Mobile devices and laptops are the main ICT tools to exchange information among people in the world. All the applications are designed by following a specific interaction style based either touchscreen or mouse and keyboard, which can be performed only with detailed movements of hands and fingers. Traditional interaction becomes difficult for elderly who have diseases limiting the hand motor skills, such as arthritis and brain stroke. The use of simple air gestures can be adopted as alternative interaction style to interact with smartphones, tablets and laptops. The aim of this research work is the development of an application that allows text writing using air gestures for people with limited hand motor skills. The application embeds several computer vision algorithms and convolutional neural networks software modules to detect and drawn alphanumeric characters and recognizing them using both mobile devices and laptops. The preliminary results obtained show that the approach is robust, and it can easily detect the alphanumeric characters written with the movement of the wrist.

(2020). Text writing using air gestures for people with limited hand motor skills . Retrieved from http://hdl.handle.net/10446/175462

Text writing using air gestures for people with limited hand motor skills

Vitali, Andrea;Previdi, Fabio;Rizzi, Caterina
2020-01-01

Abstract

Mobile devices and laptops are the main ICT tools to exchange information among people in the world. All the applications are designed by following a specific interaction style based either touchscreen or mouse and keyboard, which can be performed only with detailed movements of hands and fingers. Traditional interaction becomes difficult for elderly who have diseases limiting the hand motor skills, such as arthritis and brain stroke. The use of simple air gestures can be adopted as alternative interaction style to interact with smartphones, tablets and laptops. The aim of this research work is the development of an application that allows text writing using air gestures for people with limited hand motor skills. The application embeds several computer vision algorithms and convolutional neural networks software modules to detect and drawn alphanumeric characters and recognizing them using both mobile devices and laptops. The preliminary results obtained show that the approach is robust, and it can easily detect the alphanumeric characters written with the movement of the wrist.
2020
Ermidoro, Michele; Vitali, Andrea; Previdi, Fabio; Rizzi, Caterina
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