Xinlong Jiang
Institute of Computing Technology, CAS & Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, China
Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental disorders affecting children. Because the etiology of ADHD is complex and its symptoms are not specific, there is a lack of feasible quantitative diagnostic methods. Pursuing objective and non-invasive detection methods and standards is of great practical significance to prevent the development of the disease. In this study, we aim to address one specific concern about the objectivity and quantification of ADHD diagnosis. Over a year, we iteratively designed and tested WeDA, a scale-driven wearable diagnostic assessment system. This system contains an Android computer machine with a large touchscreen, a suite of 3D printed interactive devices, and six wearable motion sensors. We implement ten diagnostic tasks drawing on the symptoms of ADHD based on DSM-5. The experimental results of classifying children with ADHD and typically developing children and subjective evaluations from doctors, parents, and children validate the effectiveness and acceptability of WeDA.
https://doi.org/10.1145/3313831.3376374
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)