2025 ASEE Annual Conference & Exposition

Numerical integration and differentiation are essential engineering concepts traditionally taught through theoretical methods. Accelerometers, which measure acceleration, offer a practical and interactive way to illustrate these concepts in an engineering context. By engaging in hands-on experiments and data analysis with accelerometers, students can gain a deeper understanding of these techniques, fostering active learning and engagement. This paper explores the innovative use of accelerometers in teaching numerical integration and differentiation. In analyzing accelerometer data students are introduced to critical concepts such as sensor drift, sensor noise, and data smoothing. Techniques like polynomial detrending, low-pass filtering, Power Spectral Density (PSD) thresholding, and Savitzky-Golay filtering are evaluated for their effectiveness in handling the drift and noise when calculating velocity, displacement, and higher-order derivatives like jerk from real-world acceleration data. The results indicate that the implemented techniques effectively mitigate sensor drift and noise, resulting in more accurate calculations of velocity, displacement, and higher order derivatives of acceleration. Consequently, accelerometers can be a valuable practical tool for teaching numerical differentiation and integration. They bridge the gap between theory and practice, enhancing theoretical knowledge and equipping students with practical skills for careers in engineering, particularly in robotics, aerospace, and biomechanics.