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http://dx.doi.org/10.14400/JDC.2021.19.9.257

The study of blood glucose level prediction model using ballistocardiogram and artificial intelligence  

Choi, Sang-Ki (Software Convergence Institute Co., Ltd.)
Park, Cheol-Gu (Software Convergence Institute Co., Ltd.)
Publication Information
Journal of Digital Convergence / v.19, no.9, 2021 , pp. 257-269 More about this Journal
Abstract
The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R2) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.
Keywords
Blood glucose level prediction; MLP; Deep Learning; Heart Rate Variability; IoT Digital device; ICT Convergence;
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