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Developing an ageing adaptable emotional virtual human: an AI-based approach

Yuyuan Peng

Informatics for Health and Social Care2026https://doi.org/10.1080/17538157.2025.2611135article
ABDC C
Weight
0.50

What the paper says

The research aims to explore the theoretical aspects of using a virtual digital person as an emotional companion and to reveal the possibilities offered by the synthesis of design, AI, computer science, and modern technologies. Analytical methods, machine learning, and neural networks were used. The results of the study showed that the use of AI has great potential for creating a virtual person adapted to aging. The virtual digital person developed with the help of AI was specially adapted to the unique needs and requirements of the elderly. It provided not only emotional support but also served as an information resource, helping to monitor medical conditions, remind them to take medications and provide advice on activity and health care. It has been found that the creation of a virtual digital person as an emotional companion is an important step in expanding the possibilities of interaction between people and technology, in particular in the context of the growing age of the population and the need for support. The possibility of using the results obtained in practical activities will allow the use of AI to develop new methods for creating a virtual digital person.

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https://doi.org/https://doi.org/10.1080/17538157.2025.2611135

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@article{yuyuan2026,
  title        = {{Developing an ageing adaptable emotional virtual human: an AI-based approach}},
  author       = {Yuyuan Peng},
  journal      = {Informatics for Health and Social Care},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/17538157.2025.2611135},
}

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Developing an ageing adaptable emotional virtual human: an AI-based approach

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.