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EmoLand: Utilizing narrative animations, multilevel games, and affective computing to foster emotional development in children with autism spectrum disorder

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES 2025年 199卷

作者： Fan, Min Jin, Sheng Fan, Jianyu Guo, Wenchen Chen, Xuxin Commun Univ China Sch Animat & Digital Arts 1 Dingfuzhuang East St Beijing 100024 Peoples R China Commun Univ Zhejiang Sch Animat & Digital Arts 998 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China Macau Univ Sci & Technol Fac Humanities & Arts Taipa Macao Peoples R China Simon Fraser Univ Sch Interact Arts & Technol 250-13450 102 Ave Surrey BC V3T 0A3 Canada Peking Univ Sch Software & Microelect 5 YiHeYuan Rd Beijing 100871 Peoples R China Shanghai Dianji Univ Sch Elect Informat Engn 300 ShuiHua Rd Shanghai 201306 Peoples R China

Emotional skills are crucial for a child's success, though children with autism spectrum disorder (ASD) face challenges in understanding social contexts, as well as recognizing and expressing facial expressions. We present EmoLand, a web-based interactive system that utilizes narrative animations, multilevel games, and artificial intelligence (AI) affective computing to instruct children with ASD about neurotypical emotions and facial expressions in social contexts. The paper sets out the iterative design process used, which was informed by ASD intervention theories, empirical studies focused on children with ASD and their educators (tutors, therapists, parents), and multiple discipline expert inputs. A preliminary evaluation involving twelve children with ASD (aged 4 to 7 years) and five tutors suggests that EmoLand is a viable teaching aid for ASD educators and effectively assists children in learning and applying generalized knowledge about emotions and facial expressions. In this paper, we discuss the design approach and key lessons learned for creating interactive AI systems for children with ASD and explore beneficial considerations for enhancing emotional development.

关键词： Animations Games affective computing Emotional development Children with autism spectrum disorder (ASD)

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Different adaptation error types in affective computing have different effects on user experience: A Wizard-of-Oz study

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES 2025年 196卷

作者： Hossain, Mohammad Sohorab Sowers, Alexandria Fong Clapp, Joshua Dean Novak, Vesna Dominika Univ Cincinnati Dept Elect & Comp Engn 2851 Woodside Dr Cincinnati OH 45223 USA Univ Wyoming Dept Psychol 1000 E Univ Ave Laramie WY 82071 USA

In affective computing, classification algorithms are commonly used to recognize users' psychological states and adapt the technology's behavior accordingly. The accuracy of such classification and adaptation is never perfect, although previous work has shown that user experience improves as the classification/adaptation accuracy increases. This study demonstrates that, apart from accuracy rate, user experience with affective computing technologies also depends on the type of errors made in classification/adaptation. Participants (N = 97) interacted with a modified Multi-Attribute Task Battery scenario whose difficulty level was adapted every 60 s based on participant preferences. Each participant experienced three difficulty adaptation accuracies (70 %, 80 %, 90 %) using a Wizard of Oz paradigm where the "affect-aware" adaptation deviated from the user's preference for a predefined percentage of the time. Participants were randomized into five groups corresponding to different adaptation error types (ranging from "small temporary difficulty change in a nonpreferred direction" to "immediate irreversible increase to overwhelming difficulty"). Self-report measures of user experience included the NASA Task Load Index, Intrinsic Motivation Inventory, and an ad-hoc relative liking scale. Results indicated significant effects of adaptation accuracy on multiple self-report outcomes as well as interaction effects between accuracy and error type on nearly all self-report outcomes. Participants disliked lower accuracies more if they experienced severe adaptation errors. User experience was also different when adaptation errors led to very low difficulty than when they led to very high difficulty. Finally, user experience questionnaires are not necessarily reliable if users do not have prior experience with affective computing technologies. Overall, the study emphasizes that affective computing researchers should not focus exclusively on classification accuracy rates obtained

关键词： affective computing Difficulty adaptation Human factors Physiological computing Psychophysiology User experience

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Advancing Emotionally Aware Child-Robot Interaction with Biophysical Data and Insight-Driven affective computing

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SENSORS 2025年第4期25卷 1161-1161页

作者： Faria, Diego Resende Godkin, Amie Louise Ayrosa, Pedro Paulo da Silva Univ Hertfordshire Sch Phys Engn & Comp Sci Hatfield AL10 9AB England Turning Point Salford LE1 3QL Lancs England Univ Estadual Londrina LABTED NEAD BR-86057970 Londrina Brazil

This paper investigates the integration of affective computing techniques using biophysical data to advance emotionally aware machines and enhance child-robot interaction (CRI). By leveraging interdisciplinary insights from neuroscience, psychology, and artificial intelligence, the study focuses on creating adaptive, emotion-aware systems capable of dynamically recognizing and responding to human emotional states. Through a real-world CRI pilot study involving the NAO robot, this research demonstrates how facial expression analysis and speech emotion recognition can be employed to detect and address negative emotions in real time, fostering positive emotional engagement. The emotion recognition system combines handcrafted and deep learning features for facial expressions, achieving an 85% classification accuracy during real-time CRI, while speech emotions are analyzed using acoustic features processed through machine learning models with an 83% accuracy rate. Offline evaluation of the combined emotion dataset using a Dynamic Bayesian Mixture Model (DBMM) achieved a 92% accuracy for facial expressions, and the multilingual speech dataset yielded 98% accuracy for speech emotions using the DBMM ensemble. Observations from psychological and technological aspects, coupled with statistical analysis, reveal the robot's ability to transition negative emotions into neutral or positive states in most cases, contributing to emotional regulation in children. This work underscores the potential of emotion-aware robots to support therapeutic and educational interventions, particularly for pediatric populations, while setting a foundation for developing personalized and empathetic human-machine interactions. These findings demonstrate the transformative role of affective computing in bridging the gap between technological functionality and emotional intelligence across diverse domains.

关键词： emotion-aware technology affective computing child-robot interaction

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Design and Analysis of a Closed-Loop Emotion Regulation System Based on Multimodal affective computing and Emotional Markov Chain

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第4期55卷 2426-2437页

作者： Wang, Xingchao Li, Chen-Zhong Sun, Zhenglong Xu, Yangsheng Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Guangdong Peoples R China Chinese Univ Hong Kong Sch Med Biooptoelect & Biosensors Lab Shenzhen 518172 Guangdong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Guangdong Peoples R China

In our daily lives, emotions are extremely important. However, predicting and regulating emotion is still a critical problem to be solved in the research of the human-computer interaction (HCI). In this study, we explore this problem using a regulation method. A novel multimodal affective computing algorithm is proposed and implemented in an emotion regulation system. The selection of music stimuli and the modeling of dynamic emotions are done using emotional Markov chains. This regulation system can monitor the user's emotion and play music, selected by the regulation policy until the user can maintain the desired emotion. Our system was verified by two experiments. In the first experiment, by predicting the participants' affective states, we tested the precision of our multimodal affective computing system. In the second experiment, we tested the regulation algorithm embedded in a closed-loop regulation system by comparing it with playing music without feedback. The results suggest that participants can regulate and maintain the desired affective state by using the emotion regulation system.

关键词： affective computing emotion regulation emotional Markov chain human-computer interaction (HCI) affective computing emotion regulation emotional Markov chain human-computer interaction (HCI)

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Domain adaptation for bias mitigation in affective computing: use cases for facial emotion recognition and sentiment analysis systems

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DISCOVER APPLIED SCIENCES 2025年第4期7卷 1-28页

作者： Singhal, Peeyush Gokhale, Sai Shah, Arva Jain, Deepak Kumar Walambe, Rahee Ekart, Aniko Kotecha, Ketan Symbiosis Int Symbiosis Inst Technol SIT Pune 412115 India Symbiosis Int Symbiosis Ctr Appl Artificial Intelligence SCAAI Pune 412115 India Dalian Univ Technol Dalian 610101 Peoples R China Aston Ctr Artificial Intelligence Res & Applicat Birmingham England Peoples Friendship Univ Russia Named Patrice Lumum 6 Miklukho Maklaya Str Moscow 117198 Russia

This study presents a novel approach using the Demographic Bias Mitigation Framework (DBMF), which leverages Domain Adaptation (DA) to mitigate demographic biases in affective computing AI systems. Biases in technologies like sentiment analysis (SA) and facial emotion recognition (FER) can result in adverse societal impacts and diminished trust. Unlike typical DA strategies, which adapt data from sources to targets, the DBMF innovatively adapts less biased target data to biased source domains to address gender bias in Natural Language Processing (NLP-SA) and racial bias in Computer Vision (CV-FER) tasks. Statistical methods and fairness metrics confirm the framework's effectiveness in reducing bias while preserving task performance. Notably, for the CV-FER task, the DBMF achieves state-of-the-art accuracy for facial emotion recognition on a widely used dataset-SFEW2.0-marking a significant advancement in this domain. The framework's ability to handle diverse tasks, domains, and bias types highlights its potential as a unified solution for bias mitigation. Additionally, the integration of Elastic Weight Consolidation (EWC) ensures the retention of task performance across domains, further reinforcing the framework's robustness. These findings emphasize the DBMF's and DA's critical role in fostering fairness, reliability, and trustworthiness in AI-driven affective computing systems.

关键词： Domain adaptation Bias mitigation affective computing Sentiment analysis Facial emotion recognition Deep learning

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Improving care for amyotrophic lateral sclerosis with artificial intelligence and affective computing

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JOURNAL OF THE NEUROLOGICAL SCIENCES 2025年 468卷 123328-123328页

作者： Garbey, Marc Lesport, Quentin Oztosun, Guelsen Ghodasara, Veda Kaminski, Henry J. Bayat, Elham George Washington Univ Sch Med & Hlth Sci Dept Surg Washington DC USA Care Constitut Corp Houston TX USA Univ La Rochelle Lab Sci Ingn Environm LaSIE UMR CNRS 7356 La Rochelle France George Washington Univ Sch Med & Hlth Sci Dept Neurol & Rehabil Med Washington DC USA George Washington Univ Sch Med & Hlth Sci Dept Psychiat Washington DC USA

Background: Patients with ALS often face difficulties expressing emotions due to impairments in facial expression, speech, body language, and cognitive function. This study aimed to develop non-invasive AI tools to detect and quantify emotional responsiveness in ALS patients, providing objective insights. Improved understanding of emotional responses could enhance patient-provider communication, telemedicine effectiveness, and clinical trial outcome measures. Methods: In this preliminary exploratory study, fourteen patients with ALS had audio recordings performed during routine clinic visits while wearing a wireless pulse oximeter. Emotion-triggering questions related to symptom progression, breathing, mobility, feeding tube, and financial burden were randomly asked. The same questions were posed in separate psychiatric evaluations. Natural language processing (NLP) was used to analyze transcriptions, topic classifications, sentiment, and emotional states, combining pulse and speech data. AI- generated reports summarized the findings. Results: Pulse alterations consistent with emotional arousal were identified, with longer consultations and positive communication reducing pulse fluctuations. Financial concerns triggered the strongest emotional response, while discussions about breathing, mobility, and feeding tube increased anxiety. AI-generated reports prioritized patient concerns and streamlined documentation for providers. Conclusions: This study introduces a novel approach to linking pulse and speech analysis to evaluate emotional responses in ALS patients. AI and affective computing provide valuable insights into emotional responses and disease progression, with potential applications for other neurological disorders. This approach could augment clinical trial outcomes by offering a more comprehensive view of patient well-being.

关键词： Telemedicine Amyotrophic lateral sclerosis Natural language processing Generative language affective computing Signal analysis Clinical trial

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Research on the Influencing Factors of Smart Cockpit Design Requirements under the Perspective of affective computing Technology--Take the example of online taxi 24

Research on the Influencing Factors of Smart Cockpit Design ...

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Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Chendong Sun Yuxuan Fang Peiping Wu School of Industrial Design China Academy of Art Hangzhou Zhejiang China School of Design Innovation China Academy of Art Hangzhou Zhejiang China

ISBN: (纸本)9798400709753

Based on Kano analysis, this study explores the influencing factors of affective computing technology in smart cockpit design, and takes online taxi as an example. Firstly, through the KJ method and kano to qualitatively analyze the interview data of online taxi drivers, formulate the demand scale of affective computing intervention, analyze and summarize the user demand of both drivers and passengers, determine the cockpit demand with high demand degree, and combine the characteristics of affective computing technology to reveal the importance and influencing factors of the affective computing technology in the intelligent cockpit design. The study found that the user's emotional experience of the cockpit design is of great significance in the online car industry, and the affective computing technology can enhance the user's emotional experience through personalization and emotion recognition. Meanwhile, the study also found the factors affecting the application of affective computing technology in smart cockpit design, including technology maturity, user acceptance, and cost-effectiveness. Finally, this study puts forward suggestions and outlooks on the application of smart cockpit design and affective computing technology in the online car rental industry, which provides a reference for research and practice in related fields.

关键词： affective computing

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Beyond Emotions: affective computing, VR, and Robotics in Mutual Awareness and Safe Interaction

Beyond Emotions: Affective Computing, VR, and Robotics in Mu...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Paulo Menezes University of Coimbra | ISR-UC Coimbra Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

This talk explores the evolution of human-machine interaction, emphasizing affective computing for enhanced user experiences. Beyond basic emotion detection, it introduces “emotional activations” as crucial for intuitive, engaging systems. Humans naturally respond with attraction or rejection to stimuli based on emotions, guiding interaction design toward simplicity and fidelity to foster trust and reduce frustration. This principle has influenced game-based approaches in VR/AR and robotics, enhancing engagement in learning, therapy, and *** is key to interactive systems, mirroring human-animal relationships where predictability and mutual awareness ensure safe coexistence. Just as pets recognize human presence and adapt, robots must perceive human cuessuch as gaze and body languageand express internal states. The talk presents applications in therapeutic and industrial contexts, emphasizing mutual awareness as a foundation for safer, more trustworthy, and engaging intelligent systems

关键词： Robots Robot sensing systems Service robots affective computing Industries Technological innovation Research and development Medical services Interactive systems Intelligent systems

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Experimenting with affective computing Models in Video Interviews with Spanish-Speaking Older Adults

Experimenting with Affective Computing Models in Video Inter...

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IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Josep López Camuñas Cristina Bustos Yanjun Zhu Raquel Ros Agata Lapedriza Universitat Oberta de Catalunya Northeastern University PAL Robotics

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Understanding emotional signals in older adults is crucial for designing virtual assistants that support their well-being. However, existing affective computing models often face significant limitations: (1) limited availability of datasets representing older adults, especially in non-English-speaking populations, and (2) poor generalization of models trained on younger or homogeneous demographics. To address these gaps, this study evaluates state-of-the-art affective computing models-including facial expression recognition, text sentiment analysis, and smile detection-using videos of older adults interacting with either a person or a virtual avatar. As part of this effort, we introduce a novel dataset featuring Spanish-speaking older adults engaged in human-to-human video interviews. Through three comprehensive analyses, we investigate (1) the alignment between human-annotated labels and automatic model outputs, (2) the relationships between model outputs across different modalities, and (3) individual variations in emotional signals. Using both the Wizard of Oz (WoZ) dataset and our newly collected dataset, we uncover limited agreement between human annotations and model predictions, weak consistency across modalities, and significant variability among individuals. These findings high-light the shortcomings of generalized emotion perception models and emphasize the need of incorporating personal variability and cultural nuances into future systems.

关键词： Analytical models affective computing Text recognition Computational modeling Face recognition Predictive models Cultural differences Older adults Interviews Videos

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affective-computing-Driven Personalized Display of Cultural Information for Commercial Heritage Architecture

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3459-3459页

作者： Hu, Huimin Wan, Yaxin Tang, Khang Yeu Li, Qingyue Wang, Xiaohui Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Tsinghua Univ Dept Phys Beijing 100084 Peoples R China Creat Lab Esri 380 New York St Redlands CA 92373 USA

The display methods for traditional cultural heritage lack personalization and emotional interaction, making it difficult to stimulate the public's deep cultural awareness. This is especially true in commercialized historical districts, where cultural value is easily overlooked. Balancing cultural value and commercial value in information display has become one of the challenges that needs to be addressed. To solve the above problems, this article focuses on the identification of deep cultural values and the optimization of the information display in Beijing's Qianmen Street, proposing a framework for cultural information mining and display based on affective computing and large language models. The pre-trained models QwenLM and RoBERTa were employed to analyze text and image data from user-generated content on social media, identifying users' emotional tendencies toward various cultural value dimensions and quantifying their multilayered understanding of architectural heritage. This study further constructed a multimodal information presentation model driven by emotional feedback, mapping it into virtual reality environments to enable personalized, multilayered cultural information visualization. The framework's effectiveness was validated through an eye-tracking experiment that assessed how different presentation styles impacted users' emotional engagement and cognitive outcomes. The results show that the affective computing and multimodal data fusion approach to cultural heritage presentation accurately captures users' emotions, enhancing their interest and emotional involvement. Personalized presentations of information significantly improve users' engagement, historical understanding, and cultural experience, thereby fostering a deeper comprehension of historical contexts and architectural details.

关键词： affective computing large language model multimodal information fusion user-generated content information presentation

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