DailyConnect: Piloting Interventions of Situation-Based Emotional Understanding in Naturalistic Home Settings for Children with Autism Spectrum Disorder

Abstract

AbstractDailyConnect is a visual-based mobile application that supports children with autism spectrum disorder (ASD) in recalling memories by reviewing photos through discrete trial training (DTT) to understand situation-based emotions. To assess DailyConnect and its adaptability to a child's characteristics and emotional situations, a pilot study was conducted that included 15 children with ASD and their parents and teachers. The DTT steps—memory recall and situation recognition, emotion recognition, emotion cues, facial expression recognition, and response behavior—were reliable in assessing the understanding of emotional situations when compared before and after the intervention in four categories of emotional situations, namely, happiness, sadness, anger, and fear. The results revealed that DailyConnect improves the understanding of situation-based emotions, particularly negative emotions (e.g., sadness: mean diff = −.687, sig. < .01, T = −3.866, d = 1.006; anger: mean diff = −.952, sig. < .01, T = −6.187, d = .705; fear: mean diff = −.961, sig. < .01, T = −5.522, d = .627); however, its effectiveness varied for children in different emotional situations. Furthermore, subjective feedback from participants and users (parents and teachers) provided insights into design considerations for similar mobile aids.Keywords: Autism spectrum disordersituation-based emotiondiscrete trial trainingmobile application Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Key R&D Program of China under Grant 2020YFC2004100, the Project of Guangzhou Civil Affairs Science and Technology Foundation under Grant 2022MZK02, the Open Research Fund of Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision under Grant GXHIID2201, and by the NSF of China under Grants 61962038 and 62262045.Notes on contributorsChengchen LyuChengchen Lyu received her BS from East China Normal University, and received her MS degree from the University of Auckland. She is now a PhD candidate with the Institute of Software Chinese Academy of Sciences. Her research interests include human–computer interaction and multimodal learning.Hui ChenHui Chen received her PhD degree in computer science from the Chinese University of Hong Kong. She is now a professor with Institute of Software Chinese Academy of Sciences. Her research interests include human–computer interaction, affective interaction, haptics, and virtual reality.Tong XuTong Xu received her BS degree from Hefei University of Technology, and received her MS degree from the University of Chinese Academy of Sciences. She is now an Engineer with the Institute of Software Chinese Academy of Sciences, P.R. China. Her research interests include human–computer interaction and affective interaction.Xiaolan PengXiaolan Peng received her BS and MS degrees at the Human Robotic Interaction Lab from the University of Science and Technology Beijing, and received her PhD degree from the Institute of Software Chinese Academy of Sciences, P.R. China. Her research interests include human–computer interaction, affective interaction, and multimodal learning.Faliang HuangFaliang Huang received his PhD degree in data mining from the South China University of Technology, China. His research interests include data mining and artificial intelligence.Hongan WangHongan Wang received PhD degree from the Institute of Software Chinese Academy of Sciences. He has been a full professor and the director of the Beijing Key Lab of Human–Computer Interaction, Institute of Software, Chinese Academy of Sciences. His research interests include human–computer interaction and real-time intelligence.