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1. Animated Agents’ Facial Emotions: Does the Agent Design Make a Difference?

   https://doi.org/https://doi.org/10.1007/978-3-030-25965-5_2  

   Adamo, N.; Dib, H.N.; Villani, N.J. (January 2019, In: De Paolis L., Bourdot P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2019. Lecture Notes in Computer Science, vol 11613. Springer, Cham)

   The paper reports ongoing research toward the design of multimodal affective pedagogical agents that are effective for different types of learners and applications. In particular, the work reported in the paper investigated the extent to which the type of character design (realistic versus stylized) affects students’ perception of an animated agent’s facial emotions, and whether the effects are moderated by learner characteristics (e.g. gender). Eighty-two participants viewed 10 animation clips featuring a stylized character exhibiting 5 different emotions, e.g. happiness, sadness, fear, surprise and anger (2 clips per emotion), and 10 clips featuring a realistic character portraying the same emotional states. The participants were asked to name the emotions and rate their sincerity, intensity, and typicality. The results indicated that for recognition, participants were slightly more likely to recognize the emotions displayed by the stylized agent, although the difference was not statistically significant. The stylized agent was on average rated significantly higher for facial emotion intensity, whereas the differences in ratings for typicality and sincerity across all emotions were not statistically significant. A significant difference in ratings was shown in regard to sadness (within typicality), happiness (within sincerity), fear, anger, sadness and happiness (within intensity) with the stylized agent rated higher. Gender was not a significant correlate across all emotions or for individual emotions. 

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2. Perception of Emotion in Torso and Arm Movements on Humanoid Robot Quori

   https://doi.org/10.1145/3434074.3447129  

   Kaushik, Roshni; Simmons, Reid (March 2021, Companion of the International Conference on Human-Robot Interaction)

   Displaying emotional states is an important part of nonverbal communication that can facilitate successful interactions. Facial expressions have been studied for their emotional expression, but this work looks at the capacity of body movements to convey different emotions. This work first generates a large set of nonverbal behaviors with a variety of torso and arm properties on a humanoid robot, Quori. Participants in a user study evaluated how much each movement displayed each of eight different emotions. Results indicate that specific movement properties are associated with particular emotions; such as leaning backward and arms held high displaying surprise and leaning forward displaying sadness. Understanding the emotions associated with certain movements can allow for the design of more appropriate behaviors during interactions with humans and could improve people’s perception of the robot. 

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3. Objectively Quantifying Pediatric Psychiatric Severity Using Artificial Intelligence, Voice Recognition Technology, and Universal Emotions: Pilot Study for Artificial Intelligence-Enabled Innovation to Address Youth Mental Health Crisis

   https://doi.org/10.2196/51912  

   Caulley, D; Alemu, Y; Burson, S; Cárdenas, B; Girmaw, A; Kottmyer, C; Aeschbach, L; Cheungvivatpant, B; Sezgin, E (January 2023, JMIR Research Protocols)

   JMIR (Ed.)

   Psychotherapy, particularly for youth, is a pressing challenge in the health care system. Traditional methods are resource-intensive, and there is a need for objective benchmarks to guide therapeutic interventions. Automated emotion detection from speech, using artificial intelligence, presents an emerging approach to address these challenges. Speech can carry vital information about emotional states, which can be used to improve mental health care services, especially when the person is suffering. ObjectiveThis study aims to develop and evaluate automated methods for detecting the intensity of emotions (anger, fear, sadness, and happiness) in audio recordings of patients’ speech. We also demonstrate the viability of deploying the models. Our model was validated in a previous publication by Alemu et al with limited voice samples. This follow-up study used significantly more voice samples to validate the previous model. MethodsWe used audio recordings of patients, specifically children with high adverse childhood experience (ACE) scores; the average ACE score was 5 or higher, at the highest risk for chronic disease and social or emotional problems; only 1 in 6 have a score of 4 or above. The patients’ structured voice sample was collected by reading a fixed script. In total, 4 highly trained therapists classified audio segments based on a scoring process of 4 emotions and their intensity levels for each of the 4 different emotions. We experimented with various preprocessing methods, including denoising, voice-activity detection, and diarization. Additionally, we explored various model architectures, including convolutional neural networks (CNNs) and transformers. We trained emotion-specific transformer-based models and a generalized CNN-based model to predict emotion intensities. ResultsThe emotion-specific transformer-based model achieved a test-set precision and recall of 86% and 79%, respectively, for binary emotional intensity classification (high or low). In contrast, the CNN-based model, generalized to predict the intensity of 4 different emotions, achieved test-set precision and recall of 83% for each. ConclusionsAutomated emotion detection from patients’ speech using artificial intelligence models is found to be feasible, leading to a high level of accuracy. The transformer-based model exhibited better performance in emotion-specific detection, while the CNN-based model showed promise in generalized emotion detection. These models can serve as valuable decision-support tools for pediatricians and mental health providers to triage youth to appropriate levels of mental health care services. 

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4. Understanding the Effect of Speed on Human Emotion Perception in Mediated Social Touch Using Voice Coil Actuators

   https://doi.org/10.3389/fcomp.2022.826637  

   Zhu, Xin; Feng, Tiantian; Culbertson, Heather (March 2022, Frontiers in Computer Science)

   Touch as a modality in social communication has been getting more attention with recent developments in wearable technology and an increase in awareness of how limited physical contact can lead to touch starvation and feelings of depression. Although several mediated touch methods have been developed for conveying emotional support, the transfer of emotion through mediated touch has not been widely studied. This work addresses this need by exploring emotional communication through a novel wearable haptic system. The system records physical touch patterns through an array of force sensors, processes the recordings using novel gesture-based algorithms to create actuator control signals, and generates mediated social touch through an array of voice coil actuators. We conducted a human subject study ( N = 20) to understand the perception and emotional components of this mediated social touch for common social touch gestures, including poking, patting, massaging, squeezing, and stroking. Our results show that the speed of the virtual gesture significantly alters the participants' ratings of valence, arousal, realism, and comfort of these gestures with increased speed producing negative emotions and decreased realism. The findings from the study will allow us to better recognize generic patterns from human mediated touch perception and determine how mediated social touch can be used to convey emotion. Our system design, signal processing methods, and results can provide guidance in future mediated social touch design. 

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5. Exploring the Social Influence of Virtual Humans Unintentionally Conveying Conflicting Emotions

   https://doi.org/10.1109/VR55154.2023.00072  

   Choudhary, Zubin; Norouzi, Nahal; Erickson, Austin; Schubert, Ryan; Bruder, Gerd; Welch, Gregory F. (March 2023, 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR))

   The expression of human emotion is integral to social interaction, and in virtual reality it is increasingly common to develop virtual avatars that attempt to convey emotions by mimicking these visual and aural cues, i.e. the facial and vocal expressions. However, errors in (or the absence of) facial tracking can result in the rendering of incorrect facial expressions on these virtual avatars. For example, a virtual avatar may speak with a happy or unhappy vocal inflection while their facial expression remains otherwise neutral. In circumstances where there is conflict between the avatar's facial and vocal expressions, it is possible that users will incorrectly interpret the avatar's emotion, which may have unintended consequences in terms of social influence or in terms of the outcome of the interaction. In this paper, we present a human-subjects study (N = 22) aimed at understanding the impact of conflicting facial and vocal emotional expressions. Specifically we explored three levels of emotional valence (unhappy, neutral, and happy) expressed in both visual (facial) and aural (vocal) forms. We also investigate three levels of head scales (down-scaled, accurate, and up-scaled) to evaluate whether head scale affects user interpretation of the conveyed emotion. We find significant effects of different multimodal expressions on happiness and trust perception, while no significant effect was observed for head scales. Evidence from our results suggest that facial expressions have a stronger impact than vocal expressions. Additionally, as the difference between the two expressions increase, the less predictable the multimodal expression becomes. For example, for the happy-looking and happy-sounding multimodal expression, we expect and see high happiness rating and high trust, however if one of the two expressions change, this mismatch makes the expression less predictable. We discuss the relationships, implications, and guidelines for social applications that aim to leverage multimodal social cues. 

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