## 3 Human Activity Recognition Techniques
### AI-Driven HAR Algorithms
#### 3.2.3 IoPVT-Based HAR  
Emerging techniques in human activity recognition (HAR) are increasingly leveraging the Internet of Physical-Virtual Things (IoPVT) to enhance the accuracy and efficiency of activity detection [76; 77]. In IoPVT-based HAR systems, the data collected by physical sensors are transmitted to virtual platforms, where advanced algorithms and machine learning models process and analyze the information to recognize and classify different activities. In addition to the aforementioned deep learning models, IoPVT-based HAR systems rely on data synthesis techniques and transfer learning to combine data from physical sensors with virtual data sources, such as simulated sensor outputs or data extracted from videos, to create comprehensive datasets for training HAR models and apply the knowledge gained from virtual environments to real-world scenarios, enabling HAR systems to generalize better across different contexts and reduce the need for extensive real-world data collection. Studies have demonstrated that the fusion of wearable sensors with environmental sensors and virtual sensor data, e.g., from videos, simulations, or digital twins, enhances the robustness of HAR systems, particularly in real-world settings with diverse activities and environmental conditions [78; 79; 80]. Cross-domain learning will allow models to generalize better across different environments and individuals. Digital twins of human activities can be created using the IoPVT to simulate and predict behaviors in different environments, making them useful for applications in rehabilitation, ergonomics, and personalized training. For example, distinguishing \"walking upstairs\" from \"climbing a ladder\" might be tough with just a wrist sensor, but the IoPVT could cross-reference room layout data, pressure sensors on stairs, and a virtual simulation of the user's typical movements. While AI-driven HAR offers significant advancements in accuracy and adaptability, it is not without limitations. Data bias remains a critical concern, as models trained on nonrepresentative datasets may fail to accurately recognize activities across diverse populations, demographics, or cultural contexts, potentially leading to skewed outcomes or exclusion of underrepresented groups. Additionally, model generalizability poses a challenge, as algorithms optimized for specific environments, such as urban settings, may struggle to perform effectively in rural areas with differing activity patterns, sensor availability, or environmental conditions. Furthermore, computational constraints in real-time processing can hinder deployment, particularly on resource-limited edge devices, where high-dimensional data and complex deep learning models demand significant power and memory, potentially introducing latency or reducing responsiveness in time-sensitive safety applications. IoPVT-based HAR systems have the potential to address these limitations. Traditional AI models are often static, trained offline, and deployed without continuous updates. The IoPVT's interconnected nature allows for continuous learning across devices. Edge nodes, such as a smartwatch, could handle immediate processing, while cloud-based virtual components update models in real time based on new data from the network. This could reduce latency and make HAR more responsive to unexpected scenarios. Conventional HAR struggles when scaling beyond single devices or users. The IoPVT framework, rooted in the principles of the IoT, is designed for interoperability. Consider a hospital setting where patient wearables, bed sensors, and staff devices all feed into a unified HAR system. The virtual layer could aggregate and analyze these data, recognizing group activities, such as a team lifting a patient, that a single-device AI might miss. This scalability could make the IoPVT more robust for large-scale applications like smart cities or industrial monitoring.