Distinguishing aging clusters and mobile devices by hand-wrist articulation: a case of study

• Daniel Palacios-Alonso ^[1] ; Carlos Lázaro-Carrascosa ^[1] ; Raquel Más García ^[1] ; José Manuel Ferrández Vicente ^[2] ; Andrés Gómez-Rodella ^[3] ; Pedro Gómez-Vilda ^[3]
  1. [1] Universidad Rey Juan Carlos

     Universidad Rey Juan Carlos

     Madrid, España

     
  2. [2] Universidad Politécnica de Cartagena

     Universidad Politécnica de Cartagena

     Cartagena, España

     
  3. [3] Universidad Politécnica de Madrid

     Universidad Politécnica de Madrid

     Madrid, España

     

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• Localización: Understanding the Brain Function and Emotions: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019 Almería, Spain, June 3–7, 2019 Proceedings, Part I / José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Francisco Javier Toledo Moreo (dir. congr.), Hojjat Adeli (dir. congr.), 2019, ISBN 978-3-030-19591-5, págs. 11-16
• Idioma: inglés
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• Resumen

  • Nowadays, video games are not considered only mere hobbies. The use of these tools is increased in last years. On the other hand,gamification and usability techniques have empowered the improvement of communication between player/user and Aided Communication Devices (ACD). New ACDs provide a novel approach to capture biomechanical features or indicators. This work consists of a novel methodology development to capture biomechanical indicators throughout multiplatform video games. The present work has an exploratory nature to measure, hand-wrist articulation features estimated from the smartphone’saccelerometer. The intention of the study is to answer some hypothesis for instance, if the device is crucial to evaluate player’s movement capabilities or if the age of the person as a key biomarker. Once these indicators have been tested, it will be able to use them in studies of neurodegenerative diseases, where involuntary tremor is one of the most important observable correlates.