Empirical Noise Profiling and Dimensionality Reduction of Raw Triaxial Acceleration Data in Consumer-Grade MEMS Sensors
Hajar Ait Lamkademe
Journal of Electronic Systems, Vol. 16, No. 3, 2026.
DOI: https://doi.org/10.6025/jes/2026/16/3/146-162
Link: https://www.dline.info/jes/fulltext/v16n3/jesv16n3_2.pdf
Abstract:
Consumer grade Micro Electro Mechanical Systems (MEMS) accelerometers are ubiquitous in autonomous
navigation platforms but remain susceptible to inherent stochastic noise and deterministic biases. This
study presents a comprehensive empirical noise profiling and dimensionality reduction analysis of raw,
high-frequency triaxial acceleration data acquired from an MPU-6050 sensor under controlled, static
conditions. A continuous dataset comprising 37,459 samples was captured using a Raspberry Pi Pico
microcontroller. The analytical framework employed descriptive statistics, dual method anomaly detection
(Interquartile Range and Z-score), and Principal Component Analysis (PCA) to rigorously evaluate signal
integrity and multivariate covariance structures. Statistical profiling revealed a significant factory bias
offset along the X axis (approximately 1,432 LSB) and an elevated, independent noise floor along the Z-axis
(standard deviation of 493.2 LSB), in contrast to the stable, gravity aligned Y-axis. Furthermore, anomaly
detection highlighted transient data corruption events, likely stemming from IÃ‚Â²C communication glitches or
power fluctuations. Crucially, PCA demonstrated that 75.1% of the total dataset variance is captured by the
first two components: PC1 captures shared systemic noise across the X and Y axes and magnitude error,
while PC2 isolates the distinct, orthogonal dynamics of the Z-axis. These findings validate the efficacy of
PCA-based dimensionality reduction and targeted, axis specific filtering. By projecting high dimensional
sensor streams onto principal components, resource constrained embedded systems can achieve substantial
computational savings while preserving essential signal characteristics, ultimately facilitating the
development of resilient, self calibrating inertial navigation algorithms.