Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients
Journal
Information
Date Issued
December 2022
DOI
10.3390/info13120564
Abstract
This paper describes a serious game based on a knowledge transfer model using deep reinforcement learning, with an aim to improve the caretakers’ knowledge and abilities in post-stroke care. The iTrain game was designed to improve caregiver knowledge and abilities by providing non-traditional training to formal and informal caregivers who deal with stroke survivors. The methodologies utilized professional medical experiences and real-life evidence data gathered during the duration of the iTrain project to create the scenarios for the game’s deep reinforcement caregiver behavior improvement model, as well as the design of game mechanics, game images and game characters, and gameplay implementation. Furthermore, the results of the game’s direct impact on caregivers (n = 25) and stroke survivors (n = 21) in Lithuania using the Geriatric Depression Scale (GDS) and user experience questionnaire (UEQ) are presented. Both surveys had favorable outcomes, showing the effectiveness of the approach. The GDS scale (score 10) revealed a low number of 28% of individuals depressed, and the UEQ received a very favorable grade of +0.8.
File(s)![Thumbnail Image]()
Name
information-13-00564-v2.pdf
Size
2.1 MB
Format
Adobe PDF
Checksum (MD5)
58a74d090a3e3c8412102d8ef20fc5af