Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344697
Yoren Gaffary, Jean-Claude Martin, M. Ammi
The use of virtual avatars, through facial or gestural expressions, is considered to be a main support for affective communication. Currently, different works have studied the potential of a kinesthetic channel for conveying such information. However, they still have not investigated the complementarity between visual and kinesthetic feedback to effectively convey emotion. This paper studies the relation between some emotional dimensions and the visual and kinesthetic modalities. The experimental results show that subjects used visual and kinesthetic feedbacks to evaluate the pleasure and the arousal dimensions, respectively. We also observed a link between the recognition rate of emotions expressed with the visual modality (resp. kinesthetic modality) and the magnitude of that emotion's pleasure dimension (resp. arousal dimension). These different results should help in the selection of feedbacks according to the features of the investigated emotion.
{"title":"Perception of congruent facial and kinesthetic expressions of emotions","authors":"Yoren Gaffary, Jean-Claude Martin, M. Ammi","doi":"10.1109/ACII.2015.7344697","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344697","url":null,"abstract":"The use of virtual avatars, through facial or gestural expressions, is considered to be a main support for affective communication. Currently, different works have studied the potential of a kinesthetic channel for conveying such information. However, they still have not investigated the complementarity between visual and kinesthetic feedback to effectively convey emotion. This paper studies the relation between some emotional dimensions and the visual and kinesthetic modalities. The experimental results show that subjects used visual and kinesthetic feedbacks to evaluate the pleasure and the arousal dimensions, respectively. We also observed a link between the recognition rate of emotions expressed with the visual modality (resp. kinesthetic modality) and the magnitude of that emotion's pleasure dimension (resp. arousal dimension). These different results should help in the selection of feedbacks according to the features of the investigated emotion.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"20 1","pages":"993-998"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84542449","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344561
S. M. Feraru, Dagmar M. Schuller, Björn Schuller
Automatic emotion recognition from speech has matured close to the point where it reaches broader commercial interest. One of the last major limiting factors is the ability to deal with multilingual inputs as will be given in a real-life operating system in many if not most cases. As in real-life scenarios speech is often used mixed across languages more experience will be needed in performance effects of cross-language recognition. In this contribution we first provide an overview on languages covered in the research on emotion and speech finding that only roughly two thirds of native speakers' languages are so far touched upon. We thus next shed light on mis-matched vs matched condition emotion recognition across a variety of languages. By intention, we include less researched languages of more distant language families such as Burmese, Romanian or Turkish. Binary arousal and valence mapping is employed in order to be able to train and test across databases that have originally been labelled in diverse categories. In the result - as one may expect - arousal recognition works considerably better across languages than valence, and cross-language recognition falls considerably behind within-language recognition. However, within-language family recognition seems to provide an `emergency-solution' in case of missing language resources, and the observed notable differences depending on the combination of languages show a number of interesting effects.
{"title":"Cross-language acoustic emotion recognition: An overview and some tendencies","authors":"S. M. Feraru, Dagmar M. Schuller, Björn Schuller","doi":"10.1109/ACII.2015.7344561","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344561","url":null,"abstract":"Automatic emotion recognition from speech has matured close to the point where it reaches broader commercial interest. One of the last major limiting factors is the ability to deal with multilingual inputs as will be given in a real-life operating system in many if not most cases. As in real-life scenarios speech is often used mixed across languages more experience will be needed in performance effects of cross-language recognition. In this contribution we first provide an overview on languages covered in the research on emotion and speech finding that only roughly two thirds of native speakers' languages are so far touched upon. We thus next shed light on mis-matched vs matched condition emotion recognition across a variety of languages. By intention, we include less researched languages of more distant language families such as Burmese, Romanian or Turkish. Binary arousal and valence mapping is employed in order to be able to train and test across databases that have originally been labelled in diverse categories. In the result - as one may expect - arousal recognition works considerably better across languages than valence, and cross-language recognition falls considerably behind within-language recognition. However, within-language family recognition seems to provide an `emergency-solution' in case of missing language resources, and the observed notable differences depending on the combination of languages show a number of interesting effects.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"17 1","pages":"125-131"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84557224","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344578
Temitayo A. Olugbade, N. Bianchi-Berthouze, Nicolai Marquardt, A. Williams
People with chronic musculoskeletal pain would benefit from technology that provides run-time personalized feedback and help adjust their physical exercise plan. However, increased pain during physical exercise, or anxiety about anticipated pain increase, may lead to setback and intensified sensitivity to pain. Our study investigates the possibility of detecting pain levels from the quality of body movement during two functional physical exercises. By analyzing recordings of kinematics and muscle activity, our feature optimization algorithms and machine learning techniques can automatically discriminate between people with low level pain and high level pain and control participants while exercising. Best results were obtained from feature set optimization algorithms: 94% and 80% for the full trunk flexion and sit-to-stand movements respectively using Support Vector Machines. As depression can affect pain experience, we included participants' depression scores on a standard questionnaire and this improved discrimination between the control participants and the people with pain when Random Forests were used.
{"title":"Pain level recognition using kinematics and muscle activity for physical rehabilitation in chronic pain","authors":"Temitayo A. Olugbade, N. Bianchi-Berthouze, Nicolai Marquardt, A. Williams","doi":"10.1109/ACII.2015.7344578","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344578","url":null,"abstract":"People with chronic musculoskeletal pain would benefit from technology that provides run-time personalized feedback and help adjust their physical exercise plan. However, increased pain during physical exercise, or anxiety about anticipated pain increase, may lead to setback and intensified sensitivity to pain. Our study investigates the possibility of detecting pain levels from the quality of body movement during two functional physical exercises. By analyzing recordings of kinematics and muscle activity, our feature optimization algorithms and machine learning techniques can automatically discriminate between people with low level pain and high level pain and control participants while exercising. Best results were obtained from feature set optimization algorithms: 94% and 80% for the full trunk flexion and sit-to-stand movements respectively using Support Vector Machines. As depression can affect pain experience, we included participants' depression scores on a standard questionnaire and this improved discrimination between the control participants and the people with pain when Random Forests were used.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"8 1","pages":"243-249"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79952632","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344658
F. Eyben, Bernd Huber, E. Marchi, Dagmar M. Schuller, Björn Schuller
We demonstrate audEERING's sensAI technology running natively on low-resource mobile devices applied to emotion analytics and speaker characterisation tasks. A showcase application for the Android platform is provided, where au-dEERING's highly noise robust voice activity detection based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) is combined with our core emotion recognition and speaker characterisation engine natively on the mobile device. This eliminates the need for network connectivity and allows to perform robust speaker state and trait recognition efficiently in real-time without network transmission lags. Real-time factors are benchmarked for a popular mobile device to demonstrate the efficiency, and average response times are compared to a server based approach. The output of the emotion analysis is visualized graphically in the arousal and valence space alongside the emotion category and further speaker characteristics.
{"title":"Real-time robust recognition of speakers' emotions and characteristics on mobile platforms","authors":"F. Eyben, Bernd Huber, E. Marchi, Dagmar M. Schuller, Björn Schuller","doi":"10.1109/ACII.2015.7344658","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344658","url":null,"abstract":"We demonstrate audEERING's sensAI technology running natively on low-resource mobile devices applied to emotion analytics and speaker characterisation tasks. A showcase application for the Android platform is provided, where au-dEERING's highly noise robust voice activity detection based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) is combined with our core emotion recognition and speaker characterisation engine natively on the mobile device. This eliminates the need for network connectivity and allows to perform robust speaker state and trait recognition efficiently in real-time without network transmission lags. Real-time factors are benchmarked for a popular mobile device to demonstrate the efficiency, and average response times are compared to a server based approach. The output of the emotion analysis is visualized graphically in the arousal and valence space alongside the emotion category and further speaker characteristics.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"28 1","pages":"778-780"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86693926","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344592
Mohamed Yacine Tsalamlal, Jean-Claude Martin, M. Ammi, A. Tapus, M. Amorim
This study presents an experiment highlighting how participants combine facial expressions and haptic feedback to perceive emotions when interacting with an expressive humanoid robot. Participants were asked to interact with the humanoid robot through a handshake behavior while looking at its facial expressions. Experimental data were examined within the information integration theory framework. Results revealed that participants combined Facial and Haptic cues additively to evaluate the Valence, Arousal, and Dominance dimensions. The relative importance of each modality was different across the emotional dimensions. Participants gave more importance to facial expressions when evaluating Valence. They gave more importance to haptic feedback when evaluating Arousal and Dominance.
{"title":"Affective handshake with a humanoid robot: How do participants perceive and combine its facial and haptic expressions?","authors":"Mohamed Yacine Tsalamlal, Jean-Claude Martin, M. Ammi, A. Tapus, M. Amorim","doi":"10.1109/ACII.2015.7344592","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344592","url":null,"abstract":"This study presents an experiment highlighting how participants combine facial expressions and haptic feedback to perceive emotions when interacting with an expressive humanoid robot. Participants were asked to interact with the humanoid robot through a handshake behavior while looking at its facial expressions. Experimental data were examined within the information integration theory framework. Results revealed that participants combined Facial and Haptic cues additively to evaluate the Valence, Arousal, and Dominance dimensions. The relative importance of each modality was different across the emotional dimensions. Participants gave more importance to facial expressions when evaluating Valence. They gave more importance to haptic feedback when evaluating Arousal and Dominance.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"7 1","pages":"334-340"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89725926","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344550
D. Saha, Thomas L. Martin, R. Benjamin Knapp
Determining the relevance of services from intelligent environments is a critical step in implementing a reliable context-aware ambient intelligent system. Designing the provision of explicit indications to the system is effective in communicating this relevance, however, such explicit indications come at the cost of user's cognitive resources. In this work, we strive to create a novel pathway of implicit communication between the user and their ambient intelligence by employing user's stress as a feedback pathway to the intelligent system. In addition, following a few very recent works, we propose using proven laboratory stressors to collect ground truth data for stressed states. We present results from a preliminary pilot study which shows promise for creating this implicit channel of communication as well as proves the feasibility of using laboratory stressors as a reliable method of ground truth collection for stressed states.
{"title":"Towards incorporating affective feedback into context-aware intelligent environments","authors":"D. Saha, Thomas L. Martin, R. Benjamin Knapp","doi":"10.1109/ACII.2015.7344550","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344550","url":null,"abstract":"Determining the relevance of services from intelligent environments is a critical step in implementing a reliable context-aware ambient intelligent system. Designing the provision of explicit indications to the system is effective in communicating this relevance, however, such explicit indications come at the cost of user's cognitive resources. In this work, we strive to create a novel pathway of implicit communication between the user and their ambient intelligence by employing user's stress as a feedback pathway to the intelligent system. In addition, following a few very recent works, we propose using proven laboratory stressors to collect ground truth data for stressed states. We present results from a preliminary pilot study which shows promise for creating this implicit channel of communication as well as proves the feasibility of using laboratory stressors as a reliable method of ground truth collection for stressed states.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"85 1","pages":"49-55"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78185478","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344556
Bo Xiao, P. Georgiou, Brian R. Baucom, Shrikanth S. Narayanan
Our work examines the link between head motion entrainment of interacting couples and human expert's judgment on certain overall behavioral characteristics (e.g., Blame patterns). We employ a data-driven model that clusters head motion in an unsupervised manner into elementary types called kinemes. We propose three groups of similarity measures based on Kullback-Leibler divergence to model entrainment. We find that the divergence of the (joint) distribution of kinemes yields consistent and significant correlation with target behavior characteristics. The divergence of the conditional distribution of kinemes is shown to predict the polarity of the behavioral characteristics. We partly explain the strong correlations via associating the conditional distributions with the prominent behavioral implications of their respective associated kinemes. These results show the possibility of inferring human behavioral characteristics through the modeling of dyadic head motion entrainment.
{"title":"Modeling head motion entrainment for prediction of couples' behavioral characteristics","authors":"Bo Xiao, P. Georgiou, Brian R. Baucom, Shrikanth S. Narayanan","doi":"10.1109/ACII.2015.7344556","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344556","url":null,"abstract":"Our work examines the link between head motion entrainment of interacting couples and human expert's judgment on certain overall behavioral characteristics (e.g., Blame patterns). We employ a data-driven model that clusters head motion in an unsupervised manner into elementary types called kinemes. We propose three groups of similarity measures based on Kullback-Leibler divergence to model entrainment. We find that the divergence of the (joint) distribution of kinemes yields consistent and significant correlation with target behavior characteristics. The divergence of the conditional distribution of kinemes is shown to predict the polarity of the behavioral characteristics. We partly explain the strong correlations via associating the conditional distributions with the prominent behavioral implications of their respective associated kinemes. These results show the possibility of inferring human behavioral characteristics through the modeling of dyadic head motion entrainment.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"146 1","pages":"91-97"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87583652","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344582
Nesrine Fourati, C. Pelachaud
In the context of emotional body expression, previous works mainly focused on perceptual studies to identify the most important expressive cues. Only few studies gave insights on which body cues could be relevant for the classification and the characterization of emotions expressed in body movement. In this paper, we present our Random Forest based feature selection approach for the identification of relevant expressive body cues in the context of emotional body expression classification. We also discuss the ranking of relevant body cues according to each expressed emotion across a set of daily actions.
{"title":"Relevant body cues for the classification of emotional body expression in daily actions","authors":"Nesrine Fourati, C. Pelachaud","doi":"10.1109/ACII.2015.7344582","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344582","url":null,"abstract":"In the context of emotional body expression, previous works mainly focused on perceptual studies to identify the most important expressive cues. Only few studies gave insights on which body cues could be relevant for the classification and the characterization of emotions expressed in body movement. In this paper, we present our Random Forest based feature selection approach for the identification of relevant expressive body cues in the context of emotional body expression classification. We also discuss the ranking of relevant body cues according to each expressed emotion across a set of daily actions.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"47 1","pages":"267-273"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79227362","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344570
T. Wörtwein, Louis-Philippe Morency, Stefan Scherer
Public speaking has become an integral part of many professions and is central to career building opportunities. Yet, public speaking anxiety is often referred to as the most common fear in everyday life and can hinder one's ability to speak in public severely. While virtual and real audiences have been successfully utilized to treat public speaking anxiety in the past, little work has been done on identifying behavioral characteristics of speakers suffering from anxiety. In this work, we focus on the characterization of behavioral indicators and the automatic assessment of public speaking anxiety. We identify several indicators for public speaking anxiety, among them are less eye contact with the audience, reduced variability in the voice, and more pauses. We automatically assess the public speaking anxiety as reported by the speakers through a self-assessment questionnaire using a speaker independent paradigm. Our approach using ensemble trees achieves a high correlation between ground truth and our estimation (r=0.825). Complementary to automatic measures of anxiety, we are also interested in speakers' perceptual differences when interacting with a virtual audience based on their level of anxiety in order to improve and further the development of virtual audiences for the training of public speaking and the reduction of anxiety.
{"title":"Automatic assessment and analysis of public speaking anxiety: A virtual audience case study","authors":"T. Wörtwein, Louis-Philippe Morency, Stefan Scherer","doi":"10.1109/ACII.2015.7344570","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344570","url":null,"abstract":"Public speaking has become an integral part of many professions and is central to career building opportunities. Yet, public speaking anxiety is often referred to as the most common fear in everyday life and can hinder one's ability to speak in public severely. While virtual and real audiences have been successfully utilized to treat public speaking anxiety in the past, little work has been done on identifying behavioral characteristics of speakers suffering from anxiety. In this work, we focus on the characterization of behavioral indicators and the automatic assessment of public speaking anxiety. We identify several indicators for public speaking anxiety, among them are less eye contact with the audience, reduced variability in the voice, and more pauses. We automatically assess the public speaking anxiety as reported by the speakers through a self-assessment questionnaire using a speaker independent paradigm. Our approach using ensemble trees achieves a high correlation between ground truth and our estimation (r=0.825). Complementary to automatic measures of anxiety, we are also interested in speakers' perceptual differences when interacting with a virtual audience based on their level of anxiety in order to improve and further the development of virtual audiences for the training of public speaking and the reduction of anxiety.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"77 1","pages":"187-193"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74804240","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344593
Hanan Salam, M. Chetouani
In this paper, we address the problematic of automatic detection of engagement in multi-party Human-Robot Interaction scenarios. The aim is to investigate to what extent are we able to infer the engagement of one of the entities of a group based solely on the cues of the other entities present in the interaction. In a scenario featuring 3 entities: 2 participants and a robot, we extract behavioural cues that concern each of the entities, we then build models based solely on each of these entities' cues and on combinations of them to predict the engagement level of each of the participants. Person-level cross validation shows that we are capable of detecting the engagement of the participant in question using solely the behavioural cues of the robot with a high accuracy compared to using the participant's cues himself (75.91% vs. 74.32%). Moreover using the behavioural cues of the other participant is also informative where it permits the detection of the engagement of the participant in question at an accuracy of 62.15% on average. The correlation between the features of the other participant with the engagement labels of the participant in question suggests a high cohesion between the two participants. In addition, the similarity of the most significantly correlated features among the two participants suggests a high synchrony between the two parties.
{"title":"Engagement detection based on mutli-party cues for human robot interaction","authors":"Hanan Salam, M. Chetouani","doi":"10.1109/ACII.2015.7344593","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344593","url":null,"abstract":"In this paper, we address the problematic of automatic detection of engagement in multi-party Human-Robot Interaction scenarios. The aim is to investigate to what extent are we able to infer the engagement of one of the entities of a group based solely on the cues of the other entities present in the interaction. In a scenario featuring 3 entities: 2 participants and a robot, we extract behavioural cues that concern each of the entities, we then build models based solely on each of these entities' cues and on combinations of them to predict the engagement level of each of the participants. Person-level cross validation shows that we are capable of detecting the engagement of the participant in question using solely the behavioural cues of the robot with a high accuracy compared to using the participant's cues himself (75.91% vs. 74.32%). Moreover using the behavioural cues of the other participant is also informative where it permits the detection of the engagement of the participant in question at an accuracy of 62.15% on average. The correlation between the features of the other participant with the engagement labels of the participant in question suggests a high cohesion between the two participants. In addition, the similarity of the most significantly correlated features among the two participants suggests a high synchrony between the two parties.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"71 1","pages":"341-347"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76772461","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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