## 2 The Vision of IoPVT-Enabled Outdoor Health Monitoring
### Conceptual Framework  
Figure 2 conceptually presents the HARISM framework for outdoor safety monitoring enabled by the IoPVT. The HARISM framework centers on creating a dynamic interplay between the physical environment, its virtual representations, and advanced analytics tailored to human activities and environmental conditions. At its core, the HARISM framework connects three key dimensions--humans, technology, and the environment--through continuous data exchange and interactive feedback loops. The _Physical Layer_ is the foundation of the entire framework. It is a distributed network of heterogeneous sensors and devices embedded across various outdoor settings, including urban centers, rural landscapes, and coastal areas. These sensors capture a wide spectrum of data: from human motion and physiological signals (e.g., wearable biosensors, camera-based activity recognition, etc.) to environmental conditions (e.g., air quality, temperature, humidity, etc. ), infrastructure integrity (e.g., structural sensors on staircases or buildings), and climate indicators (e.g., tide levels, wind speeds, precipitation, etc.). Edge computing nodes process this incoming data locally, enabling immediate responses and reducing latency. The _Virtual Layer_ is where the Metaverse integration is built. The virtual counterparts of these physical objects, digital twins (DTs), reside in the Metaverse. In this immersive and interactive environment, sensor data are continuously synchronized with their virtual representation. The Metaverse visualizes real-time conditions and simulates potential scenarios, allowing stakeholders to experiment with interventions, preventive measures, or changes in infrastructure design [12]. As DTs evolve, they incorporate predictive models that project future risks, population flows, and environmental changes, offering a forward-looking perspective on safety and health [23]. The HARISM framework concentrates on a robust data fusion and analytics engine that synthesizes data streams from diverse sources. Advanced ML and AI-driven HAR algorithms interpret human activities, detect anomalies (such as sudden falls or suspicious behaviors), and classify complex patterns in real time [24]. Additional predictive analytics models integrate environmental and infrastructure data, identify emerging hazards (e.g., ice formation on stairs, degraded air quality near industrial zones, etc. ), and recommend proactive interventions. With the capacity to learn from historical trends and adapt to changing conditions, the analytics engine ensures that the system becomes increasingly accurate and context-aware over time [25]. A critical aspect of the conceptual HARISM framework is the integration of feedback loops. The alerts, insights, and recommendations generated by the analytics engine are sent to multiple stakeholders: emergency responders, city planners, public health authorities, environmental agencies, and community members [26; 27]. This bidirectional communication enables swift action, such as dispatching emergency services, issuing public safety advisories, updating evacuation routes, or adjusting resource allocation [28]. Concurrently, stakeholders can contribute their expertise and context-specific knowledge to the system, refining predictive models, optimizing sensor placement, and informing policy decisions. To fully realize the HARISM vision, scalability and interoperability are among the essential concerns [14; 29]. Employing standardized communication protocols, open data formats, and modular architectures ensures that new sensor types, analytics tools, and visualization techniques can be integrated seamlessly as they emerge. Equally important is the ethical dimension, where privacy, data governance, and equitable access must be considered [30; 31]. Mechanisms for anonymizing sensitive data, secure encryption, and transparent user consent processes help maintain public trust. At the same time, equitable  
Figure 2: HARISM framework for IoPVT-enabled outdoor safety monitoring.