Best Papers
 Personalized Exercise Recommender Systems for Rehabilitation Using Graph Neural Networks 


Vol. 47,  No. 4, pp. 644-655, Apr.  2022
10.7840/kics.2022.47.4.644


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  Abstract

Recently, in the field of rehabilitation treatment, applications of deep learning, artificial intelligence, and big data technology to smart rehabilitation treatment have been actively studied. In this paper, we propose a personalized exercise recommender system for rehabilitation suitable for each patient’s individual characteristics using a graph neural network (GNN), which captures the graph structure more effectively. Since it is impossible to use actual patient data due to the legal issue for data collection, this paper synthetically generates attribute data of knee disease patients, selects various rehabilitation programs, and generates empirical patient-exercise connectivity data using cross-validation. The performance of proposed model is evaluated in terms of precision, recall, nDCG in the top-N recommendation scenario, recommending the top-N items among all the predicted preferences for each user. When the generated dataset is used, it is empirically demonstrated that the proposed method outperforms the conventional recommendation algorithm in terms of all performance metrics.

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  Cite this article

[IEEE Style]

S. Kim, J. Oh, D. Oh, C. Seo, W. Shin, "Personalized Exercise Recommender Systems for Rehabilitation Using Graph Neural Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 4, pp. 644-655, 2022. DOI: 10.7840/kics.2022.47.4.644.

[ACM Style]

Sooyon Kim, Jeong-Heon Oh, Dagun Oh, Changwon Seo, and Won-Yong Shin. 2022. Personalized Exercise Recommender Systems for Rehabilitation Using Graph Neural Networks. The Journal of Korean Institute of Communications and Information Sciences, 47, 4, (2022), 644-655. DOI: 10.7840/kics.2022.47.4.644.

[KICS Style]

Sooyon Kim, Jeong-Heon Oh, Dagun Oh, Changwon Seo, Won-Yong Shin, "Personalized Exercise Recommender Systems for Rehabilitation Using Graph Neural Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 4, pp. 644-655, 4. 2022. (https://doi.org/10.7840/kics.2022.47.4.644)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880