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Status : Active
Research Topic : MENTAL RETARDATION
Field of Research : Computer-Human Interaction
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  • Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE210101107

    Funder
    Australian Research Council
    Funding Amount
    $412,744.00
    Summary
    Ahead of the Game: Balancing the Gaming Industry and Public Interest. This project battles the risks and embraces the benefits of digital gaming. There is a risk that one loses control of their gaming and prioritises it over other duties. This is offset by the benefits of using digital games for health. It is the first to decode and use the health data embedded in the connection between the gamer and their game persona (avatar). It does this by concurrently assessing important gamer, family, cul .... Ahead of the Game: Balancing the Gaming Industry and Public Interest. This project battles the risks and embraces the benefits of digital gaming. There is a risk that one loses control of their gaming and prioritises it over other duties. This is offset by the benefits of using digital games for health. It is the first to decode and use the health data embedded in the connection between the gamer and their game persona (avatar). It does this by concurrently assessing important gamer, family, cultural and game structure features. Findings will prompt the ethical growth of the Australian Health games industry and inform strategies to combat gaming disorder by tailoring games to users' needs. This will uniquely benefit Australians by re-directing this growing industry to better serve the public interest.
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    Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE200101100

    Funder
    Australian Research Council
    Funding Amount
    $425,613.00
    Summary
    Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health c .... Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health care, empowering them with empathetic abilities is important for their success. The project will advance fundamental research in machine learning, affective computing and artificial intelligence to model human behavior, personality traits and emotions for an empathetic human-robot interaction.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190101294

    Funder
    Australian Research Council
    Funding Amount
    $380,000.00
    Summary
    Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this i .... Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this issue through novel analyses of very subtle cues in facial and vocal expressions of affect embedded in a multimodal deep learning framework. Current approaches can successfully assist in binary classification tasks. This project will tackle the much more difficult problem of developing advanced affective sensing technology to simultaneously handle homogeneous and heterogeneous affect classes as well as continuous range estimates of affect intensity.
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