<![CDATA[Heart Rate Variability (HRV) is a non-invasive biomarker that reflects the activity of the autonomic nervous system and is closely related to physical fitness and recovery. This study aims to analyze physiological signals based on frequency domain components to extract fitness-related features using signal processing techniques. However, previous studies have shown limitations in utilizing comprehensive HRV frequency analysis for fitness evaluation, thus motivating this study to focus on frequency-based physiological interpretation. Electrocardiogram (ECG) signals were obtained from the PhysioNet database and processed through filtering, R peak detection, and RR interval extraction. Frequency domain analysis was performed using Power Spectral Density (PSD) to obtain spectral features, including Low Frequency (LF) (0.04–0.15 Hz), High Frequency (HF) (0.15–0.40 Hz), and LF/HF ratio. The results showed that the LF component exhibited a dominant peak around 0.1 Hz with values ranging from 0.008–0.009 s²/Hz, while the HF component ranged from 0.002–0.003 s²/Hz and had a broader distribution. An LF/HF ratio greater than 2 indicated a predominance of sympathetic activity. These findings suggest that HRV energy distribution is concentrated in the low-frequency band, reflecting a stable physiological state that has not yet reached optimal recovery. This study demonstrates that frequency-based HRV analysis using signal processing provides meaningful physiological insights for fitness evaluation without relying on machine learning models]]>
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