Machine Learning-Based Prediction Model of Pilgrims' Tiredness During Hajj Using Smartwatch Physiological and...

Pilgrim safety during Hajj is challenged by dense crowds, sustained physical exertion, and demanding environmental conditions that can increase tiredness and compromise well-being. Although wearable sensing enables objective monitoring, the reviewed literature provides limited evidence on how synthetic augmentation affects predictive performance and cross-participant generalization when wearable data are scarce. To address this gap, this study presents an integrated framework that combines correlation- and domain-informed feature screening with statistically constrained synthetic data generation and a complementary assessment of predictive performance and synthetic data fidelity. Using the Hajj 1445 (June 2024) Apple Watch dataset comprising 120 records from three participants, the study compares classical resampling, univariate normal generation, multivariate normal (MVN) sampling, and covariance-aware generation using Cholesky decomposition and Mahalanobis distance filtering under a consistent classifier evaluation protocol. The highest observed record-level test accuracy is 93.33%, achieved by a Decision Tree using MVN sampling with moderate clipping (±1.4). Leave-one-participant-out evaluation of the same configuration yields a mean accuracy of 68.65% (SD = 19.72%), indicating lower cross-participant generalization. The regularized MVN-Cholesky-Mahalanobis configuration attains a composite synthetic-data quality score of 79.27%. These results show that statistically constrained synthetic augmentation can support tiredness prediction when real data are limited, while the participant-grouped results highlight the need for validation using larger and more diverse Hajj cohorts.