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http://dx.doi.org/10.15207/JKCS.2019.10.3.001

Motion Monitoring using Mask R-CNN for Articulation Disease Management  

Park, Sung-Soo (Data Mining Lab., Department of Computer Science, Kyonggi University)
Baek, Ji-Won (Data Mining Lab., Department of Computer Science, Kyonggi University)
Jo, Sun-Moon (Department of Computer Information Technology Education, Paichai University)
Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.3, 2019 , pp. 1-6 More about this Journal
Abstract
In modern society, lifestyle and individuality are important, and personalized lifestyle and patterns are emerging. The number of people with articulation diseases is increasing due to wrong living habits. In addition, as the number of households increases, there is a case where emergency care is not received at the appropriate time. We need information that can be managed by ourselves through accurate analysis according to the individual's condition for health and disease management, and care appropriate to the emergency situation. It is effectively used for classification and prediction of data using CNN in deep learning. CNN differs in accuracy and processing time according to the data features. Therefore, it is necessary to improve processing speed and accuracy for real-time healthcare. In this paper, we propose motion monitoring using Mask R-CNN for articulation disease management. The proposed method uses Mask R-CNN which is superior in accuracy and processing time than CNN. After the user's motion is learned in the neural network, if the user's motion is different from the learned data, the control method can be fed back to the user, the emergency situation can be informed to the guardian, and appropriate methods can be taken according to the situation.
Keywords
CNN; Human Motion; Healthcare; Deep Learning; Mask R-CNN; Personal Health Record;
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Times Cited By KSCI : 2  (Citation Analysis)
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