ArticleFrontiers in sports and active living2026
Physical activity monitoring using wearable devices based on machine learning algorithms.
Article in Frontiers in sports and active living, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Wearable accelerometers provide an important source for continuous physical activity monitoring, but high-frequency signals often suffer from missing labels, temporal discontinuity, and inter-individual variability, limiting their direct use in behavioral health analysis. This study proposes an integrated framework for wrist-worn tri-axial accelerometer data. The dataset includes 100 participants under free-living conditions, each monitored for approximately 24-27 h at 100 Hz. The framework consists of three stages: (1) data quality processing with label continuity reconstruction, (2) metabolic equivalent (MET) estimation using an XGBoost regression model based on time- and frequency-domain features, and (3) behavioral interpretation using the predicted MET sequence to derive sleep-stage boundary segmentation and sedentary-event alerts. XGBoost achieved the best performance among the evaluated models (MAPE
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