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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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References

  1. Wang, S.: Spatial patterns and social-economic influential factors of population aging: a global assessment from 1990 to 2010. Soc. Sci. Med. 253, 112963 (2020)

    Article  Google Scholar 

  2. Ying, G., Zonghua, L.: Application and development of smart pension products in China. In: 2020 4th International Seminar on Education, Management and Social Sciences (ISEMSS 2020), pp. 287–291. Atlantis Press (2020)

    Google Scholar 

  3. Kleinman, A., et al.: Social technology: an interdisciplinary approach to improving care for older adults. Front. Public Health 9, 729149 (2021). https://doi.org/10.3389/fpubh.2021.729149

    Article  Google Scholar 

  4. Sun, J., Li, W.: How is smart pension possible from the perspective of population aging. In: 7th International Conference on Humanities and Social Science Research (ICHSSR 2021), pp. 345–348. Atlantis Press (2021)

    Google Scholar 

  5. Xin, S., Li, J., Wang, Y.: The development of smart pension with benefits and challenges. Tech. Rep., EasyChair (2019)

    Google Scholar 

  6. Requirements list of Smart Eldercare application scenarios in Shanghai. https://www.shanghai.gov.cn/nw31406/20200820/0001-31406_1441030.html (2020 version). Accessed 10 June 2022

  7. The second requirements list of Smart Eldercare application scenarios in Shanghai. https://mzj.sh.gov.cn/2021bsmz/20210629/6a32755904584d21a7c665a8b86e8ae3.html. Accessed 10 June 2022

  8. Requirements list of Smart Eldercare application scenarios in Guangzhou. http://mzj.gz.gov.cn/gkmlpt/content/7/7925/post_7925097.html#346. Accessed 10 June 2022

  9. Requirements list of Smart Eldercare application scenarios in Chengdu. http://cd.wenming.cn/wmbb/202205/t20220509_7605909.shtml. Accessed 10 June 2022

  10. Meyrowitz, J.: No Sense of Place: The Impact of Electronic Media on Social Behavior. Oxford University Press (1986)

    Google Scholar 

  11. Wu, F., Huang, S., Yin, B.: Scenario-based service: new thinking of the design of learning service. e-Educ. Res. 39, 63–69 (2018)

    Google Scholar 

  12. Shi, L., Yang, X., Li, J., Wu, J., Sun, H.: Scenario construction and deduction for railway emergency response decision-making based on network models. Inf. Sci. 588, 331–349 (2022)

    Article  Google Scholar 

  13. Seibert, K., et al.: Application scenarios for artificial intelligence in nursing care: rapid review. J. Med. Internet Res. 23(11), e26522 (2021)

    Article  Google Scholar 

  14. Yifei, Y., Longming, Z.: Application scenarios and enabling technologies of 5g. China Commun. 11(11), 69–79 (2014)

    Article  Google Scholar 

  15. Kahn, H., Wiener, A.J.: The Next Thirty-Three Years: A Framework for Speculation, pp. 705–732. Daedalus (1967)

    Google Scholar 

  16. Nisbett, R.E.: Mindware: Tools for Smart Thinking. Farrar, Straus and Giroux (2015)

    Google Scholar 

  17. Cognitive function screening in Xinhong Street. http://www.shmh.gov.cn/shmh/sqxx-xhjd/20201216/499154.html. Accessed 24 Feb 2022

  18. Sturtz, D.N.: Communal categorization: the folksonomy. INFO622: Content Representation 16 (2004)

    Google Scholar 

  19. Shen, K., Wu, L.: Folksonomy as a complex network (2005)

    Google Scholar 

  20. Quintarelli, E.F.: Power to the people. In: ISKO Italy-UniMIB Meeting, Milan, June 24, 2005 (2005)

    Google Scholar 

  21. Katakis, I., Tsoumakas, G., Vlahavas, I.: Multilabel text classification for automated tag suggestion. In: Proceedings of the ECML/PKDD, vol. 18, p. 5. Citeseer (2008)

    Google Scholar 

  22. Yang, Y.: Multilabel classification with meta-level features. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 315–322 (2010)

    Google Scholar 

  23. Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehousing Mining 3(3), 1–13 (2007)

    Article  Google Scholar 

  24. Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2013)

    Article  Google Scholar 

  25. Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)

    Article  Google Scholar 

  26. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011)

    Article  MathSciNet  Google Scholar 

  27. Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)

    Article  Google Scholar 

  28. Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Raedt, L., Siebes, A. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44794-6_4

    Chapter  MATH  Google Scholar 

  29. Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. Adv. Neural Inf. Process. Syst. 14 (2001)

    Google Scholar 

  30. Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 195–200 (2005)

    Google Scholar 

  31. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha, Qatar (Oct 2014). https://doi.org/10.3115/v1/D14-1181. https://aclanthology.org/D14-1181

  32. Liu, P., Qiu, X., Huang, X.: Recurrent neural network for text classification with multi-task learning (2016)

    Google Scholar 

  33. Chen, G., Ye, D., Xing, Z., Chen, J., Cambria, E.: Ensemble application of convolutional and recurrent neural networks for multi-label text categorization. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2377–2383. IEEE (2017)

    Google Scholar 

  34. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)

    Google Scholar 

  35. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  36. Yang, P., Sun, X., Li, W., Ma, S., Wu, W., Wang, H.: SGM: sequence generation model for multi-label classification (2018)

    Google Scholar 

  37. Xiao, L., Huang, X., Chen, B., Jing, L.: Label-specific document representation for multi-label text classification. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 466–475 (2019)

    Google Scholar 

  38. Guo, B., Han, S., Han, X., Huang, H., Lu, T.: Label confusion learning to enhance text classification models (2020)

    Google Scholar 

  39. Zhang, W., Yan, J., Wang, X., Zha, H.: Deep extreme multi-label learning. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, pp. 100–107 (2018)

    Google Scholar 

  40. Du, C., Chen, Z., Feng, F., Zhu, L., Gan, T., Nie, L.: Explicit interaction model towards text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6359–6366 (2019)

    Google Scholar 

  41. Pappas, N., Henderson, J.: Gile: a generalized input-label embedding for text classification. Trans. Assoc. Comput. Linguist. 7, 139–155 (2019)

    Article  Google Scholar 

  42. Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)

    Article  MathSciNet  Google Scholar 

  43. Zhang, Y., Yang, Q.: A survey on multi-task learning. IEEE Trans. Knowl. Data Eng. 1 (2021). https://doi.org/10.1109/TKDE.2021.3070203

  44. Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  45. Vandenhende, S., Georgoulis, S., Van Gansbeke, W., Proesmans, M., Dai, D., Van Gool, L.: Multi-task learning for dense prediction tasks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2021). https://doi.org/10.1109/TPAMI.2021.3054719

  46. Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 794–803. PMLR (10–15 Jul 2018). https://proceedings.mlr.press/v80/chen18a.html

  47. Liu, S., Johns, E., Davison, A.J.: End-to-end multi-task learning with attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  48. Zhou, P., Qi, Z., Zheng, S., Xu, J., Bao, H., Xu, B.: Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. arXiv preprint arXiv:1611.06639 (2016)

  49. Kalman, B.L., Kwasny, S.C.: Why tanh: choosing a sigmoidal function. In: Proceedings of the 1992 IJCNN International Joint Conference on Neural Networks, vol. 4, pp. 578–581. IEEE (1992)

    Google Scholar 

  50. Moore, D.S., Notz, W.I., Notz, W.: Statistics: Concepts and Controversies. Macmillan (2006)

    Google Scholar 

  51. Müller, R., Kornblith, S., Hinton, G.E.: When does label smoothing help? In: Wallach, H., Larochelle, H., Beygelzimer, A., d’AlchéBuc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)

    Google Scholar 

  52. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding (2019)

    Google Scholar 

  53. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

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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

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  1. Ning An
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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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