A Four-Step Method for the Development of an ADHD-VR Digital Game Diagnostic Tool Prototype for Children Using a DL Model

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder among children resulting in disturbances in their daily functioning. Virtual reality (VR) and machine learning technologies, such as deep learning (DL) application, are promising diagnostic tools for ADHD in the near future because VR provides stimuli to replace real stimuli and recreate experiences with high realism. It also creates a playful virtual environment and reduces stress in children. The DL model is a subset of machine learning that can transform input and output data into diagnostic values using convolutional neural network systems. By using a sensitive and specific ADHD-VR diagnostic tool prototype for children with DL model, ADHD can be diagnosed more easily and accurately, especially in places with few mental health resources or where teleconsultation is possible. To date, several virtual reality-continuous performance test (VR-CPT) diagnostic tools have been developed for ADHD; however, they do not include a machine learning or deep learning application. A diagnostic tool development study needs a trustworthy and applicable study design and conduct to ensure the completeness and transparency of the report of the accuracy of the diagnostic tool. The proposed four-step method is a mixed-method research design that combines qualitative and quantitative approaches to reduce bias and collect essential information to ensure the trustworthiness and relevance of the study findings. Therefore, this study aimed to present a brief review of a ADHD-VR digital game diagnostic tool prototype with a DL model for children and the proposed four-step method for its development.

Virtual Reality and Wearable Technologies to Support Adaptive Responding of Children and Adolescents With Neurodevelopmental Disorders: A Critical Comment and New Perspectives

“…Berenguer et al ( 2020 ) systematically reviewed the literature on this specific topic among children and adolescents with ASD and emphasized the effectiveness of AR-based programs to promote and support health, well-being, and quality of life in individuals with ASD. Wiguna et al ( 2020 ) proposed a four-step method as a mixed method research design aimed at combining qualitative and quantitative approaches to reduce bias and collect reliable data. An ADHD-VR digital game diagnostic-tool prototype with a deep learning application for children was successfully developed.…”

A Review on Serious Games for ADHD

“…On the other hand, machine learning can classify the data obtained during the game and increase the accuracy of diagnosis. For example, combining virtual reality and deep learning, processing the obtained data through CNN(convolutional neural network), and according to the results of data processing to determine whether suffering from ADHD [41].…”

A Lean UX Process Model for Virtual Reality Environments Considering ADHD in Pupils at Elementary School in COVID-19 Contingency

“…Machine learning technologies such as Deep Learning (DL) in conjunction with virtual reality have been part of proposals for the generation of diagnostic tools for ADHD. Wiguna et al [ 17 ] proposed a DL model and a diagnostic tool for ADHD-VR for the diagnosis of this disorder, which can be used especially in those places with few resources and where there are no specialized personnel such as doctors, child psychologists, or pediatricians, so that they can follow up this type of disorders and can establish the teleconsultation. This proposal aims to be a digital diagnostic tool under a DL model in order to ensure reliable and early diagnosis of ADHD and provide evidence of the diagnosis to parents and specialists.…”