Toward Real-Time Noise Suppression for Acceleration Sensors on Resource- Constrained Processors

The real-time, precise, and accurate data from the accelerometer sensor are essential for ensuring a reliable and robust structural health monitoring (SHM) system. However, the accelerometer data can be contaminated by various types of noise, interference, and artifacts, leading to potential data misinterpretation. To address the mentioned challenge, an efficient real-time noise suppression method for microelectromechanical systems (MEMSs)-based accelerometers is presented. The proposed approach is designed for resource-constrained environment, and it is a combination of the recurrent neural network (RNN) model with specialized preprocessing techniques. This combination not only enhances noise reduction capabilities compared with the traditional noise suppression approaches but also optimizes computational efficiency compared with the existing learning-based methods, making real-time processing on microcontrollers feasible. The presented model is trained using data obtained from real-world tests and also from simulations where different levels of noise are added to the desired signals. Moreover, an experimental setup composed of higher resolution accelerometer sensors and a shaking table is employed to ensure comprehensive noise scenarios. Notably, the model achieved the signal-to-noise ratio (SNR) improvements of up to 16.97 dB in the presence of Gaussian noise. The performance and efficiency of the trained model prove its potential for enhancing the accuracy and precision of the MEMS-based acceleration sensors, especially for SHM applications.