Evolutionary Algorithms for a Better Gaming Experience in Rehabilitation Robotics
Autor: | Ricardo C. Joaquim, Kleber de Oliveira Andrade, Marcio K. Crocomo, Glauco Augusto de Paula Caurin |
---|---|
Rok vydání: | 2018 |
Předmět: |
030506 rehabilitation
Computer science media_common.quotation_subject 0206 medical engineering Evolutionary algorithm 02 engineering and technology Serious game 03 medical and health sciences User experience design medicine Quality (business) Set (psychology) Rehabilitation robotics media_common business.industry ComputingMethodologies_MISCELLANEOUS Work (physics) ComputingMilieux_PERSONALCOMPUTING Boredom 020601 biomedical engineering Computer Science Applications ALGORITMOS GENÉTICOS Artificial intelligence medicine.symptom 0305 other medical science business |
Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
ISSN: | 1544-3574 |
DOI: | 10.1145/3180657 |
Popis: | This article proposes the use of two evolutionary algorithms (EAs) to the dynamic difficulty adjustment (DDA) of a serious game in the rehabilitation robotics application. DDA occurs in runtime for a better user experience with a game. This approach is used to improve the quality of the game experience and to avoid boredom or frustration for players with severe limitations imposed by pathologies such as stroke, cerebral palsy, and spinal cord injuries. The first EA solves the game adjustment problem, changing the game difficulty according to the player’s skill, and the purpose of the second EA is to adjust the coefficients of the first EA’s objective function so that it can work in a more effective way. To do so, the second EA uses results of game matches against simulated player profiles. The results shows that the presented method was able to identify a set of coefficients that allows the first EA to correctly adjust the difficulty level for all six tested player profiles. |
Databáze: | OpenAIRE |
Externí odkaz: |