| Title: | A Methodological Framework for Analyzing and Differentiating Daily Physical Activity Across Groups Using Digital Biomarkers from the Frequency Domain |
| Journal: | Mathematics |
| Published: | 11 Nov 2025 |
| DOI: | https://doi.org/10.3390/math13223616 |
| URL: | https://www.mdpi.com/2227-7390/13/22/3616/pdf?version=1762855656 |
Publication 18966
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Abstract
Human daily physical activity (PA), monitored via wearable devices, provides valuable information for real-time health assessment and disease prevention. However, analyzing time-domain PA data is challenging due to large data volumes and high inter- and intra-individual heterogeneity. Traditional PA analyses often rely on demographics, while advanced methods utilize time-domain summary statistics (e.g., L5, M10) or functional principal component analysis (FPCA). This study presents a data-efficient approach utilizing the Discrete Fourier Transform (DFT) to convert time-domain data into a compact set of frequency-domain variables. Our research suggests that adding these DFT variables can significantly enhance model performance. We demonstrate that incorporating DFT-derived variables substantially improves model performance. Specifically, (1) a small subset of DFT variables effectively captures major PA levels with effective dimensionality reduction; (2) these variables retain known associations with factors like age, sex, and weekday/weekend status; (3) they enhance the performance of classifiers. Mathematical and empirical analyses further confirm the reliability and interpretability of DFT-based features in dimension reduction. Across three mental health studies, these DFT-derived variables successfully capture key PA characteristics while retaining known associations and strengthening model performance. Overall, the proposed DFT-based framework offers a robust and scalable tool for analyzing accelerometer data, with broad applicability in health and behavioral research.</p>
6 Authors
- Ya-Ting Liang
- Chuhsing Kate Hsiao
- Amrita Chattopadhyay
- Tzu-Pin Lu
- Po-Hsiu Kuo
- Charlotte Wang
1 Application
| Application ID | Title |
|---|---|
| 134902 | Integrated analyses of genomics and health data for understanding the complex relationships between mental illness, neuropsychiatric disorders, cardiovascular diseases and cancers |
Enabling scientific discoveries that improve human health