Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
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,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/12/560 |
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| author | Zhuangzhuang Dai Vincent Gbouna Zakka Luis J. Manso Martin Rudorfer Ulysses Bernardet Johanna Zumer Manolya Kavakli-Thorne |
| author_facet | Zhuangzhuang Dai Vincent Gbouna Zakka Luis J. Manso Martin Rudorfer Ulysses Bernardet Johanna Zumer Manolya Kavakli-Thorne |
| author_sort | Zhuangzhuang Dai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8710a9f2d3144dc1a67c91f80afa055e |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-8710a9f2d3144dc1a67c91f80afa055e2024-12-27T14:05:14ZengMDPI AGAlgorithms1999-48932024-12-01171256010.3390/a17120560Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A ReviewZhuangzhuang Dai0Vincent Gbouna Zakka1Luis J. Manso2Martin Rudorfer3Ulysses Bernardet4Johanna Zumer5Manolya Kavakli-Thorne6Department of Applied AI & Robotics, Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UKDepartment of Applied AI & Robotics, Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UKDepartment of Applied AI & Robotics, Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UKDepartment of Applied AI & Robotics, Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UKDepartment of Applied AI & Robotics, Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UKSchool of Psychology, Institute of Health and Neurodevelopment, Aston University, Birmingham B4 7ET, UKAston Digital Futures Institute, Aston University, Birmingham B4 7ET, UKHuman 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.https://www.mdpi.com/1999-4893/17/12/560human engagementsensor-based systemsengagement estimation techniquesliterature review |
| spellingShingle | Zhuangzhuang Dai Vincent Gbouna Zakka Luis J. Manso Martin Rudorfer Ulysses Bernardet Johanna Zumer Manolya Kavakli-Thorne Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review Algorithms human engagement sensor-based systems engagement estimation techniques literature review |
| title | Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review |
| title_full | Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review |
| title_fullStr | Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review |
| title_full_unstemmed | Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review |
| title_short | Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review |
| title_sort | sensors techniques and future trends of human engagement enabled applications a review |
| topic | human engagement sensor-based systems engagement estimation techniques literature review |
| url | https://www.mdpi.com/1999-4893/17/12/560 |
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