Wavelet-based feature analysis of vehicle vibration signals for comfort assessment
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Abstract
This study explores the use of wavelet-based analysis for evaluating vehicle ride comfort through vibration signal processing. Using a custom-developed Android application, three-axis accelerometer and GPS data were collected under real driving conditions to identify three categories of passenger experience: comfortable, vibrationally uncomfortable, and oscillatory uncomfortable. The signals were decomposed using the Daubechies wavelet (Db2) up to level 8, and feature extraction was conducted using detail coefficients. Significant differences between signal types were observed through time and frequency domain analyses, with enhanced discrimination achieved via wavelet decomposition and RMS-based quantification. The results highlight that the vibrationally uncomfortable signals exhibit the highest coefficient values, clearly differentiating them from the other two categories. The findings confirm that wavelet-based methods provide improved insights into vibration signal characteristics, enabling more precise classification of discomfort. This work establishes a foundation for real-time comfort monitoring and future applications in transportation safety and healthcare, particularly in the context of patient transport.
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References
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