This comprehensive review examines the evolution and current state of smartwatch-based hybrid intelligent systems for real-time healthcare monitoring, with particular emphasis on human-in-the-loop and human-on-the-way-back paradigms. The review synthesizes research spanning military fitness assessment, rehabilitation monitoring, IoT-based health systems, and motion analysis technologies. Key developments include the integration of custom-built wearable sensor suits with advanced data fusion techniques, achieving classification accuracies exceeding 98% in fitness assessment applications. The analysis covers transient healthcare operating systems, motion-core assistive tools using pervasive embedded intelligence, and Master-Slave IoT architectures for active healthy lifestyle monitoring. Critical gaps identified include limited real-time visualization capabilities for field deployment, insufficient integration of expert knowledge in automated systems, and inadequate personalization frameworks. The review proposes a unified framework integrating temporal delta difference analysis, 3D decision fusion matrices, and expert-validated threshold systems. Future research directions emphasize expanding multi-modal sensor integration, developing privacy-preserving analytics for sensitive health data, and creating adaptive learning systems that evolve with user behavior patterns. This work provides a foundation for next-generation healthcare monitoring systems that balance automation with human expertise.