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Affect-aware socially assistive robotics (SAR) has shown great potential foraugmenting interventions for children with autism spectrum disorders (ASD).However, current SAR cannot yet perceive the unique and diverse set of atypicalcognitive-affective behaviors from children with ASD in an automatic andpersonalized fashion in long-term (multi-session) real-world interactions. Tobridge this gap, this work designed and validated personalized models ofarousal and valence for children with ASD using a multi-session in-home datasetof SAR interventions. By training machine learning (ML) algorithms withsupervised domain adaptation (s-DA), the personalized models were able to tradeoff between the limited individual data and the more abundant less personaldata pooled from other study participants. We evaluated the effects ofpersonalization on a long-term multimodal dataset consisting of 4 children withASD with a total of 19 sessions, and derived inter-rater reliability (IR)scores for binary arousal (IR = 83%) and valence (IR = 81%) labels betweenhuman annotators. Our results show that personalized Gradient Boosted DecisionTrees (XGBoost) models with s-DA outperformed two non-personalizedindividualized and generic model baselines not only on the weighted average ofall sessions, but also statistically (p < .05) across individual sessions. Thiswork paves the way for the development of personalized autonomous SAR systemstailored toward individuals with atypical cognitive-affective andsocio-emotional needs.

Toward Personalized Affect-Aware Socially Assistive Robot Tutors in Long-Term Interventions for Children with Autism