Abstract
BACKGROUND: Uterine contraction is a meaningful indicator for labor onset and appropriate hospital admission. Inaccurate self-assessment may lead to premature admission, unnecessary interventions, and higher healthcare resource use. Traditional monitoring devices have limited portability and comfort, restricting home-based use. OBJECTIVE: This study developed and validated a wearable system integrating flexible sensors, a data acquisition platform, and machine learning models to monitor uterine contractions and identify labor onset, focusing on late pregnancy and the pre-labor period. METHODS: A flexible sensor-based device was developed and validated against hospital toco. Contraction data from 82 participants (104 recordings) were preprocessed and segmented, and features were extracted for model training. Hospital admission was classified into recommended admission (RA), deferred admission (DA), and selective admission (SA). Several ML models were trained and evaluated via 10-fold stratified cross-validation using accuracy, precision, recall, F (1)-score, and area under the curve. Shapley Additive Explanations (SHAP) analysis interpreted feature contributions. RESULTS: A total of 82 participants were enrolled, and 104 uterine contraction recordings were collected, ranging from 10 to 70 min (mean 20.3). Two hundred and seventy-seven processed segments were obtained for analysis. Contraction signals were generally consistent with toco measurements (r = 0.85-0.95). XGBoost achieved accuracy of 0.87 for RA classification, and SHAP identified kurtosis, signal energy area, and standard deviation as key features. CONCLUSION: The system enabled accurate monitoring of uterine contractions, improved estimation of hospital admission timing, reduced premature admission risk, and demonstrated high wearability, offering a feasible solution for home obstetric monitoring.