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TD‐P‐31: IDENTIFYING INDIVIDUALS WITH DEMENTIA, MCI AND PHYSICAL IMPAIRMENT DURING GROUP ACTIVITIES USING MACHINE LEARNING ON PHYSICAL CHARACTERISTICS DRAWN FROM SKELETON TRACKING DATA
Author(s) -
Chen Zizui,
Czarnuch Stephen M.,
Dove Erica,
Astell Arlene J.
Publication year - 2019
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2019.06.4342
Subject(s) - artificial intelligence , support vector machine , computer science , session (web analytics) , dementia , classifier (uml) , cluster analysis , python (programming language) , machine learning , medicine , world wide web , disease , pathology , operating system
The lack of visibility to the prompt in Bubble Explode warrants a redesign, as it was used a lot when people noticed it. The findings of this study provide insight into the mechanisms of prompts in digital touchscreen games for people with dementia (i.e., their ability to attract attention and convey purpose). This can inform the inclusion of effective accessibility settings in future software design and other systems delivering prompts.