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Hybrid Online Multi-Sensor Error Detection and Functional Redundancy for Artificial Pancreas Control Systems

机译：人工胰腺控制系统的混合在线多传感器错误检测和功能冗余

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摘要

Sensor errors limit the performance of a supervision and control system. Sensor accuracy can be affected by many factors such as extreme working conditions, sensor deterioration and interferences from other devices. It may be difficult to distinguish sensor errors and real dynamic changes in a system. A hybrid online multi-sensor error detection and functional redundancy (HOMSED&FR) algorithm is developed to monitor the performance of multiple sensors and reconcile the erroneous sensor signals. The algorithm relies on two methods, outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model. The two methods have different way of using data, ORKF is comparing current signal samples with the signal trace indicated by previous samples and LW-PLS is comparing samples in the past window with the samples from a database and uses the samples with the most similarity to build a model to predict the current signal values. The performance of this system is illustrated with a clinical case involving artificial pancreas experiments, which include data from a continuous glucose monitoring (CGM) sensor, and energy expenditure (EE) and Galvanic Skin Response (GSR) information based on wearable sensors that collect data from people with type 1 diabetes. The results indicate that the proposed method can successfully detect most of the erroneous signals and substitute them with reasonably estimated values computed by the functional redundancy system.
机译：传感器错误限制了监控系统的性能。传感器的精度会受到许多因素的影响，例如极端的工作条件，传感器的劣化以及其他设备的干扰。可能难以区分传感器错误和系统中实际的动态变化。开发了一种混合的在线多传感器错误检测和功能冗余（HOMSED＆FR）算法，以监视多个传感器的性能并调和错误的传感器信号。该算法依赖于两种方法，离群值稳健的卡尔曼滤波器（ORKF）和局部加权的偏最小二乘（LW-PLS）回归模型。两种方法使用数据的方式不同，ORKF正在将当前信号样本与先前样本指示的信号迹线进行比较，而LW-PLS正在将过去窗口中的样本与数据库中的样本进行比较，并使用与建立一个模型来预测当前信号值。通过涉及人工胰腺实验的临床案例来说明该系统的性能，其中包括来自连续葡萄糖监测（CGM）传感器的数据，以及基于收集数据的可穿戴传感器的能量消耗（EE）和皮肤电反应（GSR）信息来自1型糖尿病患者。结果表明，该方法可以成功地检测出大多数错误信号，并用功能冗余系统计算出的合理估计值替代它们。

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