Data Analysis of Sensors Value Using Various Moving Average Methods

Sensors such as force and distance sensors are crucial for accurate measurements in many engineering applications. However, sensor data often contains noise that can compromise the reliability of the measurements. This study addresses the problem of noisy sensor data by applying several moving average methods to smooth the data. The methods investigated include simple, exponential, modified, square-root, and triangular moving averages, implemented using MATLAB. The effectiveness of these methods is evaluated using the Root Mean Square Error (RMSE) as a performance metric. The results demonstrate that all moving average methods significantly reduce noise, with the exponential moving average showing the lowest RMSE values, thereby indicating superior performance in improving data quality.