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

翻译：社会辅助机器人(SAR)在加强针对自闭症谱系障碍儿童(ASD)的干预措施方面显示出巨大的潜力。然而,目前SAR尚不能以自动和个性化的方式,在长期(多科)现实世界互动中,以自动和个性化的方式,看待有自闭症儿童独特的认知-情感行为的独特和多样化。为了缩小这一差距,这项工作利用SAR干预的多部分内部数据集,设计并验证了对患有自闭症儿童(SSD)的刺激和价值的个性化模型。通过培训机器学习(ML)算法,并监督个人领域适应(S-DA),个人化模型能够将有限的个人数据与其他研究参与者的更丰富的个人数据进行交换。我们评估了由4名自闭症儿童(ASSD)长期(共19次课程)组成的个人自闭式模型(IR=83%)和价值(IR=81%)的机器学习算法(MLM)在人类自闭域调领域适应(SDAR)方法之间,我们的结果显示,个人自闭式的自我智能系统(SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-S-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SD-SD-SAL-SAL-SD-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-S-SAL-S-S-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SD-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-S-S-S-S-SAL-SAL-SAL-SAL-SAL-SAL-SAL-SAL-S