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      Intelligent Joystick Sensing the User’s Emotion and Providing Biofeedback

      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      Intelligent Systems, Human Computer Interaction, Bio-Signals, Biofeedback

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          Abstract

          Development of an intelligent joystick is proposed which senses the user’s bio-signals and recognises the user’s emotion. It provides biofeedback to the user as well as the user’s emotional state information to the computer allowing human-computer interaction over sensitive environment. While the user is interacting with a computer via a joystick the bio-signals can be collected through the user’s fingers touching it. The collected bio-signals information is mapped on a two-dimensional space to find out the quality and intensity of emotion continuously and in a real-time manner. The intelligent joystick has application within several fields such as healthcare, sport and game industries. In such cases, the user can be influenced, or suffer from medical problems while under stress during interaction with the machines. The intelligent joystick will provide feedback to the user and alert alarm about unhealthy conditions through the embedded actuators and allow the machine to adapt with the users’ emotional state.

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          Most cited references 16

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          Toward machine emotional intelligence: analysis of affective physiological state

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            Emotion recognition based on physiological changes in music listening.

            Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\% and 70\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.
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              Automatic, Dimensional and Continuous Emotion Recognition

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                Author and article information

                Contributors
                Conference
                July 2018
                July 2018
                : 1-5
                Affiliations
                Bournemouth University

                Poole, UK, BH12 5BB
                Article
                10.14236/ewic/HCI2018.221
                © Haratian. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of the 32nd International BCS Human Computer Interaction Conference
                HCI
                32
                Belfast, UK
                4 - 6 July 2018
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction Conference
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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