Alle Publikationen
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2019
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(2019) : Cuteness as a ‘Dark Pattern’ in Home Robots: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Daegu, Korea: IEEE, S. 374-381
DOI: https://doi.org/10.1109/HRI.2019.8673274 Abstract: Dark patterns are a recent phenomenon in the field of interaction design, where design patterns and behavioral psychology are deployed in ways that deceive the user. However, the current corpus of dark patterns literature focuses largely on screen-based digital interactions and should be expanded to include home robots. In this paper, we apply the concept of dark patterns to the ‘cute’ aesthetic of home robots and suggest that their design constitutes a dark pattern in HRI by (1) emphasizing short-term gains over long-term decisions; (2) depriving users of some degree of conscious agency at the site of interaction; and (3) creating an affective response in the user for the purpose of collecting emotional data. This exploratory paper expands the current library of dark patterns and their application to new technological interfaces into the domain of home robotics in order to establish the grounds for an ethical design practice in HRI.
Keywords: Affect, dark patterns, data ethics, Data privacy, Design, design patterns, emotion, emotion recognition, ethical aspects, ethical design practice, Ethics, Home computing, home robots, HRI, human-robot interaction, ieee xplore, Interaction, Moral & Ethik, privacy, PSYCHOLOGY, Robot sensing systems, Software, surveillance capitalism, user experience design 2018
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(2018) : Artificial Empathy in Social Robots: An analysis of Emotions in Speech: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): Nanjing, China: IEEE Robotics & Automation Society, S. 632-637
DOI: https://doi.org/10.1109/ROMAN.2018.8525652 Abstract: Artificial speech developed using speech synthesizers has been used as the voice for robots in Human Robot Interaction (HRI). As humans anthropomorphize robots, an empathetically interacting robot is expected to increase the level of acceptance of social robots. Here, a human perception experiment evaluates whether human subjects perceive empathy in robot speech. For this experiment, empathy is expressed only by adding appropriate emotions to the words in speech. Also, humans’ preferences for a robot interacting with empathetic speech versus a standard robotic voice are also assessed. The results show that humans are able to perceive empathy and emotions in robot speech, and prefer it over the standard robotic voice. It is important for the emotions in empathetic speech to be consistent with the language content of what is being said, and with the human users’ emotional state. Analyzing emotions in empathetic speech using valence-arousal model has revealed the importance of secondary emotions in developing empathetically speaking social robots.
Keywords: Angemessen(heit) (von Technik), Anthropomorphism, appropriate emotions, artificial empathy, artificial speech, control engineering computing, emotion recognition, empathetic speech, empathetically interacting robot, human perception experiment, Human robot interaction, human subjects, human users, human-robot interaction, Humans, ieee xplore, Medical services, robot interacting, Robot sensing systems, robot speech, Robots, service robot, social robots, speech synthesis, speech synthesizers, standard robotic voice, standards, Task Analysis -
(2018) : Emotionally Adaptive Driver Voice Alert System for Advanced Driver Assistance System (ADAS) Applications: 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT): Tirunelveli, India: IEEE, S. 509-512
DOI: https://doi.org/10.1109/ICSSIT.2018.8748541 Abstract: Human cognitive analysis catalyzes the innovations in Human Machine Interface (HMI) for a variety of applications. In an Automotive Advanced Driver Assistance System (ADAS), the continuous cognitive interaction of the driver with the assistance system plays a crucial role in enhancing the active safety system. Multiple ADAS functionalities uses a variety of driver alerts through visual, audio and vibrational means to provide a numerous safety alerts to the driver. The effectiveness of any alert system is measured through its success rate in mitigating the actions which are against the alert commands. The actions taken by the driver for the alerts depends heavily on the driver’s moods, which are responsible for driver’s perception in understanding the alerts. Even though the voice alerts are considered as the most effective form of human alerts, the static nature of the voice alerts makes them less effective in making the driver to understand the criticality of the alerts when his moods are abnormal or having a reduced driving concentration levels. An adaptive voice alert system with a cognitive driver synchronization makes the alert penetration successful when the driver’s moods are abnormal or having a reduced driving concentration levels. Here in this paper the adaptive voice alert system is designed using the driver’s emotional cognitive features. The emotionally adaptive voice alert system changes the voice alerts as according to the moods of the driver, which are measured by Deep Learning based Emotion Recognition System. The adaptive voice alert system makes the voice enabled HMI effective which improves the vehicle safety.
Keywords: active safety system, adaptive driver voice alert system, Adaptive systems, Advanced Driver Assistance System (ADAS), advanced driver assistance system applications, Advanced driver assistance systems, alert commands, alert penetration successful, automotive advanced driver assistance system, Cognition, cognitive driver synchronization, Convolutional Neural Network (CNN), driver alerts, driver information systems, emotion recognition, emotion recognition system, Emotion Recognition System (ERS), emotionally adaptive voice alert system, human alerts, human cognitive analysis, Human Computer Interaction, Human Machine Interface (HMI), ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), Monitoring, natural language interfaces, safety alerts, Vehicles, voice alerts 2017
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(2017): Social interaction contexts bias the perceived expressions of interactants. In: Emotion (Washington, D.C.) 17 (4), S. 567-571. DOI: 10.1037/emo0000257
DOI: http://www.ncbi.nlm.nih.gov/pubmed/28191995 Abstract: The present study sought to determine whether contextual information available when viewing social interactions from third-person perspectives may influence observers' perception of the interactants' facial emotion. Observers judged whether the expression of a target face was happy or fearful, in the presence of a happy, aggressive, or neutral interactant. In 2 experiments, the same target expressions were judged to be happier when presented in the context of a happy interactant than when interacting with a neutral or aggressive partner. We failed to show that the target expression was judged as more fearful when interacting with an aggressive partner. Importantly, observers' perception of the target expression was not modulated by the emotion of the context interactant when the interactants were presented back-to-back, suggesting that the bias depends on the presence of an intact interaction arrangement. These results provide valuable insight into how social contextual effects shape our perception of facial emotion. (PsycINFO Database Record zitiert von 2
2016
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(2016) : Interpersonal accuracy in relation to culture and ethnicity In: Hall, Judith A.; Schmid Mast, Marianne; West, Tessa v. (Hg.): The Social Psychology of Perceiving Others Accurately: Cambridge: Cambridge University Press, S. 328-349
DOI: https://doi.org/10.1017/CBO9781316181959.016 Abstract: Classic studies by Ekman and Izard provided early evidence for the cross-cultural universality of emotion recognition, through a set of studies that were later examined from the perspective of the cultural differences they also reveal. The body of evidence as a whole supports a middle ground, suggesting that both emotional expression and its perception show basic similarities across cultures and yet meaningful differences as well. We discuss both spontaneous and motivated processes in both emotional expression and recognition. Further, this chapter attempts to review this material in terms of Brunswik's lens model, which emphasizes the creation of observable cues and their interpretation by others. We also discuss cultural differences that can arise at multiple stages of the emotion process beyond emotional expression and recognition. Namely, individuals across groups can respond differently to nonverbal cues of emotion, which involves differences in the subjective interp retation of events via cognitive appraisal, differences in internal experience, and differences in emotion regulation. These, in turn, can influence accuracy in judging emotion cues across cultures. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
2014
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(2014) : A system for feature classification of emotions based on speech analysis; applications to human-robot interaction: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM): Tehran, Iran: IEEE, S. 795-800
DOI: https://doi.org/10.1109/ICRoM.2014.6991001 Abstract: A system for recognition of emotions based on speech analysis can have interesting applications in human robot interaction. Robot should make a proper mutual communication between sound recognition and perception for creating a desired emotional interaction with humans. Advanced research in this field will be based on sound analysis and recognition of emotions in spontaneous dialog. In this paper, we report the results obtained from an exploratory study on a methodology to automatically recognize and classify basic emotional states. The study attempted to investigate the appropriateness of using acoustic and phonetic properties of emotive speech with the minimal use of signal processing algorithms. The efficiency of the methodology was evaluated by experimental tests on adult European speakers. The speakers had to repeat six simple sentences in English language in order to emphasize features of the pitch (peak, value and range), the intensity of the speech, the formants and the speech rate. The proposed methodology using the freeware program (PRAAT) and consists of generating and analyzing a graph of pitch, formant and intensity of speech signals for classify basic emotion. Eventually, the proposed model provided successful recognition of the basic emotion in most of the cases.
Keywords: acoustic properties, Acoustics, adult European speakers, Angemessen(heit) (von Technik), emotion feature classification, emotion recognition, emotional interaction, emotional state classification, emotional state recognition, emotive speech, English language, Feature extraction, formant, formants, graph analysis, graph generation, graph theory, human-robot interaction, ieee xplore, mutual communication, phonetic properties, pitch, pitch features, PRAAT freeware program, public domain software, Shape, signal classification, signal processing algorithms, sound analysis, sound perception, sound recognition, speech, speech analysis, speech intensity, speech rate, speech recognition, speech signals, spontaneous dialog 2011
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(2011) : A novel real-time emotion detection system from audio streams based on Bayesian Quadratic Discriminate Classifier for ADAS: Proceedings of the Joint INDS’11 ISTET’11: Klagenfurt, Austria: IEEE, S. 1-5
DOI: https://doi.org/10.1109/INDS.2011.6024783 Abstract: This paper presents a real-time emotion recognition concept of voice streams. A comprehensive solution based on Bayesian Quadratic Discriminate Classifier(QDC) is developed. The developed system supports Advanced Driver Assistance Systems (ADAS) to detect the mood of the driver based on the fact that aggressive behavior on road leads to traffic accidents. We use only 12 features to classify between 5 different classes of emotions. We illustrate that the extracted emotion features are highly overlapped and how each emotion class is effecting the recognition ratio. Finally, we show that the Bayesian Quadratic Discriminate Classifier is an appropriate solution for emotion detection systems, where a real-time detection is deeply needed with a low number of features.
Keywords: ADAS, Advanced driver assistance systems, Angemessen(heit) (von Technik), audio signal processing, audio streaming, audio streams, Bayes methods, Bayesian methods, Bayesian quadratic discriminate classifier, driver information systems, emotion feature extraction, emotion recognition, Feature extraction, ieee xplore, real-time emotion detection system, real-time emotion recognition concept, road accidents, speech, speech recognition, Support vector machines, traffic accidents, Vehicles, voice streams 2009
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(2009): Die Intelligence and Development Scale Sozial-Emotionale Kompetenz (IDS-SEK). Psychometrische Eigenschaften eines Tests zur Erfassung sozial-emotionaler Fähigkeiten. In: Diagnostica 55 (4), S. 234-244. DOI: 10.1026/0012-1924.55.4.234
DOI: https://doi.org/10.1026/0012-1924.55.4.234 Abstract: Tested psychometric properties of the Intelligence and Development Scales Sozial-Emotionale Kompetenz Skala (IDS-SEK). To address a lack of adequate measures for capturing the social-emotional abilities of children, the IDS-SEK was constructed. The test measures social-emotional abilities on the dimensions (1) Emotion Recognition (EE), (2) Emotion Regulation (ER), (3) Understanding of Social Situations (SV), and (4) Social-Operation Competence (SH). Results of studies conducted with 839 5- to 10-year-olds from Switzerland support the scale's construct and criterion validity. Comparisons (matched for age, sex, and intelligence quotient) of children without behavioural problems, with Asperger syndrome (n = 38), and with aggressive-behavioral problems (n = 57) provided additional proof of the scale's differentiation ability. It is concluded that the IDS-SEK is well suited to measure capabilities and deficits in the social-emotional domain in a multidimensional way and can be used as the basis of specific interventions.
Keywords: Aggressionsverhalten, Aggressive Behavior, Aspergers Syndrome, Asperger-Syndrom, Autism Spectrum Disorders, Autismus-Spektrum-Störung, Developmental Scales & Schedules, emotion recognition, Emotional Regulation, Emotionen, Emotions, Emotionserkennung, Emotionsforschung, Emotionsregulation, Entwicklungstests, Personality Scales & Inventories, Persönlichkeitstests, psychometrics, Psychometrie, Psychosocial & Personality Development, Psychosoziale Entwicklung und Persönlichkeitsentwicklung, Social intelligence, Social Skills, Soziale Fertigkeiten, Soziale Intelligenz, Test Validity, Testvalidität, Verhaltensforschung -
(2009) : Robots with emotional intelligence: 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI): 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI): New York, NY, US: Association for Computing Machinery
DOI: https://doi.org/10.1145/1514095.1514098 Abstract: This keynote talk will illustrate a basic set of skills of emotional intelligence, how they are important for robots and agents that interact with people, and how our research at MIT addresses part of the problem of giving robots such skills. One of the most important skills is the ability to perceive and understand expressions of emotion, which I will highlight by demonstrating new technologies developed to read joint facial-head movements in real-time and associate these with complex affective-cognitive states, and technologies to read paralinguistic vocal cues from speech. I will also show some non-traditional ways robots might sense and learn about human emotion, and ways they can respond to what they sense that can help or hurt people. I will discuss social and ethical issues these technologies raise. Finally, I will present some new possibilities for robots to both learn from people and help teach skills of emotional intelligence to people, especially to those with nonverbal learning impairments who often want to learn these skills, including many people with diagnoses of autism spectrum disorders such as Aspergers Syndrome.
Keywords: Affective computing, affective-cognitive state, Artificial intelligence, Aspergers Syndrome, Autism, Autism spectrum disorder, behavioural sciences, computer aided instruction, deception detection, Educational robots, emotion expression, emotion perception, emotion recognition, emotion understanding, Emotional Intelligence, empathic technology, ethical aspects, ethical issue, facial expression recognition, facial-head movement, human emotion learning, human emotion sensing, human-robot interaction, ieee xplore, image motion analysis, intelligent robots, Laboratories, learning (artificial intelligence), Media, medical disorders, MIT, Moral & Ethik, nonverbal learning impairment, paralinguistic vocal cue, physiological sensing, prosody analysis, robot emotional intelligence, Robot sensing systems, skills teaching, social issue, speech processing, system, Teaching 2006
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(2006) : Modeling Affect in Socially Interactive Robots: ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication: ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human: Hatfield, UK: IEEE, S. 558-563
DOI: https://doi.org/10.1109/ROMAN.2006.314448 Abstract: Humans use expressions of emotion in a very social manner, to convey messages such as "I'm happy to see you" or "I want to be comforted," and people's long-term relationships depend heavily on shared emotional experiences. We believe that for robots to interact naturally with humans in social situations they should also be able to express emotions in both short-term and long-term relationships. To this end, we have developed an affective model for social robots. This generative model attempts to create natural, human-like affect and includes distinctions between immediate emotional responses, the overall mood of the robot, and long-term attitudes toward each visitor to the robot. This paper presents the general affect model as well as particular details of our implementation of the model on one robot, the Roboceptionist
Keywords: Cultural differences, Displays, emotion & social robotics, emotion recognition, Global communication, human emotion, Human robot interaction, Humanoid Robots, ieee xplore, interactive systems, man-machine systems, Medical services, Mood, Orbital robotics, Roboceptionist, Senior citizens, Social robotic, socially interactive robots -
(2006): Decoding speech prosody in five languages. In: Semiotica (158). DOI: 10.1515/SEM.2006.017
Abstract: Twenty English-speaking listeners judged the emotive intent of utterances spoken by male and female speakers of English, German, Chinese, Japanese, and Tagalog. The verbal content of utterances was neutral but prosodic elements conveyed each of four emotions: joy, anger, sadness, and fear. Identification accuracy was above chance performance levels for all emotions in all languages. Across languages, sadness and anger were more accurately recognized than joy and fear. Listeners showed an in-group advantage for decoding emotional prosody, with highest recognition rates for English utterances and lowest recognition rates for Japanese and Chinese utterances. Acoustic properties of stimuli were correlated with the intended emotion expressed. Our results support the view that emotional prosody is decoded by a combination of universal and culture-specific cues.
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(2006): The influence of emotion recognition and emotion regulation on intercultural adjustment. In: INTERNATIONAL JOURNAL OF INTERCULTURAL RELATIONS 30 (3), S. 345-363. DOI: 10.1016/j.ijintrel.2005.08.006
Abstract: Previous studies have consistently shown emotion regulation to be an important predictor of intercultural adjustment. Emotional intelligence theory suggests that before people can regulate emotions they need to recognize them; thus emotion recognition ability should also predict intercultural adjustment. The present study tested this hypothesis in international students at three times during the school year. Recognition of anger and emotion regulation predicted positive adjustment; recognition of contempt, fear and sadness predicted negative adjustment. Emotion regulation did not mediate the relationship between emotion recognition and adjustment, and recognition and regulation jointly predicted adjustment. These results suggest recognition of specific emotions may have special functions in intercultural adjustment, and that emotion recognition and emotion regulation play independent roles in adjustment.
2004
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(2004) : Emotional robotics based on iT/spl I.bar/Media: 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004, 3: Busan, South Korea: IEEE
DOI: https://doi.org/10.1109/IECON.2004.1432316 Abstract: Intelligence is thought to be related to interaction rather than a deep but passive thinking. Interactive tangible media "iTMedia" is proposed to explore these issues. Personal robotics is a major area to investigate these ideas. A new design methodology for personal and emotional robotics is proposed. Sciences of the artificial and intelligence have been investigated. A short history of artificial intelligence is presented in terms of logic, heuristics, and mobility; a science of intelligence is presented in terms of imitation and understanding; intelligence issues for robotics and intelligence measures are described. A design methodology for personal robots based on science of emotion is investigated. We investigate three different aspects of design: visceral, behavioral, and reflective. We also discuss affect and emotion in robots, robots that sense emotion, robots that induce emotion in people, and implications and ethical issues of emotional robots. Personal robotics for the elderly is investigated to explore these ideas.
Keywords: Artificial intelligence, Design methodology, emotion recognition, emotional robotics, ethical issues, History, Humans, ieee xplore, intelligent robots, Intelligent Systems, interactive systems, interactive tangible media, iT/spl I.bar/Media intelligence, Logic, Mechanical engineering, Mobile robots, Moral & Ethik, passive thinking, personal robotics, Robot sensing systems
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