Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929250662088704 |
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| author | Liu, Yuanyuan Wang, Ke Wei, Lin Chen, Jingying Zhan, Yibing Tao, Dapeng Chen, Zhe |
| author_facet | Liu, Yuanyuan Wang, Ke Wei, Lin Chen, Jingying Zhan, Yibing Tao, Dapeng Chen, Zhe |
| contents | Affective computing, which aims to recognize, interpret, and understand human emotions, provides benefits in healthcare, such as improving patient care and enhancing doctor-patient communication. However, there is a noticeable absence of a comprehensive summary of recent advancements in affective computing for healthcare, which could pose difficulties for researchers entering this field. To address this, our paper aims to provide an extensive literature review of related studies published in the last five years. We begin by analyzing trends, benefits, and limitations of recent datasets and affective computing methods devised for healthcare. Subsequently, we highlight several healthcare application hotspots of current technologies that could be promising for real-world deployment. Through our analysis, we identify and discuss some ongoing challenges in the field as evidenced by the literature. Concluding with a thorough review, we further offer potential future research directions and hope our findings and insights could guide related researchers to make better contributions to the evolution of affective computing in healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13589 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond Liu, Yuanyuan Wang, Ke Wei, Lin Chen, Jingying Zhan, Yibing Tao, Dapeng Chen, Zhe Human-Computer Interaction Affective computing, which aims to recognize, interpret, and understand human emotions, provides benefits in healthcare, such as improving patient care and enhancing doctor-patient communication. However, there is a noticeable absence of a comprehensive summary of recent advancements in affective computing for healthcare, which could pose difficulties for researchers entering this field. To address this, our paper aims to provide an extensive literature review of related studies published in the last five years. We begin by analyzing trends, benefits, and limitations of recent datasets and affective computing methods devised for healthcare. Subsequently, we highlight several healthcare application hotspots of current technologies that could be promising for real-world deployment. Through our analysis, we identify and discuss some ongoing challenges in the field as evidenced by the literature. Concluding with a thorough review, we further offer potential future research directions and hope our findings and insights could guide related researchers to make better contributions to the evolution of affective computing in healthcare. |
| title | Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.13589 |