Online Thermal Effect Modeling and Prediction of Implantable Devices

Abstract

The overheating caused by the operation of implantable device can cause damage to the surrounding tissue. In applications like neural prosthesis, 1 °C of temperature increase could lead to irreversible damage to the subject. Predicting the overheating effect is therefore critical to maintain safe operation. This work proposes a Bayesian recursive multi-step prediction method for implantable device to predict the overheating effect. The method proposed in this article achieves accurate prediction within a horizon with low complexity by model updating that iteratively minimizes a function of the ${j}$ -step-ahead prediction error. At each time instant, the new available input output data are stored in a First In First Out (FIFO) queue of fixed length, and the model parameters are updated by iteratively minimizing the ${j}$ -step-ahead prediction error of the new data. Moreover, the regularization methods are introduced to improve the prediction performance by taking the Bayesian interpretation of the parameters into consideration. Monte Carlo simulation studies indicate that the developed method is able to estimate the fundamental dynamics of the system when the prediction model is underparametered, and is robust to measurement noise. For time varying systems, the developed method can capture the system dynamics during the system variation. The proposed method is demonstrated via an in-vitro test vehicle, which shows that the temperature increase can be predicted with high accuracy and low complexity.