NEWERTRACK: ML-Based Accurate Tracking of In-Mouth Nutrient Sensors Position Using Spectrum-Wide Information

In this article, we consider a family of in-mouth passive radio-frequency (RF) sensors for nutrients whose data is acquired externally using a miniaturized vector network analyzer (VNA). However, the data readings suffer from noise as the VNA’s position shifts during reading and also in-between readings. We propose an ML method to track the movement of these sensors using RF signals. Although classical studies in RF sensors have shown that the s-parameter of the sensor is related to the position of the sensor, they have always tried to find the relation between the resonance frequency and its loss to the position of the sensor. In contrast, our analysis revealed that based on theoretical perspectives, using just resonance frequency and its loss makes it impossible to find the correct position of the sensor with reasonable accuracy. To improve accuracy, we propose to use the loss information of RF sensors not only at one resonance frequency but across a wide spectrum. We introduced a pretrained neural network model consisting of a combination of DNN and convolutional neural network layers for extracting the sensor position. In order to accurately track the movement of the sensor with respect to the VNA, we propose a neural network-based RF sensor tracking (NEWERTRACK) model, which has utilized two unidirectional long short term memory layers. The results show that our proposed model achieves an average tracking error of 1.84 mm. Also, NEWERTRACK is capable of improving the position tracking accuracy by about $10\times $ compared to traditional s-parameter analysis methods and outperforms the best model in other RF methods by two times.