Digital Signal Processing and Artificial Intelligence for Automatic Learning Hathairat Ketmaneechairat 5 3 2026 https://doi.org/10.6025/dspaial/2026/5/3/135-167 https://www.dline.info/dspai/fulltext/v5n3/dspaiv5n3_1.pdf Low cost Micro Electromechanical Systems (MEMS) sensors are widely deployed in embedded systems, the Internet of Things (IoT), and cyber physical environments; however, their raw output under nominally static conditions may exhibit substantial temporal, spectral, distributional, and statistical complexity that cannot be captured by a single analytical representation. This study proposes and applies an integrated multidomain signal processing and unsupervised anomaly detection framework for the comprehensive characterization of multivariate static MEMS accelerometer data. The framework combines time domain statistics, Fast Fourier Transform (FFT) and Welch power spectral density (PSD) analysis, short time Fourier transform spectrograms, wavelet domain energy decomposition, autocorrelation and rolling root mean square (RMS) analysis, Pearson inter channel correlation, signal to noise ratio (SNR) estimation, Shannon and spectral entropy measures, rolling statistical change point detection, and two complementary unsupervised anomaly detection methods Isolation Forest and PCA reconstruction based detection. The framework is applied to a four channel dataset acquired from a stationary MPU-6050 accelerometer, comprising three raw acceleration axes and a derived percentage error channel, containing approximately 37,459 observations. The results reveal pronounced heterogeneity among the sensor channels: Sensors 1, 2, and 4 exhibit strongly non Gaussian, heavy tailed amplitude distributions with extreme skewness and excess kurtosis, whereas Sensor 3 displays substantially greater amplitude variability, strong temporal persistence, and concentrated spectral organization. Frequency domain analysis identifies a distinctive approximately 21 sample periodic component in Sensor 2, while time frequency and wavelet analyses expose localized transient events not captured by global spectral summaries. Dual entropy analysis demonstrates that amplitude domain and frequency domain complexity are distinct and independently informative properties. Change point detection identifies two statistical regime transitions, and both Isolation Forest and PCA reconstruction consistently identify 24 anomalous windows (anomaly rate H” 1.028%) in the absence of predefined labels. The integrated findings demonstrate that no single analytical domain is sufficient to characterize static MEMS sensor behavior, and that a multidomain framework is necessary to distinguish persistent nominal structure, transient anomalous deviations, and sustained statistical regime changes. The proposed framework provides a structured, reproducible, and label-free analytical foundation for sensor monitoring and anomaly detection in resource constrained and IoT-enabled systems.