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Details

Details

  • Name

    Diana Machado Carvalho
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st October 2012
001
Publications

2022

My Buddy: A 3D Game for Children Based on Voice Commands

Authors
Carvalho, D; Rocha, T; Barroso, J;

Publication
Innovations in Bio-Inspired Computing and Applications - Lecture Notes in Networks and Systems

Abstract

2021

Developing an Application for Teaching Mathematics to Children with Dyscalculia: A Pilot Case Study

Authors
Carvalho, D; Rocha, T; Martins, P; Barroso, J;

Publication
Innovative Technologies and Learning - 4th International Conference, ICITL 2021, Virtual Event, November 29 - December 1, 2021, Proceedings

Abstract

2021

Art & Accessibility

Authors
Resende, M; Carvalho, D; Branco, A; Rocha, T;

Publication
10th International Conference on Digital and Interactive Arts

Abstract

2021

Automated Evaluation Tools for Web and Mobile Accessibility: A Systematic Literature Review

Authors
Dias, J; Carvalho, D; Paredes, H; Martins, P; Rocha, T; Barroso, J;

Publication
Innovations in Bio-Inspired Computing and Applications - Lecture Notes in Networks and Systems

Abstract

2018

Performance evaluation of different age groups for gestural interaction: a case study with Microsoft Kinect and Leap Motion

Authors
Carvalho, D; Bessa, M; Magalhaes, L; Carrapatoso, E;

Publication
Universal Access in the Information Society

Abstract
With the thriving of different natural interaction paradigms—such as gesture-based interfaces—it becomes important to understand how these novel interfaces can influence users’ performance when it comes to their age. Recent advances made in human–computer interaction allow us to manipulate digital contents more intuitively; however, no work has yet been reported that systematically evaluates how gestural interfaces may influence the performance of different user groups. Different optical sensors, which allow human body acquisition with reliable accuracy, have been released, and with the appearance of such controllers for gesture recognition, it becomes important to understand if different age-related groups display similar performance levels concerning gestural interaction, or, on the other hand, if specific sensors could induce better results than others when dealing with users of different age brackets. In this article, we compare two gesture-sensing devices (Microsoft Kinect and Leap Motion) using the Fitts’ law model to evaluate target acquisition performance, with relation to three user groups: children, young adults and older adults. This case study involved 60 participants that were asked to perform a simple continuous selection task as quickly and accurately as possible using one of the devices for gestural recognition. Indeed, performance results showed statistically significant differences among the age groups in the selection task accomplished. However, when considering the users’ performance with regard to both input devices compared side by side, there were no significant differences in each group of users. We believe this situation could imply that the device itself might not have influenced the users’ performance, but actually the users’ age might. The participants feedback was interesting on account of their behaviors and preferences: Although there are no significant differences in performance, there could be when it comes to user preference. © 2017 Springer-Verlag Berlin Heidelberg