Data Extraction and Transformation Methodology for Biometric Signals from Wearable Devices

Abstract

[Abstract] The increasing use of consumer wearable devices, such as smart bands, provides new opportunities for health monitoring and clinical research. However, access to high-resolution data is often limited by proprietary formats and aggregated summaries that are unsuitable for detailed analysis. This work presents a reproducible methodology for data extraction and transformation from Xiaomi devices, applied in a clinical study with 179 participants suspected of obstructive sleep apnea. A two-stage pipeline was developed to convert exported files into structured, minute-level datasets, accessible through a graphical interface designed for non-technical researchers. The approach was evaluated in terms of data quality, robustness, and utility, successfully generating key metrics such as heart rate, oxygen saturation, respiration, steps, stress, and sleep stages. Results show that the methodology facilitates standardized access to physiological signals, supporting visualization and analysis in clinical and interdisciplinary research contexts.