Managing large-scale gatherings, such as global festivals, sporting events, and religious congregations, presents substantial challenges in ensuring crowd safety and control. Innovative frameworks are essential to address these complexities effectively. The Integrated Intelligent Crowd Control and Management (IICCM) framework combines cutting-edge technologies, including Computer Vision (CV), Artificial Intelligence (AI), and the Internet of Things (IoT), to enhance participant safety and optimize crowd management. CV enables precise real time identification and tracking, AI analyzes crowd behavior to anticipate risks, and IoT gathers environmental data to improve crowd flow, alleviate congestion, and provide timely assistance. Additionally, the framework facilitates emergency evacuation planning by modeling crowd dynamics and identifying safe, efficient escape routes. Although suitable for diverse events, the Hajj pilgrimage—a uniquely large and dynamic annual gathering—provides a rigorous test case for the IICCM framework. Managing millions of participants from varied cultural and linguistic backgrounds highlights the system’s adaptability and robustness. By effectively addressing Hajj specific challenges, the IICCM framework demonstrates its scalability and applicability to other large-scale events. This research offers valuable insights for decision-makers seeking to implement advanced crowd management technologies.

The research investigates the physical and psychological stress experienced by pilgrims during Hajj and Umrah through the implementation of advanced technologies which include wearable sensors and remote sensing systems. The study achieves a "comprehensive understanding" through its multidimensional approach which combines physiological data with environmental information and subjective feedback to analyze stress dynamics beyond previous pilgrimage research. The framework demonstrates strong methodological quality through its combination of real-time biometrics with geospatial tracking and validated questionnaires but its small sample size restricts generalization. The framework provides a fundamental model for detecting essential stress thresholds but future research should focus on expanding the system and conducting extensive validation tests. The system tracks heart rate and energy expenditure and fatigue levels through Apple Watches and mobile applications and web-based analytics software which combine these biometric measurements with environmental data points that include geographical positions and activity types. The research delivers practical findings about high-stress levels which guide the development of specific interventions to boost pilgrimage experiences despite its restricted participant pool. This research leads the way for innovative solutions which align with Vision 2030 to enhance crowd management and healthcare services and overall well-being during Hajj and Umrah. The dataset will become available to the public after acceptance.

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 ($\pm 1.4$). 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.