Virtual Reality-Based Attention Prediction Model in Gaming for Autistic Children

Abstract

Nowadays, virtual reality (VR) has emerged as a successful new therapeutic strategy in a variety of sectors of the health profession, including rehabilitation, the promotion of inpatients' emotional wellness, diagnostics, surgeon training and mental health therapy. This study develops a new model VRAPMG for children with ASD with the following steps that consider 3D gaming. In this work, the face image is considered to evaluate the attention of the children. In the data acquisition, the input face image is converted into a noncoloured image called a greyscale image. The preprocessing phase is carried out with a median filter and Viola–Jones (VJ) algorithm–based face detection is carried out. Then, the improved active shape model (ASM), shape local binary texture (SLBT) and eye position localization–based features are extracted. In the detection phase, DMO and Bi-GRU models are combined to form the hybrid classification model. Then, improved SLF is done, and the output is detected. Depending on the detected emotions, it is determined whether the children are attentive or not via entropy-based attention prediction.