Existing work on the measurements of trust during Human-Robot Interaction (HRI) indicates that psychophysiological behaviours (PBs) have the potential to measure trust. However, we see limited work on the use of multiple PBs in combination to calibrate human’s trust in robots in real-time during HRI. Therefore, this study aims to estimate human trust in robots by examining the differences in PBs between trust and distrust states. It further investigates the changes in PBs across repeated HRI and also explores the potential of machine learning classifiers in predicting trust levels during HRI. We collected participants’ electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) during repeated HRI. The results showed significant differences in HR and SKT between trust and distrust groups and no significant interaction effect of session and decision for all PBs. Random Forest classifier achieved the best accuracy of 68.6% to classify trust, while SKT, HR, BR, and BD were the important features. These findings highlight the value of PBs in measuring trust in real-time during HRI and encourage further investigation of trust measures with PBs in various HRI settings.
{"title":"Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction","authors":"Muneeb Ahmad, Abdullah Alzahrani","doi":"10.1145/3577190.3614148","DOIUrl":"https://doi.org/10.1145/3577190.3614148","url":null,"abstract":"Existing work on the measurements of trust during Human-Robot Interaction (HRI) indicates that psychophysiological behaviours (PBs) have the potential to measure trust. However, we see limited work on the use of multiple PBs in combination to calibrate human’s trust in robots in real-time during HRI. Therefore, this study aims to estimate human trust in robots by examining the differences in PBs between trust and distrust states. It further investigates the changes in PBs across repeated HRI and also explores the potential of machine learning classifiers in predicting trust levels during HRI. We collected participants’ electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) during repeated HRI. The results showed significant differences in HR and SKT between trust and distrust groups and no significant interaction effect of session and decision for all PBs. Random Forest classifier achieved the best accuracy of 68.6% to classify trust, while SKT, HR, BR, and BD were the important features. These findings highlight the value of PBs in measuring trust in real-time during HRI and encourage further investigation of trust measures with PBs in various HRI settings.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135044538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fabio Catania, Tanya Talkar, Franca Garzotto, Benjamin R. Cowan, Thomas F. Quatieri, Satrajit Ghosh
Neurodevelopmental Disorders (NDD) involve developmental deficits in cognition, social interaction, and communication. Despite growing interest, gaps persist in understanding usability, effectiveness, and perceptions of such agents. We organize a workshop focusing on the use of conversational agents with multi-modal capabilities for therapeutic interventions in NDD. The workshop brings together researchers and practitioners to discuss design, evaluation, and ethical considerations. Anticipated outcomes include identifying challenges, sharing advancements, fostering collaboration, and charting future research directions.
{"title":"Multimodal Conversational Agents for People with Neurodevelopmental Disorders","authors":"Fabio Catania, Tanya Talkar, Franca Garzotto, Benjamin R. Cowan, Thomas F. Quatieri, Satrajit Ghosh","doi":"10.1145/3577190.3617133","DOIUrl":"https://doi.org/10.1145/3577190.3617133","url":null,"abstract":"Neurodevelopmental Disorders (NDD) involve developmental deficits in cognition, social interaction, and communication. Despite growing interest, gaps persist in understanding usability, effectiveness, and perceptions of such agents. We organize a workshop focusing on the use of conversational agents with multi-modal capabilities for therapeutic interventions in NDD. The workshop brings together researchers and practitioners to discuss design, evaluation, and ethical considerations. Anticipated outcomes include identifying challenges, sharing advancements, fostering collaboration, and charting future research directions.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135044541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enduring stress can have negative impacts on human health and behavior. Widely used wearable devices are promising for assessing, monitoring and potentially alleviating high stress in daily life. Although numerous automatic stress recognition studies have been carried out in the laboratory environment with high accuracy, the performance of daily life studies is still far away from what the literature has in laboratory environments. Since the physiological signals obtained from these devices are time-series data, Recursive Neural Network (RNN) based classifiers promise better results than other machine learning methods. However, the performance of RNN-based classifiers has not been extensively evaluated (i.e., with several variants and different application techniques) for detecting daily life stress yet. They could be combined with CNN architectures, applied to raw data or handcrafted features. In this study, we created different RNN architecture variants and explored their performance for recognizing daily life stress to guide researchers in the field.
{"title":"Performance Exploration of RNN Variants for Recognizing Daily Life Stress Levels by Using Multimodal Physiological Signals","authors":"Yekta Said Can, Elisabeth André","doi":"10.1145/3577190.3614159","DOIUrl":"https://doi.org/10.1145/3577190.3614159","url":null,"abstract":"Enduring stress can have negative impacts on human health and behavior. Widely used wearable devices are promising for assessing, monitoring and potentially alleviating high stress in daily life. Although numerous automatic stress recognition studies have been carried out in the laboratory environment with high accuracy, the performance of daily life studies is still far away from what the literature has in laboratory environments. Since the physiological signals obtained from these devices are time-series data, Recursive Neural Network (RNN) based classifiers promise better results than other machine learning methods. However, the performance of RNN-based classifiers has not been extensively evaluated (i.e., with several variants and different application techniques) for detecting daily life stress yet. They could be combined with CNN architectures, applied to raw data or handcrafted features. In this study, we created different RNN architecture variants and explored their performance for recognizing daily life stress to guide researchers in the field.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135044666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intent classification is a key task in natural language processing (NLP) that aims to infer the goal or intention behind a user’s query. Most existing intent classification methods rely on supervised deep models trained on large annotated datasets of text-intent pairs. However, obtaining such datasets is often expensive and impractical in real-world settings. Furthermore, supervised models may overfit or face distributional shifts when new intents, utterances, or data distributions emerge over time, requiring frequent retraining. Online learning methods based on user feedback can overcome this limitation, as they do not need access to intents while collecting data and adapting the model continuously. In this paper, we propose a novel multi-armed contextual bandit framework that leverages a text encoder based on a large language model (LLM) to extract the latent features of a given utterance and jointly learn multimodal representations of encoded text features and intents. Our framework consists of two stages: offline pretraining and online fine-tuning. In the offline stage, we train the policy on a small labeled dataset using a contextual bandit approach. In the online stage, we fine-tune the policy parameters using the REINFORCE algorithm with a user feedback-based objective, without relying on the true intents. We further introduce a sliding window strategy for simulating the retrieval of data samples during online training. This novel two-phase approach enables our method to efficiently adapt to dynamic user preferences and data distributions with improved performance. An extensive set of empirical studies indicate that our method significantly outperforms policies that omit either offline pretraining or online fine-tuning, while achieving competitive performance to a supervised benchmark trained on an order of magnitude larger labeled dataset.
{"title":"User Feedback-based Online Learning for Intent Classification","authors":"Kaan Gönç, Baturay Sağlam, Onat Dalmaz, Tolga Çukur, Serdar Kozat, Hamdi Dibeklioglu","doi":"10.1145/3577190.3614137","DOIUrl":"https://doi.org/10.1145/3577190.3614137","url":null,"abstract":"Intent classification is a key task in natural language processing (NLP) that aims to infer the goal or intention behind a user’s query. Most existing intent classification methods rely on supervised deep models trained on large annotated datasets of text-intent pairs. However, obtaining such datasets is often expensive and impractical in real-world settings. Furthermore, supervised models may overfit or face distributional shifts when new intents, utterances, or data distributions emerge over time, requiring frequent retraining. Online learning methods based on user feedback can overcome this limitation, as they do not need access to intents while collecting data and adapting the model continuously. In this paper, we propose a novel multi-armed contextual bandit framework that leverages a text encoder based on a large language model (LLM) to extract the latent features of a given utterance and jointly learn multimodal representations of encoded text features and intents. Our framework consists of two stages: offline pretraining and online fine-tuning. In the offline stage, we train the policy on a small labeled dataset using a contextual bandit approach. In the online stage, we fine-tune the policy parameters using the REINFORCE algorithm with a user feedback-based objective, without relying on the true intents. We further introduce a sliding window strategy for simulating the retrieval of data samples during online training. This novel two-phase approach enables our method to efficiently adapt to dynamic user preferences and data distributions with improved performance. An extensive set of empirical studies indicate that our method significantly outperforms policies that omit either offline pretraining or online fine-tuning, while achieving competitive performance to a supervised benchmark trained on an order of magnitude larger labeled dataset.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135044910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Youngwoo Yoon, Taras Kucherenko, Jieyeon Woo, Pieter Wolfert, Rajmund Nagy, Gustav Eje Henter
Non-verbal behavior is advantageous for embodied agents when interacting with humans. Despite many years of research on the generation of non-verbal behavior, there is no established benchmarking practice in the field. Most researchers do not compare their results to prior work, and if they do, they often do so in a manner that is not compatible with other approaches. The GENEA Workshop 2023 seeks to bring the community together to discuss the major challenges and solutions, and to identify the best ways to progress the field.
{"title":"GENEA Workshop 2023: The 4th Workshop on Generation and Evaluation of Non-verbal Behaviour for Embodied Agents","authors":"Youngwoo Yoon, Taras Kucherenko, Jieyeon Woo, Pieter Wolfert, Rajmund Nagy, Gustav Eje Henter","doi":"10.1145/3577190.3616856","DOIUrl":"https://doi.org/10.1145/3577190.3616856","url":null,"abstract":"Non-verbal behavior is advantageous for embodied agents when interacting with humans. Despite many years of research on the generation of non-verbal behavior, there is no established benchmarking practice in the field. Most researchers do not compare their results to prior work, and if they do, they often do so in a manner that is not compatible with other approaches. The GENEA Workshop 2023 seeks to bring the community together to discuss the major challenges and solutions, and to identify the best ways to progress the field.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We focus on a largely overlooked but crucial modality for parent-child interaction analysis: physical contact. In this paper, we provide a feasibility study to automatically detect contact between a parent and child from videos. Our multimodal CNN model uses a combination of 2D pose heatmaps, body part heatmaps, and cropped images. Two datasets (FlickrCI3D and YOUth PCI) are used to explore the generalization capabilities across different contact scenarios. Our experiments demonstrate that using 2D pose heatmaps and body part heatmaps yields the best performance in contact classification when trained from scratch on parent-infant interactions. We further investigate the influence of proximity on our classification performance. Our results indicate that there are unique challenges in parent-infant contact classification. Finally, we show that contact rates from aggregating frame-level predictions provide decent approximations of the true contact rates, suggesting that they can serve as an automated proxy for measuring the quality of parent-child interactions. By releasing the annotations for the YOUth PCI dataset and our code1, we encourage further research to deepen our understanding of parent-infant interactions and their implications for attachment and development.
{"title":"Embracing Contact: Detecting Parent-Infant Interactions","authors":"Metehan Doyran, Ronald Poppe, Albert Ali Salah","doi":"10.1145/3577190.3614147","DOIUrl":"https://doi.org/10.1145/3577190.3614147","url":null,"abstract":"We focus on a largely overlooked but crucial modality for parent-child interaction analysis: physical contact. In this paper, we provide a feasibility study to automatically detect contact between a parent and child from videos. Our multimodal CNN model uses a combination of 2D pose heatmaps, body part heatmaps, and cropped images. Two datasets (FlickrCI3D and YOUth PCI) are used to explore the generalization capabilities across different contact scenarios. Our experiments demonstrate that using 2D pose heatmaps and body part heatmaps yields the best performance in contact classification when trained from scratch on parent-infant interactions. We further investigate the influence of proximity on our classification performance. Our results indicate that there are unique challenges in parent-infant contact classification. Finally, we show that contact rates from aggregating frame-level predictions provide decent approximations of the true contact rates, suggesting that they can serve as an automated proxy for measuring the quality of parent-child interactions. By releasing the annotations for the YOUth PCI dataset and our code1, we encourage further research to deepen our understanding of parent-infant interactions and their implications for attachment and development.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most existing audio-text emotion recognition studies have focused on the computational modeling aspects, including strategies for fusing the modalities. An area that has received less attention is understanding the role of proper temporal synchronization between the modalities in the model performance. This study presents a transformer-based model designed with a word-chunk concept, which offers an ideal framework to explore different strategies to align text and speech. The approach creates chunks with alternative alignment strategies with different levels of dependency on the underlying lexical boundaries. A key contribution of this study is the multi-scale chunk alignment strategy, which generates random alignments to create the chunks without considering lexical boundaries. For every epoch, the approach generates a different alignment for each sentence, serving as an effective regularization method for temporal dependency. Our experimental results based on the MSP-Podcast corpus indicate that providing precise temporal alignment information to create the audio-text chunks does not improve the performance of the system. The attention mechanisms in the transformer-based approach are able to compensate for imperfect synchronization between the modalities. However, using exact lexical boundaries makes the system highly vulnerable to missing modalities. In contrast, the model trained with the proposed multi-scale chunk regularization strategy using random alignment can significantly increase its robustness against missing data and remain effective, even under a single audio-only emotion recognition task. The code is available at: https://github.com/winston-lin-wei-cheng/MultiScale-Chunk-Regularization
{"title":"Enhancing Resilience to Missing Data in Audio-Text Emotion Recognition with Multi-Scale Chunk Regularization","authors":"Wei-Cheng Lin, Lucas Goncalves, Carlos Busso","doi":"10.1145/3577190.3614110","DOIUrl":"https://doi.org/10.1145/3577190.3614110","url":null,"abstract":"Most existing audio-text emotion recognition studies have focused on the computational modeling aspects, including strategies for fusing the modalities. An area that has received less attention is understanding the role of proper temporal synchronization between the modalities in the model performance. This study presents a transformer-based model designed with a word-chunk concept, which offers an ideal framework to explore different strategies to align text and speech. The approach creates chunks with alternative alignment strategies with different levels of dependency on the underlying lexical boundaries. A key contribution of this study is the multi-scale chunk alignment strategy, which generates random alignments to create the chunks without considering lexical boundaries. For every epoch, the approach generates a different alignment for each sentence, serving as an effective regularization method for temporal dependency. Our experimental results based on the MSP-Podcast corpus indicate that providing precise temporal alignment information to create the audio-text chunks does not improve the performance of the system. The attention mechanisms in the transformer-based approach are able to compensate for imperfect synchronization between the modalities. However, using exact lexical boundaries makes the system highly vulnerable to missing modalities. In contrast, the model trained with the proposed multi-scale chunk regularization strategy using random alignment can significantly increase its robustness against missing data and remain effective, even under a single audio-only emotion recognition task. The code is available at: https://github.com/winston-lin-wei-cheng/MultiScale-Chunk-Regularization","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jicheng Li, Vuthea Chheang, Pinar Kullu, Eli Brignac, Zhang Guo, Anjana Bhat, Kenneth E. Barner, Roghayeh Leila Barmaki
Autism spectrum disorder (ASD) is a developmental disorder characterized by significant impairments in social communication and difficulties perceiving and presenting communication signals. Machine learning techniques have been widely used to facilitate autism studies and assessments. However, computational models are primarily concentrated on very specific analysis and validated on private, non-public datasets in the autism community, which limits comparisons across models due to privacy-preserving data-sharing complications. This work presents a novel open source privacy-preserving dataset, MMASD as a MultiModal ASD benchmark dataset, collected from play therapy interventions for children with autism. The MMASD includes data from 32 children with ASD, and 1,315 data samples segmented from more than 100 hours of intervention recordings. To promote the privacy of children while offering public access, each sample consists of four privacy-preserving modalities, some of which are derived from original videos: (1) optical flow, (2) 2D skeleton, (3) 3D skeleton, and (4) clinician ASD evaluation scores of children. MMASD aims to assist researchers and therapists in understanding children’s cognitive status, monitoring their progress during therapy, and customizing the treatment plan accordingly. It also inspires downstream social tasks such as action quality assessment and interpersonal synchrony estimation. The dataset is publicly accessible via the MMASD project website.
{"title":"MMASD: A Multimodal Dataset for Autism Intervention Analysis","authors":"Jicheng Li, Vuthea Chheang, Pinar Kullu, Eli Brignac, Zhang Guo, Anjana Bhat, Kenneth E. Barner, Roghayeh Leila Barmaki","doi":"10.1145/3577190.3614117","DOIUrl":"https://doi.org/10.1145/3577190.3614117","url":null,"abstract":"Autism spectrum disorder (ASD) is a developmental disorder characterized by significant impairments in social communication and difficulties perceiving and presenting communication signals. Machine learning techniques have been widely used to facilitate autism studies and assessments. However, computational models are primarily concentrated on very specific analysis and validated on private, non-public datasets in the autism community, which limits comparisons across models due to privacy-preserving data-sharing complications. This work presents a novel open source privacy-preserving dataset, MMASD as a MultiModal ASD benchmark dataset, collected from play therapy interventions for children with autism. The MMASD includes data from 32 children with ASD, and 1,315 data samples segmented from more than 100 hours of intervention recordings. To promote the privacy of children while offering public access, each sample consists of four privacy-preserving modalities, some of which are derived from original videos: (1) optical flow, (2) 2D skeleton, (3) 3D skeleton, and (4) clinician ASD evaluation scores of children. MMASD aims to assist researchers and therapists in understanding children’s cognitive status, monitoring their progress during therapy, and customizing the treatment plan accordingly. It also inspires downstream social tasks such as action quality assessment and interpersonal synchrony estimation. The dataset is publicly accessible via the MMASD project website.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The utilization of drones to assist runners in real-time and post-run remains a promising yet unexplored field within human-drone interaction (HDI). Hence, in my doctoral research, I aim to delve into the concepts and relationships surrounding drones in the context of running, than focusing solely on one specific application. I plan on accomplishing this through a three-stage research plan: 1) investigate the feasibility of drones to support outdoor running research, 2) empathize with runners to assess their preferences and experiences running with drone, and 3) implement and test an interactive running with drone scenario. Each stage has specific objectives and research questions aimed at providing valuable insights into the utilization of drones to support runners. This paper outlines the work conducted during my Ph.D. research along with future plans, with the goal of advancing the knowledge in the field of runner drone interaction.
{"title":"Come Fl.. Run with Me: Understanding the Utilization of Drones to Support Recreational Runners' Well Being","authors":"Aswin Balasubramaniam","doi":"10.1145/3577190.3614228","DOIUrl":"https://doi.org/10.1145/3577190.3614228","url":null,"abstract":"The utilization of drones to assist runners in real-time and post-run remains a promising yet unexplored field within human-drone interaction (HDI). Hence, in my doctoral research, I aim to delve into the concepts and relationships surrounding drones in the context of running, than focusing solely on one specific application. I plan on accomplishing this through a three-stage research plan: 1) investigate the feasibility of drones to support outdoor running research, 2) empathize with runners to assess their preferences and experiences running with drone, and 3) implement and test an interactive running with drone scenario. Each stage has specific objectives and research questions aimed at providing valuable insights into the utilization of drones to support runners. This paper outlines the work conducted during my Ph.D. research along with future plans, with the goal of advancing the knowledge in the field of runner drone interaction.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this talk I will take a neurobiological perspective on human communication, and explore the ways in which visual and auditory channels express common and distinct patterns of information. I will extend this to that ways in which facial and vocal information is processed neurally and how they interact in communication.
{"title":"Multimodal information processing in communication: thenature of faces and voices","authors":"Sophie Scott","doi":"10.1145/3577190.3616523","DOIUrl":"https://doi.org/10.1145/3577190.3616523","url":null,"abstract":"In this talk I will take a neurobiological perspective on human communication, and explore the ways in which visual and auditory channels express common and distinct patterns of information. I will extend this to that ways in which facial and vocal information is processed neurally and how they interact in communication.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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