Development of a Rehabilitation Exercise Recommender System Based on Neural Collaborative Filtering (NCF) 


Vol. 47,  No. 11, pp. 1843-1858, Nov.  2022
10.7840/kics.2022.47.11.1843


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  Abstract

Rehabilitation exercises should be prescribed to patients by considering carefully those types and strength etc. In the case of wrong prescription, there will be little effect or it may bring adverse effects like injuries to patients. Rehabilitation exercise recommender system has been developed in this study by adopting the clinical test data of Chungnam National University Hospital. Training data were generated from the exercise execution data of patients with shoulder adhesive capsulitis, shoulder rotator cuff injury and low back pain diseases. Rehabilitation exercise recommender system based on NCF framework’s NeuMF model has been developed and validated using the generated training data. Developed recommender algorithm, as a result, showed better performance than GMF and MLP models. Rehabilitation exercise recommender system of this study will help patients to recover their functional ability and return to the society soon.

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[IEEE Style]

Y. Chang, B. Jeon, N. Park, M. Han, J. Kim, "Development of a Rehabilitation Exercise Recommender System Based on Neural Collaborative Filtering (NCF)," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 11, pp. 1843-1858, 2022. DOI: 10.7840/kics.2022.47.11.1843.

[ACM Style]

Yoon-Seop Chang, Boosun Jeon, NohSam Park, Mikyong Han, and Jae-Chul Kim. 2022. Development of a Rehabilitation Exercise Recommender System Based on Neural Collaborative Filtering (NCF). The Journal of Korean Institute of Communications and Information Sciences, 47, 11, (2022), 1843-1858. DOI: 10.7840/kics.2022.47.11.1843.

[KICS Style]

Yoon-Seop Chang, Boosun Jeon, NohSam Park, Mikyong Han, Jae-Chul Kim, "Development of a Rehabilitation Exercise Recommender System Based on Neural Collaborative Filtering (NCF)," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 11, pp. 1843-1858, 11. 2022. (https://doi.org/10.7840/kics.2022.47.11.1843)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880