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Automatically Labeling Aging Scenarios with a Machine Learning Approach

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HCI International 2022 – Late Breaking Papers: HCI for Health, Well-being, Universal Access and Healthy Aging (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13521))

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Abstract

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. Labeling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic labeling approach to manage and utilize aging scenarios. Under each class, there are labels with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one label, properly labeling it becomes a multi-label text classification problem. We extend the multi-label text classification model LSAN for labeling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods.

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Acknowledgments

This work was partially supported by grants from the Anhui Provincial Key Technologies R&D Program (2022h11020015), the Program of Introducing Talents of Discipline to Universities (111 Program) (B14025), and the Non-profit Central Research Institute Fund of the Chinese Academy of Medical Sciences, Grant No. 2021-JKCS-026 with accordance ethical approval from the funding academy.

Author information

Authors and Affiliations

  1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China

    Ning An & Yang Xu

  2. Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, Hefei University of Technology, Hefei, China

    Ning An

  3. National Smart Eldercare International S&T Cooperation Base, Hefei University of Technology, Hefei, China

    Ning An & Yang Xu

  4. Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei, China

    Ning An

  5. Shanghai Tianyu Senior Living Service Co., Ltd., Shanghai, China

    Qinglin Gao

  6. School of Basic Courses, Bengbu Medical College, Bengbu, China

    Wenjie Zhu

  7. Management and Business Department, Skidmore College, Saratoga Springs, NY, USA

    Aoran Wu

  8. Department of Social Sciences, University of Eastern Finland, 70150, Kuopio, Finland

    Honglin Chen

Authors
  1. Ning An
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  2. Yang Xu
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  3. Qinglin Gao
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  6. Honglin Chen
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Corresponding author

Correspondence to Wenjie Zhu .

Editor information

Editors and Affiliations

  1. Purdue University West Lafayette, West Lafayette, IN, USA

    Vincent G. Duffy

  2. Tsinghua University, Beijing, China

    Qin Gao

  3. Chongqing University, Chongqing, China

    Jia Zhou

  4. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  5. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

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An, N., Xu, Y., Gao, Q., Zhu, W., Wu, A., Chen, H. (2022). Automatically Labeling Aging Scenarios with a Machine Learning Approach. In: Duffy, V.G., Gao, Q., Zhou, J., Antona, M., Stephanidis, C. (eds) HCI International 2022 – Late Breaking Papers: HCI for Health, Well-being, Universal Access and Healthy Aging. HCII 2022. Lecture Notes in Computer Science, vol 13521. Springer, Cham. https://doi.org/10.1007/978-3-031-17902-0_18

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  • DOI: https://doi.org/10.1007/978-3-031-17902-0_18

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  • Online ISBN: 978-3-031-17902-0

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