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In recent decades, the field of affective computing has made substantial progress in advancing the ability of AI systems to recognize and express affective phenomena, such as affect and emotions, during human-human and human-machine interactions. This paper describes our examination of research at the intersection of multimodal interaction and affective computing, with the objective of observing trends and identifying understudied areas. We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing: ACM International Conference on Multimodal Interaction, AAAC International Conference on Affective Computing and Intelligent Interaction, Annual Meeting of the Association for Computational Linguistics, and Conference on Empirical Methods in Natural Language Processing. We identified 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find that this body of research has primarily focused on enabling machines to recognize and express affect and emotion. However, we find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states. Based on our analysis, we discuss directions to expand the role of affective phenomena in multimodal interaction research., Comment: 4 pages, 4 figures

Agents must monitor their partners' affective states continuously in order to understand and engage in social interactions. However, methods for evaluating affect recognition do not account for changes in classification performance that may occur during occlusions or transitions between affective states. This paper addresses temporal patterns in affect classification performance in the context of an infant-robot interaction, where infants' affective states contribute to their ability to participate in a therapeutic leg movement activity. To support robustness to facial occlusions in video recordings, we trained infant affect recognition classifiers using both facial and body features. Next, we conducted an in-depth analysis of our best-performing models to evaluate how performance changed over time as the models encountered missing data and changing infant affect. During time windows when features were extracted with high confidence, a unimodal model trained on facial features achieved the same optimal performance as multimodal models trained on both facial and body features. However, multimodal models outperformed unimodal models when evaluated on the entire dataset. Additionally, model performance was weakest when predicting an affective state transition and improved after multiple predictions of the same affective state. These findings emphasize the benefits of incorporating body features in continuous affect recognition for infants. Our work highlights the importance of evaluating variability in model performance both over time and in the presence of missing data when applying affect recognition to social interactions., 8 pages, 6 figures, 10th International Conference on Affective Computing and Intelligent Interaction (ACII 2022)

Communication in both human-human and human-robot interac-tion (HRI) contexts consists of verbal (speech-based) and non-verbal(facial expressions, eye gaze, gesture, body pose, etc.) components.The verbal component contains semantic and affective information;accordingly, HRI work on the gesture component so far has focusedon rule-based (mapping words to gestures) and data-driven (deep-learning) approaches to generating speech-paired gestures basedon either semantics or the affective state. Consequently, most ges-ture systems are confined to producing either semantically-linkedor affect-based gesticures. This paper introduces an approach forenabling human-robot communication based on a theory-drivenapproach to generate speech-paired robot gestures using both se-mantic and affective information. Our model takes as input textand sentiment analysis, and generates robot gestures in terms oftheir shape, intensity, and speed.

Affect-aware socially assistive robotics (SAR) tutors have great potential to augment and democratize professional therapeutic interventions for children with autism spectrum disorders (ASD) from different socioeconomic backgrounds. However, the majority of research on SAR for ASD has been on teaching cognitive and/or social skills, not on addressing users' emotional needs for real-world social situations. To bridge that gap, this work aims to develop personalized affect-aware SAR tutors to help alleviate social anxiety and foster social grit-the growth mindset for social skill development-in children with ASD. We propose a novel paradigm to incorporate clinically validated Acceptance and Commitment Training (ACT) with personalized SAR interventions. This work paves the way toward developing personalized affect-aware SAR interventions to support the unique and diverse socio-emotional needs and challenges of children with ASD., Comment: Accepted to ACM/IEEE International Conference on Human-Robot Interaction Workshop on Child-Robot Interaction and Child's Fundamental Rights

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.