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<p>This paper describes the development, fabrication and testing of Playcuff, a wearable device designed to act as a videogame controller for children with motor disabilities, which also provides an orthotic action to improve the control of the upper limb. The aim of this device is to empower children with motor impairment and enable them to access and enjoy gaming despite their disabilities. The videogame controller function was achieved through on-board gesture classification using a two-tiered Fine Tree machine learning algorithm integrated into the device’s firmware. Based on features extracted from two inertial sensors present on the device, the classifier was trained to identify in real time 22 classes representing different postures and movements of forearm and wrist, showing an accuracy higher than 94%. A cohort of children (<i>n</i> = 19, aged 9.01 ± 1.95&#xa0;years old) with neuromotor impairment involving the upper limb were enrolled to test the device. The acceptability and effectiveness of the device were evaluated through a specific questionnaire: the resulting answers were heavily skewed towards appreciation (80.5%) rather than criticism. The methods of classification were found to be simple and effective in controlling the game. In conclusion, Playcuff was shown to be a versatile and well-received orthotic controller, which could be used in future also for videogame-based rehabilitation.</p>

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Design and Preliminary Evaluation of a Smart Orthotic Videogame Controller Dedicated to Children

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月23日

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  • Fabio Lazzari,
  • Jacopo Romanò,
  • Roberta Nossa,
  • Sara Meloni,
  • Lorenzo Garavaglia,
  • Eleonora Diella,
  • Matteo Valoriani,
  • Francesca Fedeli,
  • Matteo Porro,
  • Emilia Biffi,
  • Simone Pittaccio

摘要

This paper describes the development, fabrication and testing of Playcuff, a wearable device designed to act as a videogame controller for children with motor disabilities, which also provides an orthotic action to improve the control of the upper limb. The aim of this device is to empower children with motor impairment and enable them to access and enjoy gaming despite their disabilities. The videogame controller function was achieved through on-board gesture classification using a two-tiered Fine Tree machine learning algorithm integrated into the device’s firmware. Based on features extracted from two inertial sensors present on the device, the classifier was trained to identify in real time 22 classes representing different postures and movements of forearm and wrist, showing an accuracy higher than 94%. A cohort of children (n = 19, aged 9.01 ± 1.95 years old) with neuromotor impairment involving the upper limb were enrolled to test the device. The acceptability and effectiveness of the device were evaluated through a specific questionnaire: the resulting answers were heavily skewed towards appreciation (80.5%) rather than criticism. The methods of classification were found to be simple and effective in controlling the game. In conclusion, Playcuff was shown to be a versatile and well-received orthotic controller, which could be used in future also for videogame-based rehabilitation.