678 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceFuture Technologies Conference, FTC 2025
Country/TerritoryGermany
CityMunich
Period6/11/257/11/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

  • Adaptation
  • Assistive technology
  • Embedded system
  • Motor skill
  • Motor stimulation

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