Glucose Prediction and Hypoglycemia Alarms Based on Adaptive Model

2014 ◽  
Vol 971-973 ◽  
pp. 275-279
Author(s):  
Yan Nian Wang ◽  
Yan Rui Shen ◽  
Yong Qiang Yong ◽  
Quan Zhong Li ◽  
Chang Qing Sun
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Glucose Concentration ◽  
Autoregressive Model ◽  
Adaptive Model ◽  
Noise Interference ◽  
Glucose Prediction ◽  
Adaptive Autoregressive Model ◽  
Concentration Signal

The paper proposes a glucose prediction model and hypoglycemia alarms technology based on CGMS. Method: By using kalman filter to smooth the glucose data from the CGMS, reducing noise interference; Then according to the non-stationary characteristics of glucose concentration signal ,Using adaptive autoregressive model (AR) glucose prediction model is established; Finally, the prediction model is applied to hypoglycemia alarms. Results: The prediction model can dynamically capture the changes of the glucose and predict glucose of 30 min ahead, RMSE、SSGPE were 5.069,5.276; And hypoglycemia can be timely detected.

scholarly journals The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Yannian Wang ◽  
Fenfen Wei ◽  
Changqing Sun ◽  
Quanzhong Li
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Prediction Model ◽  
Glucose Concentration ◽  
Blood Glucose Concentration ◽  
Original Data ◽  
Postprandial Glucose ◽  
Postprandial Blood Glucose ◽  
Glucose Prediction

Diabetes may result in some complications and increase the risk of many serious health problems. The purpose of clinical treatment is to carefully manage the blood glucose concentration. If the blood glucose concentration is predicted, treatments can be taken in advance to reduce the harm to patients. For this purpose, an improved grey GM (1, 1) model is applied to predict blood glucose with a small amount of data, and especially in terms of improved smoothness it can get higher prediction accuracy. The original data of blood glucose of type 2 diabetes is acquired by CGMS. Then the prediction model is established. Finally, 50 cases of blood glucose from the Henan Province People’s Hospital are predicted in 5, 10, 15, 20, 25, and 30 minutes, respectively, in advance to verify the prediction model. The prediction result of blood glucose is evaluated by the EGA, MSE, and MAE. Particularly, the prediction results of postprandial blood glucose are presented and analyzed. The result shows that the improved grey GM (1, 1) model has excellent performance in postprandial blood glucose prediction.

Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems

2021 ◽  
pp. 193229682110591
Author(s):  
Xiaoyu Sun ◽  
Mudassir Rashid ◽  
Nicole Hobbs ◽  
Rachel Brandt ◽  
Mohammad Reza Askari ◽  
...  
Keyword(s):  
Physical Activity ◽  
Prediction Model ◽  
Predictive Control ◽  
Prior Information ◽  
Artificial Pancreas ◽  
Adaptive Model ◽  
Percent Time ◽  
Adaptive Model Predictive Control ◽  
Glucose Prediction ◽  
Level 2

Background: Adaptive model predictive control (MPC) algorithms that recursively update the glucose prediction model are shown to be promising in the development of fully automated multivariable artificial pancreas systems. However, the recursively updated glycemic prediction models do not explicitly consider prior knowledge in the identification of the model parameters. Prior information of the glycemic effects of meals and physical activity can improve model accuracy and yield better glycemic control algorithms. Methods: A glucose prediction model based on regularized partial least squares (rPLS) method where the prior information is encoded as the regularization term is developed to provide accurate predictions of the future glucose concentrations. An adaptive MPC is developed that incorporates dynamic trajectories for the glucose setpoint and insulin dosing constraints based on the estimated plasma insulin concentration (PIC). The proposed adaptive MPC algorithm is robust to disturbances caused by unannounced meals and physical activities even in cases with missing glucose measurements. The effectiveness of the proposed adaptive MPC based on rPLS is investigated with in silico subjects of the multivariable glucose-insulin-physiological variables simulator (mGIPsim). Results: The efficacy of the proposed adaptive MPC strategy in regulating the blood glucose concentration (BGC) of people with T1DM is assessed using the average percent time in range (TIR) for glucose, defined as 70 to 180 mg/dL inclusive, and the average percent time in hypoglycemia (<70 and >54 mg/dL) and level 2 hypoglycemia (≤54 mg/dL). The TIR for a cohort of 20 virtual subjects of mGIPsim is 81.9% ± 7.4% (with no hypoglycemia or severe hypoglycemia) for the proposed MPC compared with 73.9% ± 7.6% (0.2% ± 0.1% in hypoglycemia and 0.1% ± 0.1% in level 2 hypoglycemia) for an MPC based on a recursive autoregressive exogenous (ARX) model. Conclusions: The adaptive MPC algorithm that incorporates prior knowledge in the recursive updating of the glucose prediction model can contribute to the development of fully automated artificial pancreas systems that can mitigate meal and physical activity disturbances.

scholarly journals Prediction of global ionospheric VTEC maps using an adaptive autoregressive model

2018 ◽  
Vol 70 (1) ◽  
Author(s):  
Cheng Wang ◽  
Shaoming Xin ◽  
Xiaolu Liu ◽  
Chuang Shi ◽  
Lei Fan
Keyword(s):  
Autoregressive Model ◽  
Adaptive Autoregressive Model

scholarly journals Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6460
Author(s):  
Dae-Yeon Kim ◽  
Dong-Sik Choi ◽  
Jaeyun Kim ◽  
Sung Wan Chun ◽  
Hyo-Wook Gil ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Glucose Level ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

scholarly journals Improving Accuracy of the Kalman Filter Algorithm in Dynamic Conditions Using ANN-Based Learning Module

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 94 ◽  
Author(s):  
Israr Ullah ◽  
Muhammad Fayaz ◽  
DoHyeun Kim
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Historical Data ◽  
Mean Squared Error ◽  
External Factors ◽  
Prediction Algorithm ◽  
Dynamic Conditions ◽  
Learning Module ◽  
Prediction Algorithms ◽  
Kalman Filter Algorithm

Prediction algorithms enable computers to learn from historical data in order to make accurate decisions about an uncertain future to maximize expected benefit or avoid potential loss. Conventional prediction algorithms are usually based on a trained model, which is learned from historical data. However, the problem with such prediction algorithms is their inability to adapt to dynamic scenarios and changing conditions. This paper presents a novel learning to prediction model to improve the performance of prediction algorithms under dynamic conditions. In the proposed model, a learning module is attached to the prediction algorithm, which acts as a supervisor to monitor and improve the performance of the prediction algorithm continuously by analyzing its output and considering external factors that may have an influence on its performance. To evaluate the effectiveness of the proposed learning to prediction model, we have developed the artificial neural network (ANN)-based learning module to improve the prediction accuracy of the Kalman filter algorithm as a case study. For experimental analysis, we consider a scenario where the Kalman filter algorithm is used to predict actual temperature from noisy sensor readings. the Kalman filter algorithm uses fixed process error covariance R, which is not suitable for dynamic situations where the error in sensor readings varies due to some external factors. In this study, we assume variable error in temperature sensor readings due to the changing humidity level. We have developed a learning module based on ANN to estimate the amount of error in current readings and to update R in the Kalman filter accordingly. Through experiments, we observed that the Kalman filter with the learning module performed better (4.41%–11.19%) than the conventional Kalman filter algorithm in terms of the root mean squared error metric.

Research on Adaptive Kalman Filter for INS

2017 ◽  
Vol 872 ◽  
pp. 316-320
Author(s):  
Kai Xia Wei
Keyword(s):  
Kalman Filter ◽  
Response Speed ◽  
Measured Data ◽  
Specific Method ◽  
Alignment Error ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Noise Interference ◽  
Kalman Filter Algorithm ◽  
The Stability

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

scholarly journals Assessment of Handover Prediction Models in Estimation of Cycle Times for Manual Assembly Tasks in a Human–Robot Collaborative Environment

2020 ◽  
Vol 10 (2) ◽  
pp. 556
Author(s):  
Kuo-Hao Tang ◽  
Chia-Feng Ho ◽  
Jan Mehlich ◽  
Shih-Ting Chen
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Waiting Time ◽  
Cycle Time ◽  
Prediction Models ◽  
Waiting Times ◽  
Fixed Time ◽  
Time Model ◽  
Assembly Task ◽  
Subjective Preference

The accuracy and fluency of a handover task affects the work efficiency of human–robot collaboration. A precise and proactive estimation of handover time points by robots when handing over assembly parts to humans can minimize waiting times and maximize efficiency. This study investigated and compared the cycle time, waiting time, and operators’ subjective preference of a human–robot collaborative assembly task when three handover prediction models were applied: traditional method-time measurement (MTM), Kalman filter, and trigger sensor approaches. The scenarios of a general repetitive assembly task and repetitive assembly under a learning curve were investigated. The results revealed that both the Kalman filter prediction model and the trigger sensor method were superior to the MTM fixed-time model in both scenarios in terms of cycle time and subjective preference. The Kalman filter prediction model could adjust the handover timing according to the operator’s current speed and reduce the waiting time of the robot and operator, thereby improving the subjective preference of the operator. Moreover, the trigger sensor method’s inherent flexibility concerning random single interruptions on the operator’s side earned it the highest scores in the satisfaction assessment.

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