Voiceprint-Based Access Control for Wireless Insulin Pump Systems

Hao, Bin; Hei, Xiali; Tu, Yazhou; Du, Xiaojiang; Wu, Jie

doi:10.1109/mass.2018.00046

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…MFCC uses MEL filters, which places more filters in lowfrequency band and less in high-frequency band whereas IMFCC uses inverted mel-scale filters, which places less filters in low-frequency band and more in highfrequency band. The original implementations of RFCC, SCFC, SCMC, and SCMC only use linear scale [27] and the RPS (only) uses MEL, we extend the implementations in [27], [39] • RMSCC. The signal energy can be computed by taking the root average of the square of the amplitude, i.e., RMS [40].…”

Section: ) Feature Extractionmentioning

confidence: 99%

Feature Fusion and Voiceprint-Based Access Control for Wireless Insulin Pump Systems

et al. 2019

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Without effective cryptographic mechanisms, the wireless channel between the USB/uploader and insulin pump frequently suffers from vulnerabilities. Either eavesdropping or therapy manipulation attacks would put the patients in a life-threatening situation. Towards tackling this problem, we propose an access control scheme by introducing feature fusion and voiceprint. Featured by the anti-replay speaker verification and voiceprint-based key agreement, it secures communications over the wireless channel. Through a cascaded fusion of speaker verification and anti-replay countermeasure, the anti-replay speaker verification guarantees that the pump can only be accessed after the verification. When defending against zero-effort and replay impostors with our scheme, the equal error rate can be reduced to 2.22%. Furthermore, to generate a common key for the wireless channel, in the voiceprint-based key agreement, we present a noninteractive energy-difference-based voiceprint extraction and adaptive Reed-Solomon coding based fuzzy extractor. Thus, it enhances the communication encryption which protects the pump from eavesdropping and therapy manipulation attacks. Also, with an appropriate constraint on voiceprints similarity, the key agreement lowers the risk of channel establishment from device locating outside the pump's close proximity. INDEX TERMS Wireless insulin pump, feature fusion, voiceprint, access control, acoustic channel.

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Section: ) Feature Extractionmentioning

confidence: 99%

Feature Fusion and Voiceprint-Based Access Control for Wireless Insulin Pump Systems

et al. 2019

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Securing the elderly in cyberspace with fingerprints

Zheng

Yang

Johnstone

et al. 2020

Assistive Technology for the Elderly

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

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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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Voiceprint-Based Access Control for Wireless Insulin Pump Systems

Cited by 3 publications

References 22 publications

Feature Fusion and Voiceprint-Based Access Control for Wireless Insulin Pump Systems

Feature Fusion and Voiceprint-Based Access Control for Wireless Insulin Pump Systems

Securing the elderly in cyberspace with fingerprints

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

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