A Feasibility Study of a Data-Driven Human-Robot Conversational Interface for Reminiscence Therapy

Céspedes, Nathalia; Hsu, Anne; Jones, Janelle; Farkhatdinov, Ildar

doi:10.1109/humanoids53995.2022.10000119

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) 2022

DOI: 10.1109/humanoids53995.2022.10000119

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A Feasibility Study of a Data-Driven Human-Robot Conversational Interface for Reminiscence Therapy

Nathalia Céspedes

Anne Hsu

Janelle Jones

et al.

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2024

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Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review

Dai,

Zakka,

Manso

et al. 2024

Algorithms

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Human engagement is a vital test research area actively explored in cognitive science and user experience studies. The rise of big data and digital technologies brings new opportunities into this field, especially in autonomous systems and smart applications. This article reviews the latest sensors, current advances of estimation methods, and existing domains of application to guide researchers and practitioners to deploy engagement estimators in various use cases from driver drowsiness detection to human–robot interaction (HRI). Over one hundred references were selected, examined, and contrasted in this review. Specifically, this review focuses on accuracy and practicality of use in different scenarios regarding each sensor modality, as well as current opportunities that greater automatic human engagement estimation could unlock. It is highlighted that multimodal sensor fusion and data-driven methods have shown significant promise in enhancing the accuracy and reliability of engagement estimation. Upon compiling the existing literature, this article addresses future research directions, including the need for developing more efficient algorithms for real-time processing, generalization of data-driven approaches, creating adaptive and responsive systems that better cater to individual needs, and promoting user acceptance.

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Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review

Dai,

Zakka,

Manso

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

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A Feasibility Study of a Data-Driven Human-Robot Conversational Interface for Reminiscence Therapy

Cited by 1 publication

References 28 publications

Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review

Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review

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