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Doctoral Symposium on Information and Communication Technologies, 2022, Vol.1647, p.161-172
2022

Details

Autor(en) / Beteiligte
Titel
Artificial Intelligence Applied to Video Game for Detection of Mild Cognitive Impairment
Ist Teil von
  • Doctoral Symposium on Information and Communication Technologies, 2022, Vol.1647, p.161-172
Ort / Verlag
Switzerland: Springer International Publishing AG
Erscheinungsjahr
2022
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • Mild Cognitive Impairment (MCI) is considered the fisrt stage of dementia, where the scientific community has a great interest in providing the solution from multidiscplinary areas. Biological, psychological and environmental variables are the cause of MCI and they can be monitored without invasive processes. Abnormal brain aging is a natural cause of MCI which can trigger a complex senile dementia. The computer-aided systems will become in the new technologies for diagnosis and prevention of dementia diseases. Our approach is develop a tool for detection of Mild Cognitive impairment using virtual video games. With the aim of diagnosing MCI, we developed a video game based on missions through Montreal Cognitive Assessment (MoCa) and Mini-Mental State Examination (MMSE) standardized tests. 45 samples were realized between both genders of productive age group. Executive and episodic memory was evaluated through spatial orientation, object recognition, numerical calculation and multi-tasks skills to solve activities of daily life. The AI is an agent player that is used to obtain the time threshold for each mission. The time threshold represents the performance time to be reached by each player in his different age range. The results indicated that female gender spent more time with respect to the AI algorithm for overall missions. In addition, impairments in concentration and executive memory tasks have been determined with increasing age from 35 years onwards.
Sprache
Englisch
Identifikatoren
ISBN: 9783031183461, 3031183460
ISSN: 1865-0929
eISSN: 1865-0937
DOI: 10.1007/978-3-031-18347-8_13
Titel-ID: cdi_springer_books_10_1007_978_3_031_18347_8_13
Format
Schlagworte
ML-Agents, MMSE, MoCA, Pathfinding, Unity

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