Inspired by the human somatosensory system, pressure applied to multiple pressure sensors is received in parallel and combined into a representative signal pattern, which is subsequently processed using machine learning. The pressure signals are combined using a wireless system, where each sensor is assigned a specific resonant frequency on the reflection coefficient (S11) spectrum, and the applied pressure changes the magnitude of the S11 pole with minimal frequency shift. This allows the differentiation and identification of the pressure applied to each sensor. The pressure sensor consists of polypyrrole‐coated microstructured poly(dimethylsiloxane) placed on top of electrodes, operating as a capacitive sensor. The high dielectric constant of polypyrrole enables relatively high pressure‐sensing performance. The coils are vertically stacked to enable the reader to receive the signals from all of the sensors simultaneously at a single location, analogous to the junction between neighboring primary neurons to a secondary neuron. Here, the stacking order is important to minimize the interference between the coils. Furthermore, convolutional neural network (CNN)‐based machine learning is utilized to predict the applied pressure of each sensor from unforeseen S11 spectra. With increasing training, the prediction accuracy improves (with mean squared error of 0.12), analogous to humans' cognitive learning ability.

In this paper, a fluidic glucose sensor that is based on a complementary split-ring resonator (CSRR) is proposed for the microwave frequency region. The detection of glucose with different concentrations from 0 mg/dL to 400 mg/dL in a non-invasive manner is possible by introducing a fluidic system. The glucose concentration can be continuously monitored by tracking the transmission coefficient S21 as a sensing parameter. The variation tendency in S21 by the glucose concentration is analyzed with equivalent circuit model. In addition, to eradicate the systematic error due to temperature variation, the sensor is tested in two temperature conditions: the constant temperature condition and the time-dependent varying temperature condition. For the varying temperature condition, the temperature correction function was derived between the temperature and the variation in S21 for DI water. By applying the fitting function to glucose solution, the subsidiary results due to temperature can be completely eliminated. As a result, the S21 varies by 0.03 dB as the glucose concentration increases from 0 mg/dL to 400 mg/dL.

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