A Visible Light Channel Based Access Control Scheme for Wireless Insulin Pump Systems

Zhao, Jian; Kong, Kam; Hei, Xiali; Tu, Yazhou; Du, Xiaojiang

doi:10.1109/icc.2018.8422827

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…The prediction of the smart infusion pump is needed for an accurate flow rate of the dosage to be concentrated for the patient. (15) The essential parameters required to achieve the accurate drug concentration for single and multiple infusions are flow rate and delay. In ICU and critical conditions, patients are administered with more than one drug at the same time.…”

Section: Infusion Pump Control Methodologymentioning

confidence: 99%

Machine learning approach to predict delay in smart infusion pump

Alamelu

Sivanantham

2022

Salud, Ciencia y Tecnología

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Wireless smart infusion pumps are currently under development. It is critical to ensure that the patient receives the correct drug concentration. Practically, the performance of the pump has relied on the minimum startup delay. The minimization of the startup delay is prominent in open-type infusion pumps and rarely in closed types. The emphasis on reducing startup delay puts practitioners and caregivers at ease while ensuring patient safety. The startup delay of the infusion pump is based on the flow rate and the lag time. The prediction of the flow rate and lag time for an infusion pump is necessitated to ensure a safe drug dosage for the patient. Currently, machine learning methods and computational methods to predict the desired parameter are widely used in healthcare applications and medical device performance. The reduction of start-up delay can be achieved by predicting its associated parameters lag time and flow rate. The flow rate is dependent on the speed of the infusion pump, which has to be calculated based on the number of gears and revolutions. The speed of the pump has to be predicted for accurate flow delivery. Our present research attempts to predict the lag time of an infusion pump using different kernel functions of support vector regression (SVR). The performance of the SVR for each kernel function is compared with R2, RMSE, MAE, and prediction accuracy. The prediction accuracy of 99,7 % has been obtained in optimized SVM.

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Section: Infusion Pump Control Methodologymentioning

confidence: 99%

Machine learning approach to predict delay in smart infusion pump

Alamelu

Sivanantham

2022

Salud, Ciencia y Tecnología

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“…Ahmad et al [43] proposed to use a deep learning method to predict the dosage threshold and ask the patient to perform gestures if the insulin to be injected is higher than the threshold. A visible channel based access control scheme, proposed by Zhao et al [44], can authenticate the doctor's USB by transmitting the PIN/key to the IPS via visible light signals. 3) IPS forensics: Forensic research for the IPS and medical devices in general is to keep records related to medical devices for post incident investigation.…”

Section: B Ips Securitymentioning

confidence: 99%

Fingerprint Access Control for Wireless Insulin Pump Systems Using Cancelable Delaunay Triangulations

et al. 2019

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An insulin pump is a small wearable medical gadget which can mimic the way a healthy pancreas functions by providing continuous subcutaneous insulin infusion for the patient. However, in the current products, the access to the pump is not securely controlled, rendering the pump insecure to harmful or even lethal attacks, such as those that lead to the privacy breach of the patient and the delivery of abnormal dosage of insulin to the patient. In a conventional symmetric key-based security solution, how to distribute and manage the key is quite challenging, since the patient bearing an insulin pump may visit any hospital or clinic to receive treatment from any qualified doctor. In order to prevent malicious access to the pump, in this paper, we propose a Fingerprint-based Insulin Pump security (FIPsec) scheme which requires an entity to first pass the fingerprint authentication process before it is allowed to access the insulin pump. With this scheme, the request from an adversary to access the pump will be blocked thereby protecting the pump from the possibility of being subjected to serious attacks during the post-request period. In the scheme, a cancelable Delaunay triangle-based fingerprint matching algorithm is proposed for the insulin pump, which has capabilities to resist against nonlinear fingerprint image distortion and the influence of missing or spurious minutiae. In order to evaluate the performance of the proposed fingerprint matching algorithm, we perform comprehensive experiments on FVC2002 DB1 and DB2 fingerprint databases. The results show that the FIPsec scheme can achieve a reasonably low equal error rate and thus becomes a viable solution to thwarting unauthorized access to the insulin pump.INDEX TERMS Biomedical informatics, wearable sensors, implantable biomedical devices, insulin pump system, artificial pancreas, cyber security, authentication, access control.

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Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

Rabby

Hossen

et al. 2021

BMC Med Inform Decis Mak

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Background Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning model is used. Methods In this work, we propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model considering sensor fault. We use the Kalman smoothing technique for the correction of the inaccurate CGM readings due to sensor error. Results For the OhioT1DM (2018) dataset, containing eight weeks’ data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 min and 60 min of prediction horizon (PH), respectively. Conclusions To the best of our knowledge, this is the leading average prediction accuracy for the ohioT1DM dataset. Different physiological information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus insulin, and cumulative step counts in a fixed time interval, are crafted to represent meaningful features used as input to the model. The goal of our approach is to lower the difference between the predicted CGM values and the fingerstick blood glucose readings—the ground truth. Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management.

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A Visible Light Channel Based Access Control Scheme for Wireless Insulin Pump Systems

Cited by 3 publications

References 23 publications

Machine learning approach to predict delay in smart infusion pump

Machine learning approach to predict delay in smart infusion pump

Fingerprint Access Control for Wireless Insulin Pump Systems Using Cancelable Delaunay Triangulations

Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

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