Abstract
Soft tactile sensors have been used to acquire biomedical signals for a variety of applications. Lack of proper calibration can lead to data discrepancies and, in some cases, misdiagnoses. This study introduces a channel-specific calibration technique using an enhanced stacked Simple Recurrent Unit (SRU) network for real-time calibration of each sensor channel. The network is designed to correct deviations due to sensor characteristics or operational pressures through a dynamic weighting mechanism that adjusts to each channel’s energy level. A pneumatic-controlled platform was developed to ensure quality training data for the deep learning model, facilitating a robust public dataset. For pulse wave signals, our approach significantly outperformed existing methods, reducing the Root Mean Square Error (RMSE) by 68.21% over raw signals and 25.68% compared to LSTM-based calibration. It also improved the accuracy of radial augmentation index (radial-AIx) measurements from −3.19% to −0.03%, making biomedical sensors more reliable.
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